Personalized Medicine - Science topic

Hussain, T., & Zia, M. (2021). Nanotechnology in cancer biomarker detection and screening: From diagnostic tools to personalized medicine.

How can AI be applied in Personalized Medicine? Artificial Intelligence (AI) is reshaping the landscape of healthcare, particularly in the field of personalized medicine. