Abnormal Clots and All-Cause Mortality During the Pandemic Experiment: Five Doses of COVID-19 Vaccine Are Evidently Lethal to Nearly All Medicare Participants

Abstract

Nyström and Hammarström (2022) found 7 segments in the bio-active SARS-CoV-2 spike protein that can produce abnormal proteinaceous (fibrinaloid) clots according to the Waltz algorithm. In vitro results confirmed the Waltz predictions. If the spike coding sequence was captured in the BNT162b2, Moderna, and other injectables, as claimed by the manufacturers, the clot producing segments are present in them too. Mainstream medical publications claim that SARS-CoV-2 infection can cause abnormal clotting, especially in “long COVID”. Telling evidence from Medicare data shows a decreasing life expectancy with each dose of COVID-19 “vaccine” — 1 dose is worse than 0, and 2 worse than 1, etc. In Connecticut, 26,091 Medicare participants who died before December 31, 2022, but never took a COVID injection, on the average, survived 428 days after the middle of the pandemic period (July 27, 2020). By then nearly all of them must have been exposed to and/or infected by some SARS-CoV-2 variant — hence, the CDC urging to take the “vaccines”. By contrast, 108,156 Medicare patients across the US who died before January 1, 2023, after just 1 dose of COVID-19 “vaccine”, survived only 308 days — a loss of 119.9 days on the average. Connecticut participants, 23,248 of them, who received 2 to 5 doses, on the average, lost an additional 62 days of life-expectancy with each booster. It follows that 5 boosters times 62 days reduces the average remaining 308 days left-to-live after dose 1 by 310 days. So, nearly all the Medicare participants will have been dead for 2 days by booster 4 (dose 5). The upshot is that 5 doses, on the average, will kill all the Medicare participants who accept the advice of the CDC.[1] For 157,495 of the 65 and older Medicare population studied here — people supposedly most apt to benefit from COVID-19 injectables — days-left-to-live shrinks by 74 days, on the average, with each dose. It is also likely that the COVID-19 injectables are partly, maybe wholly, responsible for the unnatural clots found by treating physicians, pathologists, and embalmers in living and dead recipients of the experimental injectables. It is certain is that the injectables are increasing all-cause mortality across the globe. [1] In the dataset from Connecticut, only 7 of 57,261 Medicare participants (7/57261 = 0.000122), or about 1.22 persons in 10,000 survived 5 doses during the experimental pandemic in order to take a 6th dose. Those who did so died, on the average, in 34 days. Only 1 participant survived 6 doses to receive a 7th and died within 69 days at the age of 68.

Full text

International Journal of Vaccine Theory, Practice, and Research 3(1) April 4, 2023 | Page 847
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Abnormal Clots and All-Cause Mortality During the
Pandemic Experiment: Five Doses of COVID-19 Vaccine
Are Evidently Lethal to Nearly All Medicare Participants
Daniel Santiago, PharmD†, and John W. Oller, Jr., PhD‡
† Pharmacist in Orlando, Florida at sanshou1428@protonmail.com (ORCID 0000-0001-5975-0592)
‡ Professor Emeritus, University of New Mexico, joller@unm.edu (ORCID 0000-0001-7666-651X)
Abstract
Nyström and Hammarström (2022) found 7 segments in the bio-active SARS-CoV-2 spike protein that can
produce abnormal proteinaceous (fibrinaloid) clots according to the Waltz algorithm. In vitro results confirmed
the Waltz predictions. If the spike coding sequence was captured in the BNT162b2, Moderna, and other
injectables, as claimed by the manufacturers, the clot producing segments are present in them too. Mainstream
medical publications claim that SARS-CoV-2 infection can cause abnormal clotting, especially in “long
COVID”. Telling evidence from Medicare data shows a decreasing life expectancy with each dose of COVID-
19 “vaccine” — 1 dose is worse than 0, and 2 worse than 1, etc. In Connecticut, 26,091 Medicare participants
who died before December 31, 2022, but never took a COVID injection, on the average, survived 428 days
after the middle of the pandemic period (July 27, 2020). By then nearly all of them must have been exposed to
and/or infected by some SARS-CoV-2 variant — hence, the CDC urging to take the “vaccines”. By contrast,
108,156 Medicare patients across the US who died before January 1, 2023, after just 1 dose of COVID-19
“vaccine”, survived only 308 days — a loss of 119.9 days on the average. Connecticut participants, 23,248 of
them, who received 2 to 5 doses, on the average, lost an additional 62 days of life-expectancy with each
booster. It follows that 5 boosters times 62 days reduces the average remaining 308 days left-to-live after dose 1
by 310 days. So, nearly all the Medicare participants will have been dead for 2 days by booster 4 (dose 5). The
upshot is that 5 doses, on the average, will kill all the Medicare participants who accept the advice of the CDC.
1
For 157,495 of the 65 and older Medicare population studied here — people supposedly most apt to benefit
from COVID-19 injectables — days-left-to-live shrinks by 74 days, on the average, with each dose. It is also
likely that the COVID-19 injectables are partly, maybe wholly, responsible for the unnatural clots found by
treating physicians, pathologists, and embalmers in living and dead recipients of the experimental injectables. It
is certain is that the injectables are increasing all-cause mortality across the globe.
Keywords: amyloid disease, amyloidogenicity, abnormal clots, BNT162b2 contents, genetic therapies with XNAs,
Medicare participants, Moderna, myocarditis, SARS-CoV-2 spike protein, synthetic nucleic acid, XNA
1
In the dataset from Connecticut, only 7 of 57,261 Medicare participants (7/57261 = 0.000122), or about 1.22 persons
in 10,000 survived 5 doses during the experimental pandemic in order to take a 6th dose. Those who did so died, on the
average, in 34 days. Only 1 participant survived 6 doses to receive a 7th and died within 69 days at the age of 68.

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Introduction
There have been reports of extraordinary abnormal clots being removed from both living (Dong &
Corson, 2022) and dead (Crowder & O'Looney, 2022; O’Looney et al., 2022; Trigoso, 2022)
individuals who received one or more doses of a COVID-19 injectable, or some mix of more than
one. By February 22, 2023 the in vivo experimental genetic therapy had already been administered in
13.29 billion doses — with 1,695,567 doses being added to the total in just one day in the still
unfolding true narrative representation (see TNR-theory in Oller, 2014). When we started writing
this paper, of the approximately 8 billion people on the planet, more than 5 billion (62.5% of the
whole world’s population) qualified as “fully vaccinated”. To that growing number, on February 22,
2023, 1,266,689 persons were added in just one 24-hour period. By December 31, 2022 during the
experimental pandemic period, in the US alone 665,886,823 doses had been administered to a
population of fewer than 332,612,403 people.
As a result, the ongoing program of worldwide COVID-19 “vaccination” is by far the largest
medical experiment in recorded history and it is still underway. Here, we first examine some of the
background of the two-phase experiment. The first phase consisted of the experimental
“pandemic” that, as we document, must be attributed to the accidental (or possibly intentional)
release of the weaponized SARS-CoV-2 virus into the human population. The SARS-CoV-2 virus
was artfully engineered in bioweapons laboratories mainly in the US and China to enhance its
infectious powers first in mice, then ferrets, later bats, and finally in human beings through a highly
bio-active “spike protein” (Yount, Denison, Weiss, & Baric, 2002; Menachery, Dinnon, Yount, . . . &
Baric, 2019). We say it is “bio-active” because of the known interactions between DNA, RNA, and
proteins in which the SARS-CoV-2 spike protein participates (Aldén et al. 2022; Kyriakopoulos et al.
2022; Lyons-Weiler, 2023). The second phase consisted of the ongoing effort to inject synthetic
genetic materials into every person on the planet. As we examine the scope and outcomes of this
two-phase experiment, we consider certain hypotheses about what factors precisely have resulted in
the observed increase in all-cause mortality across the world, though we focus mainly in this paper,
on the US population. We term the ongoing “pandemic” an “experiment” first and foremost
because of the fact that the SARS-COV-2 virus was an experimental product of bioweapons
research in various laboratories dating from prior to the development of SARS-CoV-1 (see Fleming,
2021; also Kennedy, Couey, & Rixey, 2023; Huff, 2022; Huff & Lyons, 2023).
As the facts of the true narrative concerning COVID-19 continue to materialize, the host of
unpredicted consequences of the “directed evolution”/“gain-of-function” research planned by the
defensive and offensive bioweapons experts (for documentation see Oller, 2021; Fleming, 2021;
Huff, 2022) are being revealed on a daily basis in real clinical outcomes. Among the suspected causes
of the emerging injuries and deaths summed up in the book, Cause Unknown: The Epidemic of Sudden
Deaths in 2021 and 2022 by Ed Dowd (2022), are the more than 13 billion injections of some
COVID-19 “vaccine” each dose of which contains more than 13 billion exemplars of a synthetic
nucleic acid posing as a natural “mRNA” coding for the SARS-CoV-2 spike protein (in the Pfizer
and Moderna products). In the AstraZeneca version, the payload is estimated to contain as many as
50 billion exemplars of a synthetic “DNA” aiming purportedly to cause recipients to manufacture

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billions more replicas of the SARS-CoV-2 spike protein.
2
This paper addresses the central clinical
outcome question:
What is now pushing upward the abnormally high level of deaths from all-causes in the most heavily
vaccinated populations of the world, especially in the USA?
3
Among the most likely suspected causes are the known components of the COVID-19 injections.
The foremost suspects are the strings of synthetic nucleic acids that have been under development
with the assistance of agencies within the US Department of Defense at a cost of hundreds of
millions of US taxpayer dollars (Karikó, Ni, et al., 2004; Karikó, Muramatsu, et al., 2008; Sahin,
Karikó, & Türeci, 2014; Pardi et al., 2015, 2018). Among the components suspected are the 728
m1Ψ nucleotides found in every complete xenonucleic acid (XNA) coding for SARS-COV-2 spike
protein (Oller & Santiago, 2022; Santiago, 2022a, 2022b). Additionally, we know that the
development of the COVID-19 injectables though supposedly rushed, was actually developed over
an unprecedented lengthy time scale with vast sums of taxpayer money. Also, in spite of
unsupervised manufacturing (Gutschi, 2022; Latypova, 2022b; Banoun, 2022, 2023), the whole
experiment, still ongoing, has been undergoing research and development for at least three decades
(Elzoghby, 2013; Dena & El-Sherbiny, 2022). That fact makes the ongoing experiment not only the
most extensive and costly in recorded history, but it makes it also one of the longest vaccine
programs in history. It is certainly not the shortest and fastest as has often been claimed in the
mainstream media (Cohen, 2020; Ball, 2020; and Schelenz & Long, 2021 to cite only a few of
thousands that come up on a Google search).
ABNORMAL CLOTTING AS A HALLMARK OF THE SARS-COV-2 SPIKE PROTEIN
According to the clinical observations, case studies, and written reports from front-line workers, the
abnormal clots that have been seen clinically usually occur in people who have received at least a
second dose of one of the experimental genetic injectables. Highly experienced embalmers
including John O’Looney, Richard Hirschman, and Brenton Faithfull (O’Looney et al., 2022) have
reported removing clots from about 50% to 70% of the corpses of recipients of one or more doses
of the COVID-19 “vaccines”. They report having seen normal blood clots previously in perhaps as
many as 5% to 10% of the hundreds of bodies they embalmed before the worldwide distribution of
COVID-19 “vaccines” began, but they report never having seen the kind being removed from
recipients of the experimental COVID-19 injectables. The strange clotting phenomena, they report,
only began to appear in corpses in 2021 and 2022 after the COVID-19 injectables began to be
widely distributed.
4
2
The numbers of exemplars of any particular synthetic nucleic acid cited here are from Fleming (2021, p. 99).
3
Data from Europe have recently shown trends similar to the one we document for the US. All-cause mortality has
increased in the UK (Santiago & Oller, 2022), and in Europe (Redert, 2023; Aarstadt & Kvitastein, 2023).
4
Notably, some of the key researchers involved in developing the supposedly novel “mRNA” therapies for treating
COVID-19, including Ralph Baric, Yount, and othes had commented as far back as 2002 (Yount, et al.) on the power of
“genetic modifications of the entire coronavirus genome, particularly in the replicase gene” to enhance “transfection
frequencies . . . 10- to 15-fold” (p. 11065). They commented that the mouse hepatitis virion “contains three or four virus
proteins including a spike glycoprotein of ~180/90 kDa” which could help the engineers to understand “the replication
strategy of coronaviruses”and then use that virion “as a model for pathogenesis, docking and entry, receptor usage,

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Until recently, testimonials supporting the observations of the embalmers, which were also later
confirmed by many medical practitioners who found abnormal clotting in living persons, could be
found on a Telegram channel formerly dedicated exclusively to the abnormal post-vaccination
“clotting” issues. However, that channel was taken down in 2022 only to be re-created under the
new moniker — “Efectos Adversos de la Vacuna” [Adverse Effects of the Vaccine]. The many new
testimonials now posted on Telegram in Spanish suggest a continuing and growing world-wide
scope for the abnormal, often fatal, clotting phenomena in many recipients of COVID-19
injections.
In the meantime, the abnormal clotting phenomena are, it seems, almost exclusively represented in
the mainstream medical/pharmaceutical publications as pertaining only to “long COVID” effects
(Pretorius et al., 2021; Raveendran et al., 2021; Patterson et al., 2022; Grobbelaar et al., 2022; Hofer,
2022; McCafferty et al., 2022; Montano et al., 2022; Nyström & Hammarström, 2022). The living
and dead persons with the abnormal clotting are regarded in the mainstream technical medical
literature, and in the popular press, as victims of “long COVID”. They have also been called “long
haulers” (Rubin, 2022). However, the clinical outcomes in recipients of the experimental genetic
therapies — for example, the BNT162b2 Pfizer product, as well as the other COVID-19 injectables
— are well documented outside the mainstream medical publications in multiple reports by
competent and independent observers (Crowder & O’Looney, 2022; Dong & Corson, 2022;
McLernon, 2022; Trigoso, 2022). Also, independent researchers, as we document in this paper, have
begun to link the abnormal clotting directly to the COVID-19 injectables, usually in recipients of
two or more doses.
5
NORMAL BLOOD CLOTTING IS DISTINCT
Normal blood clotting begins by converting the liquid blood to a gel. Then, with the addition of
fibrinogen under the direction of an incompletely understood complex of many factors, the gel is
transformed into a fibrinous blood clot (Weisel & Litvinov, 2017). The whole process is part of the
transcription, replication, polymerase function, and assembly and release” (p. 11065). Then, by 2019, those same
researchers together with Menachery et al. extended their coronavirus gain-of function engineering to “bat coronavirus
infection” observing that looking forward, their “results argue that both receptor binding and proteolytic cleavage of the
spike are critical factors that must be considered for evaluating the emergence potential and risk . . . for future
coronavirus emergence events in humans” (p. e01774-19).
5
While the construction of the current paper was underway, the second author was motivated to join with the first when
a fifth person among his own close relatives, those who willingly accepted one or more of the recommended doses of
the COVID-19 injections, abruptly fell desperately ill or died suddenly with abnormal clotting. One individual in her 40s
was diagnosed, soon after a “booster” injection with an acute Type 2 diabetes resulting in almost complete blindness and
damage to both kidneys. Two deaths among the five were specifically attributed to clotting by attending physicians. One
of the individuals who, in compliance with CDC recommendations took a COVID-19 “booster”, subsequently
experienced two distinct surgeries removing large clots from the same leg. The second clotting incident was fatal. On the
recommendations of the treating physicians, the clotting was being treated with multiple blood thinners, and during the
last brief hospitalization for the removal of a rapidly formed clot in the same leg from which a similar clot had been
removed some weeks earlier, the patient died. The proximate cause of death was attributed to “internal bleeding” and a
“ruptured spleen”. For reasons to be explored in this paper, it is doubtful that the clotting could have been helped by the
administration of the blood thinners, but the blood thinners may well have contributed to the uncontrollable internal
bleeding that began soon after they were administered. In both of the clotting cases, the treating physicians seem to have
misdiagnosed the underlying causes leading to death. Here, we examine tens of thousands of cases in multiple datasets.

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thoroughly integrated normal maintenance-repair-defense systems of the human body (Oller, 2022).
In cases of cuts, punctures, and other causes of bleeding, coagulation starts immediately after the
damage to the lining inside the lumen of the injured blood vessel(s). Thrombin, an important
component of this repair action, converts soluble fibrinogen into insoluble strands of fibrin.
However, the abnormal clots being removed during the embalming process from the multitude of
corpses of persons who had received more than one of the COVID-19 injections in most cases
before expiring (O’Looney et al., 2022; Trigoso, 2022), and also from more than a few living persons
— where the strange clots also are being found in surgeries, phlebotomies (the routine drawing of
blood samples), or placement of peripheral intravenous (IV) cannulation (Dong & Corson, 2022) —
the strange new clots appear to be radically larger and different in important ways from normal
blood clots.
The differences have been remarked upon especially by embalmers (Crowder & O’Looney, 2022),
but the abnormal characteristics of the fibrinous clots have also been noted by clinicians dealing
with living patients (Dong & Corson, 2022; Thorp et al. 2022). The strange new clotting structures
have a greater coherence and insolubility than ordinary blood clots. Stripped of any associated
blood, they have a whitish callous-like coloration and, in corpses and cardiovascular procedures
where they are observed, they are huge by comparison with normal blood clots.
Furthermore, they are apt to show up in arteries as well as in veins — a feature that is not seen with
normal blood clotting at all. The abnormal clots are distinguished as well by the rapidity of their
formation. Most importantly, their tangible toughness, and the consistency of their internal structure
make them notably different from any ordinary blood clots (Crowder & O’Looney, 2022).
The differences in elasticity (stretchiness) and coherence (toughness) have been universally noted by
embalmers and by clinicians who have extracted the abnormal clots from dead and living persons,
respectively. One of the unnatural aspects viewed commonly only by embalmers is the extent to
which the coherent abnormal clots occupy large portions of the branching vascular systems of veins
and arteries throughout a corpse. The common vacuum equipment used by embalmers to remove
the remains of bodily fluids from the cardiovascular system tends to breakdown because it clogs up
with relatively small amounts of the large coherent fibrinous composites.
In living persons, by the time the abnormal clots are detected by a surgeon, a phlebotomist, or a
nurse connecting an IV tube, the clots are thousands of times greater than the nanometer scale
where fibrinogen begins its normal work to prevent uncontrolled internal or external bleeding after a
severe bruise, puncture wound, or other injury. By the time the abnormal clotting associated with
recipients of the COVID-19 injectables can be seen by clinicians, not to mention embalmers, the
abnormal clots range from a millimeter up to a full centimeter or more in diameter, and in length
they range from centimeters to a meter or more (Crowder & O’Looney, 2022; O’Looney et al.,
2022).
As has already been suggested by Kell and Pretorius (2017) and by Nyström and Hammarström
(2022) the abnormal fibrinous clots associated with the SARS-CoV-2 spike protein and with the
synthetic “mRNA” spike proteins, the ones coded for in the XNA genetic therapies (the COVID-19

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injectables), begin as tiny fibrils at a nanoscopic scale as shown in Figure 1 which is adapted from
Nyström and Hammarström (2022). The fibrils seen in Figure 1 appear to be about 10 to 20
nanometers in diameter and up to 2000 nm (about 2 micrometers), or more, in length.
Table 1 lists all seven of the amyloidogenic segments in the SARS-CoV-2 spike protein receptor
binding region, but Figure 1 shows only fibrils generated in vitro meeting all three of the “amyloid
criteria” set by Nyström and Hammarström 2022. In particular, the fibrils had to stain yellow with
thioflavin showing sigmoidal kinetics, red showing congophilicity, and they had to have a “fibrillar
ultrastructure”. Spikes 192, 601, and 1166 (each segment being named for the position in the full
1273 residue sequence of the spike protein of its first amino acid) met all these criteria and spike
192 in particular showed “exceptionally well-ordered fibrils” which were similar to the in vitro
products when all 7 of the peptides in Table 1 were combined, as seen in the side-by-side
comparison in Figure 2.
6
The key finding is that the fibrils formed by the first of the seven amyloidogenic peptides in the
sequence of amino acids comprising the SARS-CoV-2 spike protein receptor binding region (Table
1) would apparently have the power to cause the formation of the microclotting reported in the
literature pertaining to the COVID-19 disease in both its acute and long-haul versions. We were able
to confirm that spike 192 and spike 258 in Table 1 are in BNT162b2 “mRNA” (actually an XNA)
coding for the spike protein in the Pfizer injectable according to the coding sequence published by
Nance and Meier (2021). Government proponennts also said the “modified sequence” was included
in the Moderna and the adenovirus DNA AstraZeneca injectables as well (Oller & Santiago, 2022).
In any case, the research of Nyström and Hammarström (2022) specifically implicates the SARS-
CoV-2 synthetic spike protein as a possible source of abnormal clotting. Given that the lumen of
6
It should also be noted here that spike 192 and the other amyloidogenic components in the injectables are supposedly
enhanced in their power to generate proteinaceous fibrils by the unnatural mRNA coding that employs N1-
methylpseudouridines in place of uridines (as stressed by the BNT162b2 and experimental genetic therapy proponents,
Nance & Meier, 2021). If that is the case, the XNAs should be more apt than the original SARS-CoV-2 spike protein
itself, to cause fibrinaloid clotting and all the disease conditions that it brings with it.
Table 1
The 7 amyloidogenic segments identified
in vitro
in the SARS-CoV-2 spike protein receptor binding region
by Nyström and Hammarström (2022) reported in “Amyloidogenesis of SARS-CoV-2 spike protein”,
Journal of the American Chemical Society
,
144
(20), 8946 retrieved on March 5, 2023 at
https://doi.org/10.1021/jacs.2c03925. Reprinted here as permitted by their Creative Commons 4.0 license.
The notes
a
and
b
are theirs.

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the smallest capillaries in the body is about 5 to 10 micrometers in diameter (Capillary, 2022), the
fibrinous materials seen in Figure 1 and in Figure 2 are already large enough, if matted together, to
impede the passage of red blood cells which are about 6 to 8 micrometers in their largest dimension
(Red Blood Cell, 2019). Because the red blood cells are typically a little larger than the lumen of the
capillary which they must squeeze through, the changing Zeta potential and electro-magnetic charge
inside the iron
laden hemoglobin
are believed critical
(Davidson &
Seneff, 2012) to
enable the normal
blood flow. Also,
healthy red blood
cells are round and
flexible helping to
enable the slide
through the
capillary lumen.
However, the very
small fibrinaloid
formations seen in
Figure 1 and 2, if
present in matted
clumps, would
cause precisely the
sort of
Figure 1. Side-by-side transmission electron microscopy (TEM) of fibrils obtained in vitro
from a mixture of all 7 of the amyloidogenic segments in the receptor binding region of
the synthetic SARS-CoV-2 spike protein (at left) with fibrils formed in vitro with the same
procedure by spike 192 alone (at right) identified by Nyström and Hammarström (2022)
and shown in their supplementary Figure 3S, items B and C, as discussed in
“Amyloidogenesis of SARS-CoV-2 spike protein”, Journal of the American Chemical Society,
144(20), on page 8946 retrieved on March 5, 2023 at
https://doi.org/10.1021/jacs.2c03925. Adapted and reprinted here as permitted by their
Creative Commons 4.0 license.
Figure 2. Fibrils formed in vitro by researchers Nyström and Hammarström (2022). The source of coding was the SARS-
CoV-2 spike protein. That coding, according to Nance and Meier (2021), except for the 728 N1-methylpseudouridine
insertions should be the same as found in the spike protein XNA of the BNT162b2 injectable. See Nyström, S., &
Hammarström, P. (2022), Amyloidogenesis of SARS-CoV-2 spike protein, in the Journal of the American Chemical Society,
144(20), 8945–8950. These micrographs are adapted from their figure 1 on page 8946, at
https://doi.org/10.1021/jacs.2c03925. They are adapted and reprinted here as permitted by their Creative Commons 4.0
license.

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cardiovascular disasters that are occurring increasingly according to McCullough (2023). It is mainly
at the capillary level where the exchange of oxygen and nutrients for carbon dioxide and waste
products takes place at the turn-around point from arteries carrying a load of oxygenated blood
from the lungs going to veins carrying de-oxygenated blood back to the lungs. Fibrinaloid log jams
at that level are certain to be problematic.
The logical question that we pose is whether the amyloidogenic segments, or the SARS-CoV-2 spike
protein XNA as a whole, might be causing the abnormal clotting observed in both living and dead
recipients of the BNT162b2 “vaccine” and its counterparts produced by the other known
manufacturers of COVID-19 genetic therapies (specifically, AstraZeneca, Janssen, Moderna,
Novavax, Pfizer, and Sanofi — all having been named in press conferences by President Donald
Trump in July and November of 2020a, 2020b). We also know now, based on multiple sources cited,
for instance, in detail by Eyeinthesky (2022), that the Department of Defense funding for those
companies, especially Moderna, supported research pertaining to synthetic “mRNA” and nanolipid
delivery systems since at least 2012 (under the guidance of Katina Karikó). All of it has been traced
back more or less directly to the US Department of Defense (Latypova, 2022c; Morris & Latypova,
2023)
ABNORMAL CLOTTING PRECIPITATED BY THE SARS-COV-2 SPIKE PROTEIN
Now in the aftermath of COVID-19 it has become known that abnormal clotting is precipitated by
the SARS-CoV-2 spike protein without any outside assistance from any XNA based COVID-19
“vaccine” (Ahmad et al., 2022; De Michele et al., 2022; Montano et al., 2022). This empirically
established fact, together with mounting evidence of the strange clotting phenomena in recipients
of the synthetic nucleic acid (XNA) component in the COVID-19 injectables, raises important
questions about the potential impact of the artificial spike proteins created by billions of the XNA
molecules in 13+ billions of doses in 5+ billions of recipients. We are referring specifically to the
genetically engineered synthetic “mRNA”, an XNA without question (Oller & Santiago, 2022;
Santiago, 2022a, 2022b), that aims to code for the SARS-CoV-2 spike protein in the BNT162b2
injectable, and in its Moderna counterpart as well. The AstraZeneca DNA seeks to cause the
production of the SARS-CoV-2 spike protein through a different genetic pathway (Nance & Meier,
2021).
With all that in mind, here are some of the inadequately addressed issues that arise:
• Perhaps the coagulating effects are being caused by spike proteins in vivo because of XNA
molecules whether from a deceptive “mRNA” or “DNA” starting point. The XNAs are, in
any case, designed to cause the production of multitudes (billions upon billions) of SARS-
CoV-2 spike proteins in COVID-19 “vaccine” recipients.
• The production of synthetic versions of the spike protein from SARS-CoV-2 (Nance &
Meier, 2021) may be partly, or perhaps solely, responsible for the causation of the abnormal
clotting being observed currently in the corpses as well as in the living bodies of recipients
of the COVID-19 “vaccines”.

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• If the SARS-CoV-2 spike protein is already known to cause clotting of an abnormal,
injurious, disease producing kind, it seems the XNA versions of that protein must also be
prone to causing such abnormal clotting.
• Supposing that the XNAs in the lipid-nanoparticles can and do actually invade nucleated
cells with whatever else the payload may contain, surely interactive effects must occur in vivo.
• In addition to whatever impact can be expected from the disclosed components of
BNT162b2 discussed by Segalla (2023) in this journal (also by Banoun, 2023), additional
impacts must be expected from whatever components though not previously acknowledged
by the manufacturers that are being brought to light by microscopists across the world
(Campra, 2021; Benzi Cipelli et al., 2022; Martín, 2022; Hughes, 2022; Latypova & Nixon;
Nixon, 2023).
As Oller and Shaw (2019) argued, when the membranes guarding the deepest levels of genetic
information in nucleated cells are compromised — as is the case according to recent studies showing
that some components of the COVID-19 injectables can reverse transcribe into the recipient’s DNA
(Aldén et al., 2022) — metastatic conditions traditionally regarded as cancers can be expected to
manifest or re-manifest suddenly, almost explosively. Similarly, rapidly developing prion-like
conditions similar to the new Creutzfeldt-Jakob disease discussed in this journal by Perez, Chalmin-
Moret, and Montagnier (2023) are consistent with the exploding plethora of disruptions that can be
anticipated. Here is where the absurdity of predicting in advance and trying to prepare for the
consequences of “directed evolution”/“gain-of-function” research leading to a pandemic such as
COVID-19, as pointed out by Huff in his interview with Tony Lyons on March 1, 2023 at the
Children’s Health Defense website, becomes obvious. The combinatory possibilities explode to an
irreducible complexity with even very small fragments of the synthetic nucleic acid strings. Whereas,
for instance, Nyström and Hammarström (2022) are focussing attention on seven peptides (Table 1)
that can be produced by strings as short as 5 nucleotides and as long as 21, the SARS-CoV-2 spike
protein at 1273 nucleotides, is only a little more than 4% of the entire SARS-CoV-2 virus estimated
at 29,870 nucleotides. Moreover, whatever protein/peptide sequences happen to be produced or
injected can potentially interact with about 35,000 distinct proteins found in the human body, not to
mention their potential interactions with the still greater complexities of the body’s maintenance-
repair-defense systems which are invoked in the transition from the laboratory context to living
persons.
Although researchers Kell and Pretorius (2017) acknowledge the coagulopathies that are associated
with the spike protein of SARS-CoV-2, they nonetheless seem to hold tightly to the severely
constrained mainstream medical publication requirements. Those publications either assert or imply
that more than 13 billion injections in more than 5 billion “fully vaccinated” recipients of the
COVID-19 of injections to produce SARS-CoV-2 spike proteins could not possibly have any causative role
in the widespread abnormal clotting being observed in the world population. Omitting many of the references
embedded in the quoted remarks just below, here are some of the findings of Kell, Laubsher, and
Pretorius (2022, pp. 540-541):
Coagulopathies . . . especially the formation of extensive microclots in vivo, are a hallmark of both COVID . . .
and long COVID . . . , and we have demonstrated that these microclots too are amyloid in character . . . .
Importantly, the addition of purified, recombinant SARS-CoV-2 S1 spike protein to coagulation-competent
normal plasma is sufficient to induce the formation of anomalous clots [Ryu et al. 2021] that adopt amyloid

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states that are also resistant to fibrinolysis [Grobbelar et al., 2021]. Note that the observations of the microclots
in (platelet-poor) plasma are performed without the addition of exogenous thrombin; they are naturally there in
the circulation of patients with both acute and long COVID. The size of these amyloid microclots, that can be
observed microscopically and stained . . . are typically anywhere from 1–200 µm [oon their longest axis]; this
means that they can effectively block up, and inhibit blood flow through, all kinds of microcapillaries, thereby
strongly lowering the availability of oxygen in tissues. As expected, they consist mainly of fibrin, but also
contain many other proteins, including α2-antiplasmin [Grobbelar et al., 2021] (and even the virus itself
[Marfella et al., 2021]). They also have heightened pro-inflammatory activity and elicit fibrin autoantibodies
[Ryu, et al., 2021] (and maybe others). Elements of at least some spike protein variants of SARS-CoV-2 can
also stimulate (in silico) the amyloidogenic aggregation of serum amyloid A [Jana et al., 2021].
They not only follow the mainstream narrative in medical publications claiming, overtly at least, that
the SARS-CoV-2 spike protein caused COVID-19 disease in all its forms (mild or fatal, short or
long), but they go further to suggest that the COVID-19 coagulopathies may involve some of the
same genetic precursors as the traditionally recognized amyloidogenic diseases such as Alzheimer’s
and the known prion diseases. They even suggest that the abnormal clotting associated with the
spike protein may be causally involved in Parkinson’s, Type 2 diabetes, rheumatoid arthritis, and
autoimmunity in general (Kell & Pretorius, 2017, p. 25, Figure 9).
Homology Seems Central
All those disease conditions seem to involve near homologies in communications through the body’s
complex biosignaling systems. In this connection, we are reminded of the “pathogenic priming”
theory of Lyons-Weiler (2021).
7
Even in ordinary experience, extreme similarities can cause mistaken
identifications. We may mistake one twin or even a sibling for a different one. Or, similar sounding
utterances may lead to false expectations in all sorts of real life contexts of experience. In
biosignaling systems, close homologies also can confuse the body’s maintenance-repair-defense
systems into mistaking self-nucleic acids, proteins, cells, tissues, or even whole organ systems, for the
disordered/diseased products of foreign agents that need to be attacked and destroyed, or at least
quarantined for further analysis and observation. The reverse also seems possible. Genuine disease
agents, such as the synthetic XNA posing as a native (self) mRNA, supposedly, according to relevant
mainstream medical publications, can be mistaken for self-material and may be used to produce
whole armies of invading SARS-CoV-2 spike proteins.
Such intentionally engineered mistakes — ones that end up conforming exactly to the formal
requirements of pre-meditated lies (see Oller, 2014) — via the COVID-19 “vaccines” are being
presented to the maintenance-repair-defense systems of the human body as if they were true
representations of native bodily proteins. Yet these deliberate lies are the sole basis for the hoped for
outcomes of the COVID-19 genetic therapies as spelled out in the mainstream narrative for Pfizer,
Moderna, AstraZeneca, and the rest of the COVID-19 “vaccines”. All of this messaging is
promoted, for example, by Nance and Meier (2021) speaking on behalf of the US Department of
Defense as we noted earlier (Oller & Santiago, 2022).
At any rate, the whole narrative behind the COVID-19 genetic therapies is predicated on the theory
that the body can be deceived by synthetic nucleic acids into regarding foreign disease agents, such
7
The importance of nearly homologous strings in complex biosignaling systems was also inadvertently noted by Yip et
al. (2016) specifically in relation specifically SARS-coronavirus infections. They wrote that “anti-spike antibodies
facilitate infection of SARS-CoVpp” (p.

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as the XNA coding for SARS-CoV-2 spike protein, as native components of its own biosignaling
systems. Kell and Pretorius (2017) explain the connection with the abnormal clotting associated with
the SARS-CoV-2 spike protein as follows. They write that
the finding [by Patterson et al., 2022] that S1 spike protein [the part from amino acid residues 14 through 685,
in the full 1,273 amino acid sequence, that contains the receptor binding domain fusing with angiotensin-
converting enzyme 2 (ACE2) thus enabling viral cell entry according to Huang et al. 2020] can itself persist in
CD16+ Monocytes in PASC [post acute sequelae of COVID-19] for up to 15 months post-infection is highly
relevant, as the amplification of trigger proteins to make microclots as part of the clotting mechanism
8
means
that miniscule (and highly substoichiometric) amounts
9
. . . can suffice [Kell & Pretorius, 2017; Pretorius et al.,
2016]. . . .
Given such a starting point in the SARS-CoV-2 spike protein coding sequence, Kell et al. claim that
exceedingly small amounts (“substoichiometric” amounts) of the homologous sequence for an
“epitope” of the fibrin producing peptide, at a ratio of 1 molecule to 100 million, may cause deadly
diseases:
The role of autoantibodies and biomimicry Autoantibodies [sic, their capital letter] are a feature of many
chronic, inflammatory diseases, such as rheumatoid arthritis . . . and where the cross-reactive epitopes leading
to “mimicry” by and of host protein targets are understood . . . . it has already been shown that both acute
[Leonardi & Proenca, 2020; Woodruff et al., 2020] and Long COVID [Phetsouphanh et al., 2022] are
accompanied by immunological dysfunction, and by novel antibodies [Proal & VanElzakker, 2021; Woodruff
et al., 2020], including in the latter case to (an “abnormal” but unspecified form of) fibrin [Ryu et al., 2021]. We
recognise that any change in the conformation of a protein can result in the generation of novel epitopes that
can thereby lead to the production of novel antibodies; indeed the use of secondary antibodies in the detection
of small molecules [Guntas et al., 2021] relies precisely on this fact. Although we consider that the more
primary event is the generation of fibrinaloid microclots, we also recognise that they are likely to be able to
change the natural conformation of many proteins that might normally present as harmless (seen as “self ”).
Chertow et al. (2021), about a year earlier, found in 37 of the 44 deceased patients on whom they
performed complete autopsies that they had “acute pneumonia or diffuse alveolar damage at the
time of death” (lines 232-333). Chertow et al. point out that the observed “diffuse alveolar damage
showed clear temporal associations, with the exudative phase seen mainly within the first three
weeks of infection and the fibrosing phase not seen until after a month of infection” (our Figure 3).
Based on its distribution throughout the bodies of those who died with SARS-CoV-2 infection, the
authors conclude: “Our data prove that SARS-CoV-2 causes systemic infection and can persist in the
body for months” (lines 80-82):
Overall, SARS-CoV-2 RNA was detected in respiratory tissue of 43/44 cases (97.7%); 160 cardiovascular tissue
of 35/44 cases (79.5%); lymphoid tissue of 38/44 cases (86.4%); 161 gastrointestinal tissue of 32/44 (72.7%);
renal and endocrine tissue of 28/44 cases (63.6%); 162 reproductive tissue in 17/40 cases (42.5%); muscle,
8
We must insert here the objection that normal blood clotting is not “mechanical”. It is not controlled by an
“mechanism” that might be construed as some kind of automatic switch. It is certainly more than that. Insofar as it is
understood at all, it is known to be under the direction of systems of communication that involve the delicate and highly
articulated maintenance-repair-defense systems guided by holistic interactions of DNA, RNA, and protein signaling
systems.
9
The stoichiometric principle of chemistry holds that normal reactions will include proportionate amounts of input and
output material (by mass). Usually the input and output ratio can be expressed a something close to a 1 to 1 distribution
from start to finish. However, in the case at hand, the clotting process, Kell and Pretorius maintain, can be precipitated
by a ratio of 1 triggering molecule to 100 million outcome molecules.

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skin, adipose, and peripheral nervous tissue 163 in 30/44 cases (68.2%); ocular tissue and humors of 22/28
cases (57.9%); and brain tissue in 164 10/11 cases (90.9%). . . .
The autopsy results of Chertow et al. demonstrate in actual cases how damage eventually marked by
abnormal clotting, “fibrosing” as they describe it, begins before causing the kinds of fatal disease
conditions widely attributed to the spike protein of the SARS-CoV-2 virus. In our view, the most
important point to be borne in mind with respect to the abnormal clotting phenomena associated
both with the original mRNA and with whatever synthetically produced SARS-CoV-2 spike proteins,
or fragments, may be caused by the experimental XNA — in the BNT162b2, for instance, or in its
Moderna, AstraZeneca, or other counterparts — is that there are exponentially many more ways for
the disruptive and deceptive experimental injections to cause harm than to bring about hoped for
benefits to recipients (Seneff & Nigh, 2021; Seneff, et al., 2022; Santiago, 2022a, 2022b; Perez, et al.,
2023; Segalla, 2023).
In fact, as we noted earlier, the exploding range of possibilities rapidly becomes irreducibly complex,
as implied by Huff in his 2023 interview with Lyons. The difficulty, as he explicitly said, makes
government sponsoring of “directed evolution”/“gain of function” research for the purpose of
predicting, preparing for, and heading off future pandemics before they happen, a special form of
self-deception masquerading as thoughtfulness, as if the officials funding the research really believe
it might work although they must know that it is a gamble guaranteed to lose every time it is
attempted. The exponentially exploding combinatory possibilities in biosignaling systems
Figure 3. This is “Extended Data Figure 5” from Chertow et al. (2021), “SARS-CoV-2 infection and persistence
throughout the human body and brain [Preprint]” https://doi.org/10.21203/rs.3.rs-1139035/v1 showing the
“temporal association of diffuse alveolar damage in patients dying from COVID-19”. The figure shows
relatively early appearance of the “initial exudative phase of diffuse alveolar damage, while patients dying after
prolonged illness are more likely to show organizing or fibrosing stages” (lines 839-843). Adapted and
reprinted here as permitted by their Creative Commons 4.0 license.

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progressing upward from nucleotides to amino acids to proteins, organelles, cells, tissues, and organ
systems shows all the claims by the Department of Defense about predicting pandemics in advance
in order to get prepared to head them off with ready-made “vaccines” to be ludicrous. The
proposition of the Department of Defense requires the acceptance of mathematical operations that
amount to something like dividing a measured quantity by zero, or multiplying a real mass by infinity.
What Could Go Wrong?
In this paper we explore some of the ways for things to go wrong. Logically, commandeering the
body’s ribosomal protein producing systems in order to trick them into manufacturing billions of
SARS-CoV-2 spike proteins seems exceedingly unlikely to enhance health and well-being while the
probability that it will cause disorder, disease, and death is a virtual algebraic certainty. If the billions
of synthetic “mRNAs” injected in every dose of BNT162b2, for example, are strategically cloaked
so they will not be spotted out as “foreign, non-self ” pathogens, can such deceptions lead to good
outcomes?
LIES DO NOT FIT THE CONTEXT
For reasons explained in detail elsewhere with mathematical proofs that have stood the test of time
in their minutest details (Peirce, 1897; Tarski, 1941, 1956; Sowa, 2011, 2017; Oller, 2014), in any
meaningful sign system whatsoever, the formal structure of a deception is such that it artfully
pretends to be a truthful, valid, and faithful representation of whatever facts it purports to represent
(Oller, 2014). However, what qualifies all such representations as lies, is the strictly formal fact that
they do not validly fit within all the formal constraints of whatever context into which they may be
deliberately injected. Their sole reason for being is the specific intention of deceiving one or all
interpreters concerning the material facts they purport to represent but do not quite report in all the
requisite material details. It is important to note, however, that if the details of any false
representation could be corrected so that it might come to fit all the formal requirements of the
larger context in which it is contained in space and time, it would no longer be a lie. It would be
transformed into a true narrative representation (TNR). Thus, the distinctiveness of lies in general is
a strictly formal, logicomathematical fact. Opinions have no power to change or impact such formal
relations in the slightest degree. Voting simply does not count when the questions are about ordinary
TNRs as contrasted with fictions, errors, lies, nonsense, or completely erased representations.
EVERY XNA CODING FOR SARS-COV-2 SPIKE PROTEIN IS A PACK OF DECEPTIONS
In the instance of the synthetic XNA coding for the SARS-CoV-2 spike protein, every instance
introduced in, say, the Pfizer BNT162b2 injectable, is supposed to consist of an exact replica of a
string of pseudo-codons pretending to be ordinary self-produced mRNA authored by the body’s
native nuclear DNA.
10
In that respect each one of them is a lie to begin with. The SARS-CoV-2
10
The exactness and completeness of the XNA string in every single instance, however, depends on the quality of the
process by which the Pfizer (Moderna, AstraZeneca, or other genetically engineered product) has been produced. Here
we concentrate on the strings supposedly incorporated into the XNA (the fake “mRNA”) to produce a particular exact
sequence of amino acids in the SARS-CoV-2 spike protein (also a fake, because the ones made by the XNA in the
“vaccines” are only a small part of the full SARS-CoV-2 spike protein and do not represent the entire SARS-CoV-2 virus
which they are designed to make the body think they actually are). The difference, as C. S. Peirce elegantly explained, is

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virus itself, pretending to belong inside the cells, tissues, and organ systems of any human body is
already a lie before it is transformed into any genetically modified XNA. However, when it is
transformed into its XNA shape, coding for the 1273 amino acids of just the SARS-CoV-2 spike
protein
11
, it is modified to contain many additional lies embedded within itself. By simple
combinatory logic we can show why those lies, billions of them in each dose of every genetically
engineered injectable (Pfizer 13.1 billion, Moderna 13.1 billion, AstraZeneca 50 billion; according to
Fleming, 2021, p. 99), must tend to increase exponentially over time into a multitude of multitudes
of lies that rapidly reach a level of irreducible complexity (generalizing from proofs by Gregory
Chaitin, 2004, 2007). Unless all of those lies can be expunged, corrected, or at least quarantined and
ignored by the body’s biosignaling systems, they must lead toward disorders, diseases, and the
eventual catastrophic breakdown of the body’s biosignaling systems which is death.
BAD THINGS HAPPEN WHEN LIES ARE MISTAKEN FOR TNRS
The first layer of deception in the COVID-19 XNAs consists in the fact that the SARS-CoV-2 virus
itself does not belong inside the human body. It is an invasive viral disease agent. Therefore, to get
the body to regard any single instance of such an XNA as a native mRNA to deliver to its own
ribosomal systems is a step toward disorder and disease. This level of deception, incidentally, seems
to be the one that best qualifies the COVID-19 injectables as “vaccines” because all traditional
vaccines prior to the COVID era, without any exceptions that we know of, aim to present some
disease agent, or part of one, to the body in such a way as to make the maintenance-repair-defense
systems think the body is being invaded by whatever the targeted agent(s) of the vaccine may be.
12
In that way, because of the homology between the targeted agent(s), and the active components in
the vaccine whatever they may be, the body’s communication systems are supposed to be trained up
to deal more efficiently in the future with any real invasion by one or many targeted disease agents.
The whole exercise is something like military training for genuine warfare where the training itself
(vaccination) aims to be rigorous, may involve some risk of injury or even death, but is, hopefully,
that between being something and merely representing something (in this instance, every instance of the XNA, perfectly
well-formed by the manufacturers or not, is always pretending to be something that it is not. Moreover, it also pretends
to represent something else, a normal bodily protein, when in fact, if it works as its designers intend for it to work, it
represents a part of the supposed COVID-19 disease causing SARS-CoV-2 spike protein. Deceptions in the billions are
involved from the beginning and can only be multiplied as the intended process moves forward across time. The
question that arises is not whether corruption will occur but how severe that corruption can be expected to become.
11
The full genome of the SARS-CoV-2 virus is known to consist of about 29,870 base pairs; according to a strain close
to the alpha Wuhan variant isolated in northern Germany by Pfefferle et al. (2020).
12
This stratagem defines what Jenner thought he had discovered with cowpox rendering people immune to smallpox.
Again, it was a case of homology leading to mistaken identification. If indeed the smallpox virus is defeated by
antibodies built to defeat cowpox, the deception may have had benefits. On the other hand, during the period when the
CDC claims smallpox was eradicated, many other relevant changes in hygiene, waste disposal, refrigeration of food
stuffs, and so forth were also accounting for huge reductions in disease across the board, smallpox being just one among
many diseases that were waning. Moreover, contrary to the popular mythology of vaccination, relevant research
specifically contrasting “smallpox” vaccinated populations against unvaccinated populations showed that the smallpox
vaccine of Jenner was the source of the only pandemic outbreaks of smallpox during the time frame after his supposed
prophylactic remedy was widely deployed.

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much more apt to keep trainees alive during genuine warfare than if they had skipped the training
exercises (i.e., if they had not been vaccinated).
COVID-19 “VACCINES” COMMANDEER CRITICAL COMMUNICATION SYSTEMS
The XNA strings of pseudo-codons in the COVID-19 injectables involve additional layers of
deception with hoped-for hypothetical outcomes that cannot be guaranteed in advance by any
stretch of the imagination. Among the deceptions that have been acknowledged in the lipid
nanoparticle payload of the Pfizer BNT162b2 product, for instance, are the 728 N1-
methylpseudouridine substitutions for the native uridines of the SARS-CoV-2 spike protein in every
well-formed XNA contained in the injectable. Each of them is also a pretender, like the larger
sequence in which it is contained. Each XNA is a lie containing a whole pack of additional lies. They
are, in fact, lies multiplied by the number of XNAs pretending to be self-produced mRNAs coming
from the body’s own native DNA, and each well-formed XNA being multiplied by its own 728
embedded N1-methylpseudouridine substitutions for the native uridines that are no longer present
in the pretending “mRNA” of the SARS-CoV-2 spike protein.
13
Even if each of those XNAs were
manufactured to perfection (though we know they are not
14
), the lies they represent from the start,
and the ones they contain within them, all of them designed to deceive the body’s native
maintenance-repair-defense systems, are incommensurably more likely to cause harm downstream
than they are to bring about health and well-being (Seneff & Nigh, 2021; Seneff et al., 2022).
Methods and Results
In this section our purpose is to present some analyses of clinical data emerging from the ongoing
worldwide COVID-19 experiment. Although we did not design the experiment, we are in as good a
position as any researchers to examine and analyze the emerging clinical findings. In our analysis, we
choose to focus attention on the most reliable data accessible in the US concerning one of the larger
and one of the more vaccinated populations in the world. We are not suggesting that the findings
here will generalize perfectly to the whole world, but the size of the US population and the numbers
of deaths occurring over the time frame to be examined closely must nonetheless be indicative of
what is happening in the larger worldwide experiment, especially in the developed/industrialized
nations most heavily impacted by the injectables. Certainly, there is no reason to expect that the
13
Each XNA, according to Nance and Meier (2021) who credit the research of Sahin, Karikó, & Treci (2014), deploys
its 728 contained lies to cloak itself from the body’s maintenance-repair-defense systems while the whole string purports
to be ordinary self-produced mRNA on its way to a ribosomal system to produce the pretend / fake self-protein which it
encodes. However, Karikó’s research must have begun sometime prior to her 2004 publication about pseudouridine
(Karikó, Ni, et al., 2004).
14
Documents recently obtained through Freedom of Information Act filings and other reliable sources show that none
of the COVID-19 injectables are manufactured anywhere close to perfection (Gutschi, 2022; Latypova, 2022b; Segalla,
2023; Banoun, 2023). The shoddy manufacturing was apparently only one of the consequences of unregulated creation
of the BNT162b2 product and its counterparts produced by other pharmaceutical companies.

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COVID-19 “vaccines” should be less harmful, safer, more efficacious, etc., in other parts of the
world than they are in the country that allegedly spends more than any other on “health” care
(Masters, 2009; Belk & Belk, 2020). Also, though we would like to be able to do a more complete
analysis looking at roughly the same time segments leading up to and including both phases of the
ongoing experiment for the whole world, in seeking to put together comparable data for all the
world population we found that the quality and coverage is sadly deficient. The World Health
Organization claims to have worldwide data but we found it to be too sparse for the purposes we
had in mind. Therefore, we will concentrate on data from checked sources for the US to establish a
baseline and then to evaluate all-cause mortality during both phases of the ongoing pandemic
experiment.
The timeline of interest throughout the remainder of this paper is the one shown in Figure 4. Our
focus is on the three distinct time segments marked off between 2017-1-1 (year-month-day) and
2022-12-31. Segment (A), t1, serves to find a baseline (average weekly rate) of all-cause mortality
against with compare segments (B), t2, and (C), t3. (B), t2, is the time from the onset of the pandemic
up to the introduction of the COVID-19 injectables on December 14, 2020. Time segment (C), t3,
begins when the COVID-19 injectables are introduced to quench the pandemic. Given that our
criterion for comparing the time segments in question is all-cause mortality as reported to the US
Centers for Disease Control, we have not included the first three months of 2023 in order to be
relatively certain that the deaths reported during the compared time segments are fully up-to-date.
15
While the primary symptoms of interest are the abnormal clotting observed in both living and dead
persons, we apply the standard of all-cause mortality to test the hypotheses to be detailed below. All
those hypotheses are tested specifically against all-cause mortality, adjusted to the standard of deaths
15
The CDC warns users of the Wonder site that provisional data “are lagged by an average of 1–2 weeks”. Therefore, to
make sure the comparisons of all-cause mortality in each of the three time segments are comparable, all of the weeks of
2023 up to the time of this writing are excluded from the analyses reported below.
Figure 4. Timeline showing segments to be contrasted in the Results section of this paper. (A) Segment t1 begins
January 1, 2017 and ends 167 weeks later with the announcement of the onset of the worldwide pandemic by
Tedros A. Ghebreyesus, Director-General of the World Health Organization on March 11, 2020. The reason for
including that segment is to provide a baseline of all-cause mortality against which to compare the two phases of the
worldwide experimental pandemic in phases one and two as represented in time segments t2 and t3.

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per one hundred thousand of the persons who were alive during each respective time segment. For
population estimates, we have relied on those supplied by the US Census Bureau.
16
PHASE ONE OF THE WORLDWIDE PANDEMIC-EXPERIMENT
The first phase, was the “gain-of-function”/“directed evolution” (artificially speeded up) production
of the SARS-CoV-2 virus itself. It is, in fact, unlikely that any such virus would have ever emerged
by itself if left to natural causes — i.e., without human intervention and technology. We set aside the
question of whether the virus was either deliberately or accidentally released into the world
population from an epicenter in Wuhan, Hubei Province of China leading to the COVID-19
“pandemic”. Regardless, according to Huff and Lyons (2023), the announced pandemic
17
which
marked the beginning of the pandemic experiment, may have begun some days or possibly weeks
prior to the opening of the pre-Olympic 2019 CISM [Conseil Internationale du Sports Militaire]
Military World Games on October 11, 2019. If the reports from athletes are to be trusted, it seems
the city of Wuhan was already in some kind of “lockdown” mode with Chinese authorities wearing
masks and hazmat suits keeping the athletes quarantined to the “Olympic type” village prepared for
them before the games were officially opened. Judging from the fact that some, perhaps many, of
the 9,308 athletes who competed (Wikipedia, 2023) returned home with the COVID-19 disease, it
seems likely that the pandemic began in Wuhan early in September 2019 but not later than the first
week in October 2019. Otherwise, given the approximate 6-day incubation period judging from the
Chinese data (Wassie et al., 2020), it seems the virus would have had insufficient time to spread to
the athletes and infect some of them prior to the conclusion of the games on October 27, 2019.
Given the foregoing facts, it may be supposed that the first stage of the worldwide experimental
pandemic began near the middle or end of September, 2019. Huff supposes, therefore, that the
entry of the SARS-CoV-2 virus into the human population was known to insiders prior to the
convening of “Event 201” at Johns Hopkins University on October 18, 2019. He notes that the
convocation itself featured various representatives of key entities, agencies, and individual
stakeholders to the “gain-of-function” research that led to the accidental escape, or the intentional
release, whichever it may have been. What is well-known by now and not a subject of any reasonable
debate is that SARS-CoV-2 was a laboratory produced, weaponized virus (see Fleming, 2021; also
Huff, 2022; Huff & Lyons, 2023). Therefore, it is reasonable to think of the propagation in human
subjects as a bioweapons experiment, whether intentional or accidental. Whereas we do not claim
any credit or responsibility for the design of the experimental SARS-CoV-2 weaponized virus, we
have no reason whatever to doubt the documentary evidence showing that the “virus” produced was
a product of “gain-of-function”/“directed evolution” research. It was developed over more than
16
Other sources were consulted, such as Our World in Data in addition to the CDC Wonder system, but the Wonder
system provided consistent population estimates only through 2016, whereas the US Census Bureau estimates are based
on the same system throughout the timeline of interest.
17
On January 9,2023, an interview between Clayton Morris and Alexandra (Sasha) Latypova revealed on the basis of
documents obtained by following the requirements of the Freedom of Information Act that the entire “pandemic” (as
suggested also by Huff, 2022; Huff & Lyons, 2023) was a form of theater where the actors on stage, e.g., the would-be
poster-child, a little 13 year-old girl who was so severely injured in the “clinical trials” that the segment that was
supposed to be used at the Super Bowl LVII on February 12, 2023 was pulled. Critical documentary evidence from
validated sources are discussed here.

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two decades with the involvement of multiple teams of researchers in the US and China (Fleming,
2021; Huff, 2022). The main basis for the SARS-CoV-2 infective agent, we can say without fear of
being contradicted, was a deliberately constructed experimental product developed in various
bioweapons laboratories.
PHASE TWO OF THE WORLDWIDE PANDEMIC EXPERIMENT
The second phase of the pandemic experiment, involved the advanced preparation, beginning about
three decades before the COVID-19 “pandemic” by ostensibly trying to produce various remedial
COVID-19 “genetic therapies” for the possible future pandemic. These would be presented to the
public as “warp-speed produced vaccines” but we now know they had been under development and
were already at an advanced design stage before the “pandemic” was announced to the public
worldwide. Much of this has come from Huff and Lyons (2023) and from other sources that Huff
has validated with his own firsthand insider information backed up by ample documentation from
sources we have already cited.
The official start of the second phase of the worldwide experiment, at any rate its public part, began
with the distribution of genetic therapies, marketed as a whole new generation of “vaccines”
promising to prevent severe COVID-19 disease and to reduce deaths in recipients (as documented
extensively by Kennedy, 2021). That development, we now know, thanks to discoveries from the
Freedom of Information Act, and from investigations of patents and contracts underlying them,
showing the government funding of “gain-of-function” research over many years (see Latypova,
2022c; Morris & Latypova, 2023) was owed to the US Department of Defense (see Huff, 2022). It
was grounded in sponsored research by Karikó and colleagues dating back more than a decade
earlier than 2013 (Karikó, Ni, et al., 2004; Karikó, Muramatsu, et al., 2008).
The documentation (Fleming, 2021; Huff, 2022; Huff & Lyons, 2023) shows, that the COVID-19
genetic therapies that were supposedly rushed into production to halt the pandemic and return the
whole world to normalcy were actually being developed decades before the World Health
Organization announced on March 11, 2020 that a worldwide “pandemic” was underway. In fact,
documents that have recently come to light show that hundreds of millions of doses of the genetic
remedies for the “pandemic” were already in production by December, 2019 (Huff & Lyons, 2023),
well before the WHO even announced the existence of any “pandemic”. Perhaps the most damning
document of all is one showing that the US Department of Defense had issued a subcontract dated
November 12, 2019 to Labyrinth Global Health, Inc., specifically calling for “COVID-19 research” a
full month before the supposed virus was even named (Reese, 2023), and four months prior to the
lockdown that occurred in the US after the March 11, 2020 announcement by the WHO that a
worldwide pandemic was underway. The contract in question was part of a much larger one with
Black and Veatch — a company deeply tied to the US military from World War I forward, but
especially during and following the Manhattan Project of World War II (“Black & Veatch”, 2023)
leading to the nuclear bombs dropped on Hiroshima and Nagasaki. Until the penetration of biology
to the nanometric level of molecules, atoms, and beyond, the military industrial entities of the world
seemed to direct public fear and attention to nuclear weapons. Now, the world’s worst nightmares
seem to be about biological threats and the transhumanist revolution that seems to be looming on

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the horizon (see, for instance, Bostrom, 2003, 2005; Broudy & Kyrie, 2021; Kyrie & Broudy, 2022a,
2022b; and their references).
HYPOTHESES TO BE TESTED USING DATA FROM EMERGING CLINICAL OUTCOMES
Regarding the still ongoing two-phase worldwide pandemic experiment, we seek to test three
alternative hypotheses with respect to the abnormal clotting phenomena and the associated disease
conditions that were already emerging in phase one of the ongoing experiment. We draw the
reasonable inference that the “fibrosing” pointed out in autopsies of dead victims of COVID-19
disease by Chertow et al. (2021; see Figure 3 above), together with the diffuse distribution of the
SARS-CoV-2 virus not only in the lungs, liver, vasculatory system, kidneys, heart tissue, and the
brain, in all probability, must be involved in causing the cascading series of biosignaling injuries
leading to whatever increase in all-cause mortality has actually occurred from t1 to the remaining
segments of the time frame spelled out in Figure 4.
From here forward we keep the SARS-CoV-2 spike protein and its genetically engineered versions in
the COVID-19 “vaccines” in the background until we come to our Discussion and Conclusions
sections. All the while, however, we also have in mind, as Segalla (2023; see his discussion in English
here) has pointed out, that the lipid nanoparticles used to deliver whatever unannounced ingredients
the BNT162b2 and Moderna injectables may contain, are injurious on their own. Therefore, it would
be irresponsible and incautious not to test the alternative hypotheses that we formulate explicitly in
this section of our paper.
The null hypotheses concerning the mean rate, µ, of all-cause mortality per 100,000 population for
each of the time segments of interest, if there were no impact from the two-phase worldwide
pandemic experiment, can be summed up as follows:
H0: µt1 = µt2 = µt3
However, there are three alternative hypotheses to be tested. The first alternative is the unsurprising
expectation that the standardized weekly pre-pandemic mean of all-cause mortality per 100,000
persons in the US population distributed over a comparable time frame prior to the worldwide
exposure to SARS-CoV-2 should be less than the standardized weekly mean during the pandemic:
HA1: µt1 < µt2, at an α < 0.0001
We set a stringent significance (α) level at p< 0.0001 for HA1 (and for the subsequent alternative
hypotheses) because the stakes are life and death. HA1 merely asserts that after the onset of the
worldwide pandemic all-cause mortality up to the time when the COVID-19 vaccines were
introduced, SARS-CoV-2 and whatever consequences the declared pandemic brought with it (e.g.,
lockdowns, social distancing, masks, increased fear and stress, suicides of desperation, loss of life
from comorbidities, businesses that failed, etc.) must have logically caused an increase in all-cause
mortality above the previous baseline before the announcement of the pandemic.
18
18
Other factors that are literally coming to light and the electro-magnetic frequnecies at distinct wavelengths that can,
like the microwaves in everyone’s kitchen, be tuned to particular molecular entities from water molecules to virions,
bacteria, and brain cells. Certain metals and carbon-based composites (Jernigan & Joseph, 2005; Jernigan et al. 2021;
Yanowitz, 2022) have been implicated along with the strange abnormal clots discussed above in this paper.

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Next, a slightly more interesting alternative hypothesis concerns the downturn in all-cause mortality
as people in the US were supposedly benefiting — according to the government-approved CDC,
Department of Defense, etc., narrative — from 1 to 7 doses of the COVID-19 injectables.
According to promoters of the injectables, the administration of hundreds of millions of doses in
the US should have caused a return of all-cause-mortality in the US population toward (if not all the
way back to) the standardized weekly mean of all-cause mortality at the µt1 baseline before the
pandemic began in t2. HA2 takes into consideration the difference between the baseline all-cause
mortality mean µt1 prior to the pandemic minus the mean µt2 in order to estimate what should be
expected once the curative/preventative COVID-19 vaccines were widely distributed during t3. HA2
takes account of the expectation/promise that the COVID-19 vaccines would move the mean of
all-cause mortality, after they are injected into more than half the world population and from 67% to
79% of the US population, back in the direction of the pre-pandemic baseline of t1. According to
the public narrative the whole basis for creating the COVID-19 experimental “vaccines”, and for
injecting more than 13.2 billion doses of them into more than 5.1 billion people worldwide at a cost
of trillions of dollars (Pharmaceutical Technology, 2023) was to stop the pandemic attributed to the
spike protein of the SARS-CoV-2 virus. More specifically, the genetic therapies provided in the
experimental COVID-19 “vaccines” were intended to commandeer the ribosomal systems of
recipients causing them to produce the offending SARS-CoV-2 spike protein on the theory that the
immune systems of recipients would attack the virus and thus return impacted populations to the
neighborhood of the former baseline of all-cause mortality during the pre-pandemic period often
referred to as “normalcy” in the mainstream media (Economist Group, 2021; Administration for
Community Living, 2022; Brennan, 2023). With that in mind, HA2 can best be stated as follows:
HA2: (µt3 – (µt2 – µt1)) ≈ µt1, at an α < 0.0001
There is one additional alternative hypothesis, actually consisting of a series contained within the
scope of HA2 provided it should turn out to be true. This latter series, which we loosely refer to as
HA3, follows from the assumption that multiple doses of COVID-19 injectables should be more
effective in returning the recipient groups, all else being equal, to the mean all-cause mortality before
the two-phase experiment began. If so, the following series of inequalities should be true across the
board where the greater than symbol “>” is taken to mean that all-cause mortality should decrease
from zero doses to the nth booster — more doses working better than fewer doses in returning the
population toward (if not to) normalcy:
HA3: 0 dose > 1 dose µt3 > 2 dose µt3 > 3 dose µt3 . . . > 7 dose µt3
In other words, in accord with the mainstream narrative, HA3 asserts that, all else being equal,
significant increases in longevity (lowering of all-cause-mortality) should follow with each additional
dose of a COVID-19 vaccine received by any randomly selected sample of persons in the
population. However, based on our review of the background literature on abnormal clotting
phenomena in the first part of this paper, there is a completely opposite version of HA3 in which the
symbol “>” is read to mean that each additional dose beyond the first will increase all-cause
mortality for any random sample of the population (i.e., will reduce the time remaining to live)
making recipients worse off instead of better off — as Seneff and Nigh (2021) and also Seneff, et
al. (2022) have been arguing. In what follows we will rigorously test both of those versions of the
sequence, the one that favors the mainstream narrative and the one that refutes it.

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RESULTS: CLINICAL OUTCOMES OF THE PANDEMIC EXPERIMENT
For any given segment of time, the underlying formula for standardized reporting of all-cause
mortality is the following:
(𝑎𝑙𝑙 𝑑𝑒𝑎𝑡ℎ𝑠/𝑡𝑎𝑟𝑔𝑒𝑡 ∗ 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛) X 100,000 = 𝑎𝑙𝑙 𝑐𝑎𝑢𝑠𝑒 𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 𝑝𝑒𝑟 ℎ𝑢𝑛𝑑𝑟𝑒𝑑 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑝𝑒𝑜𝑝𝑙𝑒
The critical data points in the formula consist of the ratio inside the parentheses at the left side.
Deaths need to be accurately recorded, and the size of the population during the time frame at issue
must also be estimated with reasonable accuracy.
In the first part of the analysis that follows, for population estimates we have relied on the US
Census Bureau for each of the segments of time in question (see footnote 11), and for the record of
deaths we have used data from the Centers for Disease Control and Prrevention (2023). Multiplying
the result of the quantity in the parentheses on the left side of the equation times 100,000 merely
converts the ratio to a standard form enabling comparisons, say, between all-cause mortality across
populations of different sizes, for instance, the whole US population compared against a smaller or
larger population, component of the whole population, or a randomly selected sampler of persons
within the larger population or some subcomponent of it. In what follows we look first to the whole
US population to test HA1 and HA2, and then, to test HA3, we rely on a sample of Medicare
participants who received only one dose of COVID-19 vaccine to compare with random samples
drawn from the state of Connecticut who received none or from 2 to 5 doses of COVID-19
vaccine.
Column (F) in Table 2 reports the mean of weekly all-cause mortality for the whole US population
(as recorded by the CDC during each of the time periods specified in column A) — where rows 5,
6, and 7 correspond to data from the entire US population for t1, t2, and t3 of Figure 4. To make the
weekly rate more interpretable, column (B) in Table 2 converts the raw weekly mean number of
deaths in each time segment to a standardized weekly mean per 100,000 persons. It is worth noting
that the US population from week-to-week during the entire time period segmented into three parts
in Figure 4 — pre-pandemic baseline period (t1), pandemic period (t2), and experimental vaccine
administration period (t3) — must consist in a large proportion of instances of the very same
individuals. In fact, they are very nearly the same people in the time segments on either side of t2.
(excepting those who died previously, or immigrated into the US, or were born during the time
frame).
According to the US Census Bureau’s Population Clock (on March 20, 2023 shown just below as
Figure 5), the population in the US is increasing by one newborn child every 9 seconds on the
average, and by one immigrant every 32 seconds. At the same time the population is being decreased
by one death every 10 seconds. The absolute difference in the population from one time segment to
the next, therefore, amounts to approximately 2.37% of the total population from year-to-year as
seen in Table 3 (on the following landscape page). This means that from year-to-year, we can
estimate that about 97.6% of the people are the same individuals as those who were present in the preceding
year. As a result, the comparisons of greatest interest with respect to the hypotheses stated earlier
come very close to meeting the required assumptions for a repeated-measures design except for the
fact that the people who end up dead can only die once. Nevertheless, the exposure to SARS-CoV-2
during t2 happened to about 97.6% of the people who were present during t1 and who will still be

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Table 2
All-Cause Mortality in the United States Annualized in Each of the Various Time Segments Leading up to and Including the
Worldwide Pandemic Experiment inRows (6) and (7)
(A) Time Segment
(Year-Month-Day)
(B) All-Cause
Mortality per
100,000 People
in the USA
(C) Total
Unadjusted All-
Cause Mortality
(Raw) for Time
Segment
(D)
Population
During Time
Segment †
(E) Total All-
Cause Mortality
during the Time
Segment Made
Proportional to a
52 Week Year ‡
(F) Mean All-Cause
Mortality per Week
for the Time Segment
in Question
(1) 2011 thru 2015 all weeks
821.5
12,994,778
1,581,910,672
2,598,956
49,980
(2) 2016 all weeks
849.3
2,744,248
323,127,513
2,744,248
52,774
(3) 2017 all weeks
864.5
2,810,988
325,147,121
2,810,988
54,057
(4) 2018 all weeks
867.8
2,839,074
327,167,434
2,839,074
54,598
(5) 2019 all weeks thru week 11 of 2020
879.1
3,505,841
329,158,518
2,893,710
54,826
(6) 2020-3-21 thru 2020-12-5 Pandemic
before Any COVID-19 Vaccines)
1011.8
2,451,254
331,511,512
3,354,348
64,507
(7) 2020-12-12 thru 2022-12-31
Pandemic After COVID-19 Vaccines
1023.7
7,072,106
332,612,403
3,405,088
65,482
† In row 1 of this column, the CDC summed the population estimate for the 5 years at its Wonder Bridged-Race Population Estimates website. Their estimate for
those 5 years agrees closely with the US Census Bureau total "resident" US population estimate at 1,582,997,300, based on the July 1 date for each year. Given that the
July 1 date falls at the middle of each calendar year, and is the evident standard applied by the CDC in calculating annualized all-cause mortality for the US population,
in rows 2-6 we use the mid-point estimates from the US Census Bureau for each of the time segments in question.
‡ Here in data column 4, if the time segment is greater than a full year (as in rows 1, 4, and 6) or less than a full years (as in row 5 marking the 38 weeks of t he
experimental pandemic prior to the administration of any COVID-19 vaccines), the CDC weekly average all-cause mortality as reported in March 2023 (column 5) is
multiplied by 52 to obtain an annulaized (standard) rate for the whole of the time segment in question. Then, to obtain the crucial all-cause mortality per million people
(column 1), so the comparisons are exactly as fair as the accuracy of the US CDC and Census Bureau data allows them to be, we simply divid e the total unadjusted all-
cause mortality in column 3 by the median population estimated by the US Census Bureau for the time frame in question to obtain the criterial annualized all-cause
mortality for the time segment in question as reported in in column 1.

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present during t3. As a logical consequence it
follows that we are in each of the time periods at
issue largely comparing apples with apples as the
expression goes. Approximately 97.6% of the
people who came into the experimental pandemic
during t2 remained in place for whatever
challenges or benefits the COVID-19 injections
might add into the mix during time segment t3. It
follows that the near homogeneity of the
population variance should only be affected by
environmental and/or governmental changes
impacting almost a large percentage, or all, of the
population because the affected population itself
consists of about 97.6% the self-same individuals
for the segments on either side of the middle
segment, t2, consisting of phase-one of the
experimental pandemic. Except for the fact that
people can only die once, the transition from
phase-one to phase-two conforms to the
requirements of a repeated-measures design in
which members of a population are compared
only against themselves with respect to some treatment such as the introduction of the virus in the
transition from t1 to t2 or the introduction of the COVID-19 injections at the transition from t2 to t3.
Otherwise, all else must remain approximately the same across the time segments — more
particularly, except for a relatively small percentage of persons who are new to the population or
who have left due to migration or death, the population across the three time segments is 97.6% the
same as shown in Table 3.
It is also known with mathematical
certainty from the central limit
theorem (Pólya, 1920; Le Cam,
1986) that the sampling distribution
for sizes above about 25 individuals
will be normally distributed and
have a standard deviation equal
approximately to the square root of
the standard deviation of any given
actual sample of that size (i.e., the
standard error of measurement).
When the numbers reach into the
tens of thousands the stability of
estimates, all else being equal, is
assured. The upshot of the numbers in Figure 5 and Table 3 is to say that the statistical estimates of
means, variances, and the contrasts needed to test the various hypotheses stated above can be
counted on in what follows here for the requisite reliability to definitively answer the implicit and
explicit statistical questions posed.
Figure 5. Components of population change according
to the US Census Bueau and its World Population
Clock.
Table 3
Estimating the Proportion of the US Population Remaining
the Same in the Three Time Segments of Figure 4.
Estimated births per annum at 1 every 9 seconds
3,504,000
Immigrants per annum at 1 every 32 seconds
985,500
Deaths per annum at 1 every 10 seconds
3,153,600
Estimated total change in persons per annum in
the US population
7,643,100
Estimated proportion of the total population that
changes each year
2.37%
Estimated proportion of the population remaining
the same across all time segments in Figure 4
97.63%

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Also, the design of the pandemic experiment as laid out in Figure 4 above reduces the typical
complexity of CDC and Our World in Data reports (often dividing time segments into weeks, days,
or even hours, multiplied by as many as 11 age groups from 0 years to 80+ years) into a simple three
segment comparison format that can be easily understood. The crucial components of the design
are the independent variables which are well-known and ubiquitous: they consist of (1) the SARS-
CoV-2 virus (plus whatever it brought with it, lockdowns, masks, social distancing, fear, stress,
business failures, loss of employment, suicides, etc.) during t2, on the one hand, and of (2) the
COVID-19 injections during t3 on, the other hand. It follows that all the assumptions that need to be
met in order to test the various null and alternative hypotheses, indeed are satisfied and the powerful
and extraordinarily simple statistic known as Welch’s t-test (2023) is supremely applicable. It is a
generalization of the long-standing Student’s t-test, but is more robust than a one-way analysis of
variance which might be applied to assess the impact of the independent variables. Welch’s t-test is
specifically adapted to problems with samples that have unequal numbers of cases and unequal
variances as do all of the contrasts in what follows.
According to the mainstream published medical researche, essentially all the “excess deaths” in the
category of all-cause mortality during the period designated in Figure 4 as t3 must be attributed to
SARS-CoV-2 or its variants. However, SARS-CoV-2 (and whatever it brought with it) is the only
ubiquitous independent variable in t2. Similarly, the only ubiquitous independent variable that is
added to a substantial majority of the whole population living during t3 consists of the newly
introduced COVID-19 injectables that were administered to between 60 and 79 percent of the entire
US population during the time frame at issue. The fact that the COVID-19 “vaccines” were not
present at all during t2 guarantees that whatever beneficial change the injections might have brought
to bear must register independently of the purportedly negative impact of the SARS-CoV-2
bioweapon and whatever stress factors it brought with it to the whole population. However, during
t3, because the impact of the injectables is added only to a portion of the population in 1 to 7 doses,
independent samples of each of the impacted groups contrasted with those not impacted, should
produce a significant and measurable impact. If the mainstream narrative is true, each dose, on the
average, all else being held equal, should enhance the longevity of the recipients. If the narrative is
false and the injectables are doing harm, each additional dose should increase all-cause mortality in
the impacted group. In fact, if the mainstream narrative about returning the whole population to
“normalcy” is correct, the mean all-cause mortality expressed as µt3 ought to be about as much less
than µt2, as µt1 is than µt2. If the injectables are working as intended, they should cause a return
toward, if not all the way back to, the baseline of all-cause mortality during t1. At any rate, whatever
benefits or injuries hundreds of millions of doses of the COVID-19 injectables brought with them
should be measurable and substantial in contrasts of samples from t2 to t3. In particular, those
people only exposed to SARS-CoV-2 and its accompanying stress factors across both phases of the
experimental pandemic, should either be measurably worse off (according the mainstream narrative)
or better off according to Seneff et al. and those who regard the COVID-19 injectables as a bad
idea.
ALTERNATIVE HYPOTHESIS ONE (HA1)
With all the foregoing in mind, Table 4 reports Welch’s t-test examining the predicted contrast
between t1 and t2. The test applied is the one-tailed variety because SARS-CoV-2 and whatever other

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stress factors it brought with it during t2 can only be expected to increase all-cause mortality. Because
there is nothing during the t1 time frame that is remotely comparable to SARS-CoV-2, the stress
factor that differentiates the two time frames (t1 from t2) is an independent variable not impacting t1
at all. Incidentally, the introduction of the stress factors associated with the experimental pandemic
in t2 requires the assumption that the variances across samples from those time frames should be
unequal. More particularly, as is confirmed in the analyses to follow, increasing stress on the
population being sampled can only be expected to cause greater variability in the all-cause mortality
statistic.
Table 4
Two-Sample Welch’s
t-
test Contrasting t2 Against t1 Assuming Unequal
Variances and Testing HA1 that the COVID-19 Pandemic Would
Significantly Increase All-Cause Mortality in the US
Pandemic (t2)
Baseline All-Cause
Mortality (t1)
Mean
64506.68
54825.77
Variance
33962289
12786896
Observations
38
167
Hypothesized Mean Difference
0
df
44
t Stat
9.827899
P(T<=t) one-tail
5.69E-13
t Critical one-tail
4.057435
Looking to the results in the first line of Table 4, the probability that an increase of approximately
9,680.9 deaths could be added to the weekly 54825.77 average from t1 to get to the weekly average of
64,506.68 deaths by chance during t2 is estimated at approximately 5.69 in 10-13. In other words, it
has virtually zero likelihood of ever occurring by chance. This result suggests that the magnitude of
the problems that the weaponized SARS-CoV-2 brought with it were both significant and
substantial.
Table 5
Two-Sample Welch’s
t-
test Assuming Unequal Variances and Testing HA2
that the COVID-19 Injectables in
t3
Would Return the US toward the Pre-
Pandemic Level of All-Cause Mortality in the US Making It Substantially
Lower than in the Pandemic Period
t2
with No Vaccines Available
Vaccine Period of the
Pandemic (t3)
Pandemic without the
COVID-19 Vaccines
(t2)
Mean
65482.46296
64506.68421
Variance
75887381.73
33962289.41
Observations
108
38
Hypothesized Mean
Difference
10,000
df
97
t Stat
-7.14230086
P(T<=t) one-tail
8.48 E-11

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t Critical one-tail
3.87
Such a contrast cannot occur by chance. Therefore, the null hypothesis that µt1 = µt2 can be ruled out
and HA1 that µt1 < µt2, at an α < 0.0001 must be accepted.
Next we consider HA2. Table 5 reports the two-sample Welch’s t-test. Again, because the
directionality and approximate magnitude of the predicted return to normalcy once the COVID-19
injectables were administered to more than half the world, and more than 70 percent of the people
in the US, was measured and known prior to the application of this test, Table 5 uses a one-tailed
Figure 6. All-cause mortality per 100,000 people in the US appears on the vertical axis whereas the time segments
leading up to and including the worldwide pandemic experiment are charted on the horizontal. Blue bars show
weekly all-cause mortality per year per 100,000 persons for (1) the years 2011-2015, (2) 2016, (3) 2017, (4) 2018,
(5) 2019 through the 11 weeks of 2020 up to the announced onset of the COVID-19 pandemic by the WHO,
(6) the official time segment occupied by the pandemic itself up to the time when the vaccines were introduced, and
(7) represents the time period combining whatever SARS-CoV-2 variants were still circulating along with the
injectables.

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Welch’s t-test and the results are contrary to the government-sponsored narrative. What we find is
that, instead of returning to normalcy with the multi-trillion dollar phase-two of the worldwide
experiment, the COVID-19 injections increased the level of all-cause mortality during the pandemic,
t2, and at a probability level that not only rules out the null variant of HA2 but makes the case that
the COVID-19 vaccines are doing more harm than good, as predicted some months ago by Seneff
and Nigh (2021) and also by Seneff, et al. (2022).
The injectables are doing essentially the same level of harm as the weaponized virus and its stress
factors during the pandemic period, t2, and rather than calming the situation back to (or even
towards) normalcy, the injections have elevated all-cause mortality.
Another way to look at the data from Table 5 is offered in Figure 6. If the COVID-19 injectables
were working to return the world to pre-pandemic normalcy, we should expect a drop of the red bar
at the right-hand side of Figure 6 back to (or at least significantly towards) the level of all-cause
mortality during the baseline period defined at t1. But instead, not only do the injectables perform
much like the SARS-CoV-2 virus along with whatever other stress factors it brought with it
(lockdowns, fear, etc.), they exceed the level of damage done in the pandemic phase of the
experiment by actually increasing the mean all-cause mortality across the entire US population.
Finally, we turn to HA3. In Table 6 we present data from samples of Medicare participants drawn
either from the whole population in the US, or from the State of Connecticut. Notably these are
people who must be 65 years of age or more to enter that program. The data in Table 6 refers to
about 157,495 Medicare participants who died on or before December 31, 2022.
To show the generalizability of the
Medicare data, it must be noted that,
all else being held equal,
comorbidities owed to cumulative
injuries, diseases, and the like in a
span of not less than 65 years, makes
this segment of the US population
people more susceptible to any
additional stress factors than any
younger segment of the population.
In the CDC promoted narrative, it
has therefore been argued that the
people in the Medicare age range
should be prioritized to make them
first to receive COVID-19 vaccines.
Whereas US Medicare participants are
more vulnerable than their younger
counterparts to comorbidities, we
have evidence that the impact on the
COVID-19 injectables generalizes
from the 65 and up age bracket across
the board to younger people. In the UK during weeks 34-52 in 2021 and weeks 1-12 in 2022, Oller
and Santiago (2022) found a nearly perfect correlation r = 0.99881 between all-cause mortality ((N =
Figure 7. Deaths reported to Public Health England for Weeks 34-52
(excluding week 51) in 2021 and for Weeks 1-12 of 2022 for 16,724
persons who tested positive for COVID-19 within 60 days before
they died. The total deaths with 1 to 3 doses of COVID-19
correlated with total deaths reported from all causes at 0.99881.

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31,437) across all age groups in 10-year increments from 0 to above 80 for persons who died in a
hospital reported to Public Health England and 26,013 of whom died after for 1 to 3 doses of
COVID-19 vaccine. Figure 7
graphs the observed correlation in
the yellow and gray lines. That
almost perfect correlation between
vaccine dosage and mortality
across all age groups reporting
deaths from all causes to Public
Health England assures
researchers of substantial
generalizability across similar
populations worldwide. Given that
the correlation is nearly perfect
across all age groups, it follows
that the older and more vulnerable
people impacted by the pandemic
experiment cannot be expected to
be very different on account of
their age and greater vulnerability
to the stresses of the experiment
itself than younger persons. The
differential impact of the
pandemic with all its stresses
during t2 as contrasted with t3
when the COVID-19 injections
were added into the mix in
hundreds of billions of doses
should generalize to the
population in the US and in the
world at large.
To assure the reliability of
reporting of relevant deaths to the
CDC, we marked the end of t3 (as
shown in Table 2) at December 31,
Table 6
Days Left-to-Live for Persons on Medicare Who Died After
Exposure to SARS-CoV-2 and After Zero to Five Doses of
COVID-19 Vaccine
Number
of Doses
Mean of
Days Left-
to-Live
Standard
Deviation
Variance
Number
of Cases
Zero†
427.84
12.34
121.11
26091
One‡
307.90
186.66
34842.20
108155
Two ¤
247.22
10.60
112.47
14356
Three
198.62
118.62
14071.40
7474
Four
112.43
72.53
5260.18
1211
Five
58.63
51.25
2626.21
207
† The "Zero" group consists of 26,090 Connecticut Medicare
participants who were almost certainly exposed to SARS-CoV-2 virus at
least by the midpoint of the pandemic, July 27, 2020, but who chose not
to accept any COVID-19 vaccinations. The people in this sample
nonetheless died before January 1, 2023. However, during the pandemic
experiment, Connecticut had 689,572 Medicare participants
(HelpAdvisor.com, 2023). Given the greater life-expectancy of the
“Zero” dose group, the number of days left-to-live for that group must
be substantially under-estimated. Therefore, the contrast in Figure 8
between the “Zero” and “One” groups must be greater than it appears
to be there because many more of the “Zero” dose people will have
survived into 2023 and beyond, than those who received one or more
doses of the COVID-19 injections. Incidentally, the correlation between
days to survive and proportion of the population represented in the
various Connecticut samples of this table is 0.989. The Medicare
particiants seem to have increasingly perceived the consequences of
taking the shots as the pandemic experiment progressed.
‡ The "One" dose group in the Connecticut data seemed to omit people
who received a free vaccination outside the Medicare system. This
inference is based on the fact that 6,366 more people were reported as
having received "Two" doses (14356) than were reported as receiving
only “One” (7911). To correct that logical problem, for the data charted
in Figure 7, we calculateed mean days left-to-live for the 108,155
Medicare cases from the whole US who died on or before December 31,
2022. These records were available to us in the same dataset thanks to
Steve Kirsch (2023a, 2023b).
¤ As noted, in this Connecticut Medicare dataset nearly twice as
many people were reported as receiving a second dose than who
were recorded as receiving a first.

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2022.
19
Given that 665,886,823 doses of Emergency Use Authorized COVID-19 “vaccines”from
Johnson&Johnson, Moderna, Novavax, and Pfizer/BioNTech (Pharmaceutical Technology,
Figure 8. Average number of days left-to-live for persons who died before January 1, 2023 and
who were exposed to both the SARS-CoV-2 virus and stresses of the pandemic (t2) plus
whatever number of doses of the COVID-19 vaccine they received during t3.
March 11, 2023) were administered to the entire US population during the 814 days from December
14, 2020 to December 31, 2022, we should be able to find a measurable effect from t2 to t3
attributable exclusively to the COVID-19 injectables. The effect of the experimental SARS-CoV-2
virus, stress, lockdowns, etc. during t2 should be markedly impacted by an independent effect of the
COVID-19 injectables administered only in t3. True, other factors during t3 may interact with the
billions of injections, but no such interaction is possible in t2, so any interaction must also show up
in t3 as a distinct (orthogonal, completely independent, contribution of the COVID-19 injections).
This follows with irrefutable logic on account of the fact that the population of people impacted
contains 97.6% of the same individuals from the prior time period. They are being compared only
against themselves. Therefore, the other factors in either time frame must be as either equivalent or,
if not, as impacting the same people across the time segments so that the only measurable difference
in all-cause mortality must be attributed to the introduction of the injectables.
19
The early termination of our reckoning to compare the three time periods of the two phase experiment was on
account of the lag between death and reporting to the CDC. We were able to check the weekly deaths recorded in two
distinct databases on a week by week basis and the records we are using in this report from the CDC seem to be reliable.
The two data sources, CDC and Our World in Data, correlated at 0.999999798. The total difference in reporting showed
331 more persons recorded by the CDC than by Our World in Data.

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On December 14, 2020, the first day of vaccine availability in the US, 4,824 doses were administered
and on the last day of t3, December 31, 2022, there were 38,069 doses administered (according to
Our World in Data, March 11, 2023). Given such widespread exposure the supposedly beneficial
impact of the COVID-19 “vaccines” added on top of whatever negative impact was still being had
by SARS-CoV-2 and its variants, a substantial reduction in all-cause mortality should have appeared
in the injected recipients during t3.
The expected effect — according to the almost universal narrative supported by the CDC and the
mainstream press — would be a return to (or at least towards) the pre-pandemic “normalcy” during
t1 (Economist Group, 2021; Administration for Community Living, 2022; Brennan, 2023). If that
promise were true, the red bar in Figure 6 (above) should drop significantly toward (if not all the
way to) the level of the horizontal broken line in that figure. It should have fallen back toward the
pre-pandemic level of all-cause mortality. It did not.
With that in mind, finally, we come to HA3 which, by the mainstream promises, predicts an increase
in life expectancy with each additional dose of COVID-19 vaccine. We find the opposite. Table 6
and the results graphed in Figure 8 tell the tale. There is a significant reduction of life expectancy,
averaging 74 days, from zero doses of COVID-19 vaccine to one, two, and so forth up to five doses.
Persons surviving beyond a 5th
dose were so few (only about 1
in 10,000; actually 8 of 57,261,
only 1 of whom survived to
take a 7th dose) that they are
not included or shown in
Figure 8. On the average,
Medicare patients who refused
all COVID-19 injectables (all
of whom died), according to
the Medicare records
summarized in Table 6 and
graphed in Figure 8 (see the
top two entries), survived
119.94 days longer than those
who took just one dose of the
COVID-19 “vaccine”.
20
According to the alternative
hypothesis at issue, HA3, the life expectancy of persons surviving the pandemic during t2 should —
if all other factors are held the same as they are when the people sampled are 97.6% the very same
individuals — be increased by each dose of COVID-19 vaccine received during t3. This is what we
should expect if the mainstream narrative were true. However, the Medicare data summarized in
Table 6 and graphed in Figure 8 demolishes that theory.
Each new dose, on the average, shortens the time left-to-live by 74 days for the average independent
samples. The patients in the Medicare system who must be 65 years of age to qualify for
20
Throughout this section we are relying on Medicare data available on the website of Steve Kirsch (2023a, 2023b) to
anyone wishing to analyze it. We also include in our Suplementary Files, all of the data we have used in this paper.
Table 7
Welch’s
t
-Test Assuming Unequal Variances: Two-Samples
Consisting of 14,356 Medicare Participants Who Received Two
Doses of COVID-19 Vaccine Contrasted with 7,473 Connecticut
Participants Who Received Three Doses of COVID-19 Vaccine
Two Doses of
COVID-19
Vaccine
Three Doses of
COVID-19
Vaccine
Mean Days to Live
247.22
198.62
Variance
28084.12
14069.90
Observations
14,356
7,473
Hypothesized Mean
Difference
0
df
19,892
t Stat
24.80
P(T<=t) one-tail
4.3454E-134
t Critical one-tail
3.72

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participation in it — people according to the mainstream narrative who are the most in need and
most likely to benefit from getting all the injections available and recommended by the CDC —
according to the Medicare data analyzed here, irrespective of any other factors (age, income,
multiple comorbidities, etc.) are almost certain to die sooner from one dose than none, sooner from
two than one, and so forth. Hardly anyone in the Medicare system can survive the first five doses in
order to receive a sixth. In the Medicare data at hand, only 7 people of 57,261 individuals in the data
set (see Supplementary Data Files), were willing and able to take six doses of any COVID-19
vaccine. They died, on the average within 34 days of that sixth dose. Only one person in the entire
dataset (a 68-year-old person) survived the first six doses to take a seventh before dying within 69
days. The reason, according to the dotted trend line of Figure 8, is that the CDC recommended 7
doses of the vaccine are sufficient to reduce the life expectancy of recipients, on the average, by
more than the number of days they would have had left-to-live even if they had taken no doses of
the injectable(s) at all (74 X 6 = 444 days; 16.16 days longer than the average time left to live, 427.84
days, for people who took zero doses).
21
To keep things simple and non-redundant, although we tested every contrast in Figure 8 with the
supremely relevant and applicable Welch’s t-test for samples with unequal sample-sizes and unequal
variances, we only need to report the result for the smallest contrast at issue to establish that all the
adjacent contrasts in Figure 8 are significant at a level that is more definitive than 4.34 raised to the
negative 134th power (see the next to last line in Table 7). And any event with that remote level of
probability cannot be expected to occur at all. The number of repeat experiments required to
produce such a result, in theory, would be many orders of magnitude greater than the estimated
number of particles in the universe which comes to about 1080 (Heile, 2018).
In fact, that smallest contrast in Figure 8, between two doses and three, is significant at a probability
level approximating absolute zero. It leaves no remaining likelihood at all that any finite number of
repetitions of the pandemic experiment would accidentally occur, strictly by chance, producing a
contrast as large as the one observed. Given that the contrast showing a loss 48.6 days left-to-live
when Medicare participants opted to get a third dose of COVID-19 vaccine is the smallest one in
the whole series, every component of the HA3 series of alternatives must be accepted and every null
counterpart of HA3 must be rejected. The hypothesis predicting a measurable negative impact of
each successive dose of COVID-19 vaccine on life expectancy of recipients must be accepted.
Each additional dose of COVID-19 vaccine — all else being held equal by virtue of the fact that
there is no reason to expect that sicker or older, or otherwise more prone-to-die persons are more
likely to get another dose of COVID-19 vaccine than people who are healthier, younger or less
prone to die, etc. — on the average, shortens the life expectancy of the Medicare participant
receiving it. Each successive shot diminishes the remaining lifespan of persons like the thousands of
Medicare participants in the data set examined here, on the average, by 74 days per dose received.
DISCUSSION
For some time now a few outspoken medical doctors of distinction such as Peter McCullough —
one of the most published MDs of the world and one of the most sought after editors,
21
As one of us noted in an earlier paper (Santiago, 2022b), getting successive doses of COVID-19 vaccine is like playing
Russian Roulette. Before the sixth, much less seventh pull of the trigger, the spin will have landed on an non-empty
chamber, and as Kirsch (2023a) put it: “Game Over”.

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practitioners, researchers, and theoreticians in mainstream medicine — have been warning that the
worldwide pandemic experiment has resulted in millions of cases of myocarditis. According to more
than 200 mainstream peer-reviewed papers that McCullough referred to in March 2022 (see and hear
him here), myocarditis was being diagnosed at a rate of about 4 cases per million per year prior to
the introduction of the COVID-19 injectables. During the worldwide pandemic experiment, the
number of cases that have occurred worldwide, according to the mainstream research literature as
early as a year ago, already accounted for about 25,000 million cases in recipients of two or three
doses of any one of the marketed COVID-19 vaccines. He estimates that during the pandemic
experiment period about 500 cases per million per year were occurring in male athletes in peak
physical condition who were between the ages of 18 to 24 years. We ourselves find it interesting that
McCullough, a distinguished MD, cited the world’s formerly most “notorious” and now perhaps
most admired tennis champion, Novak Djokovic, to bolster his (McCullough’s) case and public
appeal.
Notably, Djokovic was banned from the Australian Open 2023 because of newly created laws there
requiring “foreigners to be vaccinated”. He was already there but refused to take any COVID-19
vaccine and was deported. Against enormous pressure, eight months after McCullough’s citation of
the tennis champion who refused to be injected, by November 2022, Djokovic won back the right to
play in the 2023 tournament (Nivison, 2022). To top it off, not only was he courageous in refusing
the COVID-19 vaccine, as McCullough also noted, but Novak Djokovic came back on January 29,
2023 to win the Australian Open for the 10th time. He did it, moreover, playing the final match
against Stefanos Tsitsipas in straight sets (ABC News January 15, 2023), in spite of the fact that
Djokovic had a radiographically documented three-centimeter tear in his left hamstring. In doing so,
Djokovic replaced the second best player, Nadal Rafael, to take the top ranking in the world, and
tying Nadal’s record of 22 Grand Slam wins (Gonzalez, February 1, 2023).
22
McCullough also cited several mainstream documents in the medical research literature to make his
case about circulatory problems brought on by the COVID-19 vaccines a little more than a year ago.
For example, Mansanguan et al. (2022)
23
estimated 23,000 serious cardiac issues per million and
LePessec et al. (October 24, 2022 cardio online, Switzerland) set the number at 28,000 per million.
During the two phases of the pandemic experiment (t2 and t3) as specified in Figure 4 above, we
have suggested multiple factors: first, whatever is in the COVID-19 injectables, but also variants of
22
We are grateful to Australian, Gerry Brady, Doctor of Medicine (retired) University of Queensland, Australia, with 30
years of patient focused clinical experience, and now the renowned Publisher and Editor of BOOM Finance and
Economics for reviewing this paper and for commenting particularly on this section. Among his many contributions to
the ongoing discussion is “COVID Under Question Cross Party Enquiry by Members of the Australian Senate and
Federal Parliament 23rd March 2022”. See some of the relevant videos here.
23
McCullough does not mention the fact that to get into the mainstream medical literature — an arena that he and his
colleagues in the editorial business must know as well as the rest of us who consult it know — it is necessary to include
some prominent but absolutely false statements such as the following in the second and third sentences of the opening
paragraph in the introduction to the work by Mansanguan et al.: “Clinical trials have revealed that the vaccine’s efficacy is
95% and its safety profile is good, similar to that of other vaccines [we omit four citations given here to support what is a known
falsehood that we have emphasized in red italics]. Systemic reactions to the vaccine, which were usually mild and transient
[our emphasis of the part of this half-truth that is known to be false] have been reported more commonly among the
younger population and more often after the second dose”. In the last part of the quotation, we omit three supporting
citations in support of the part of which actually happens to be true. It is true that “systemic reactions” are more
common for these injectables after a second dose and especially so in younger people.

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other disease agents, evolved mutants of the initial SARS-CoV-2 bioweapon, lockdowns, sudden
inaccessibility to healthcare, and the like.
In December 2022, a little more than a month before Djokovic would defy all odds to win the
Australian Open 2023, but several months after McCullough’s reference to the 200-plus mainstream
papers showing serious cardiological issues in well-known young athletes who, for whatever reasons
did not refuse the COVID-19 injections, Polykretis and McCullough (2022) wrote:
From January 2021 to the time of writing [this paper published December 22, 2022], 1598 athletes suffered
cardiac arrest, 1101 . . . with deadly outcome. Notably, in a 38-years timespan (1966-2004), 1101 athletes under
the age of 35 died (~29/years) due to various heart-related conditions, 50% of whom had congenital
anatomical heart disease and cardiomyopathies and 10% had atherosclerotic heart disease with early onset.
It is true that all kinds of factors make young athletes, males in particular, more susceptible to
cardiac events because of pushing themselves to the limits of extreme exertion not only in
competitions but also in training. Therefore, it is no surprise that catastrophic cardiac-related events
can occur in that group of people who are notably in far better physical condition than most non-
athletes and the general population at large. However, McCullough and his co-author Polykretis had
to examine a 38-year span of time to find 1101 athletes who ostensibly died of exertion-induced
cardiac stress. If we assume their research is approximately correct, COVID-19 injections have
produced 1101 lethal outcomes during slightly more than 24 months in t3 of the worldwide
pandemic experiment — since the “emergency authorization” of the “safe and effective” COVID-
19 “vaccines” with a “safety profile similar to that of other vaccines” — the COVID-19 vaccines
have increased the likelihood of athletes dying suddenly by a factor of 19 times. That factor is
estimated from the 38 years of 1966 up to 2004 times the 12 months of all those years divided by
the 24 months of t3 during the vaccination phase of the worldwide pandemic experiment when
McCullough and Polykretis were writing their paper.
Deaths from all causes, the unassailable best index to measure outcomes in the worldwide pandemic
experiment, have increased on the average by more than 10,000 persons per week since the
pandemic experiment began. The primary cause of all those deaths can be linked to the abnormal
fibrinaloid (proteinaceous but unprecedented) clots we documented in the first part of this paper.
Our statistical analyses of some very large datasets in the second part show that the “emergency
authorized” COVID-19 injectables did not return all-cause mortality to its pre-pandemic baseline
but actually raised it somewhat above the level of the pandemic itself prior to the release of the “safe and
effective” COVID-19 vaccines. Mainstream medical publications keep saying that the COVID-19
injections are “similar to other vaccines” (e.g., see Mansanguan et al., 2022) in spite of the fact that
the very spotty and under-used Vaccine Adverse Event Reporting System (Lazarus et al., 2010)
shows them to be more injurious and lethal during the first two years of their existence than all the
vaccines of recorded history put together across the whole period of VAERS record keeping
(Santiago, 2022b; Nass, 2023).
Excess deaths are increasing and the abnormal clots discussed in the first part of our paper are
probably the central causative factor. John Campbell, an MD in the UK (December 29, 2022) and
Meryl Nass an MD in the US (March 19, 2023) have documented the increase in deaths from all-
causes. They suggested that in March 2020 the weekly average was up by 197 persons above the
baseline but by October 2022 they estimated an increase of 1,564 per week based on UK data. By
February 11, 2023, Campbell (February 11, 2023) accentuated his concern about rising rates of all-

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cause deaths. As long ago as October 7, 2022, Joseph Ladapo, MD and Florida Surgeon General
released an analysis showing an 84% increase in the relative incidence of cardiac related death
among males 18-39 years old within 28 days following a COVID-19 mRNA injection. Our analysis
of CDC and US Census Bureau data throughout the pandemic experiment period from its start on
March 11, 2020 up to December 31, 2022 showed an average increase in all-cause mortality for the
whole US population at above 10,000 persons per week throughout the whole period.
Conclusion
The CDC, US Census Bureau, and Medicare data presented here show that the worldwide pandemic
experiment not only raised the US weekly all-cause death rate by more than 10,000 persons per week
but the administration of more than 635 million doses of COVID-19 injectables to well over half
the US population only made things worse. The expensive remedy only moved the all-cause
mortality index higher. Pressuring people to accept one or more doses of the COVID-19 injectables
not only did not return the all-cause death rate to its pre-pandemic baseline, it made things worse
just as Seneff and Nigh (2021) and Seneff, et al. (2022) predicted. Moreover, tens of thousands of
recorded deaths in Medicare participants from all 50 US states and from Connecticut, during the
period of the experiment after the COVID-19 injectables were marketed, reveal that life expectancy
for injected Medicare participants (people supposedly in the age bracket most needing protection
from infectious viruses, etc.) fell in a nearly straight line from an average of about 307.9 days after
one dose to about 58.6 days after five doses. Life expectancy for Medicare patients was shortened on
the average by 74 days with each dose of COVID-19 injectable fluid (whatever is in it).
Unfortunately, on the sixth dose, the average life expectancy according to the Medicare data is
already 16.16 days past zero.
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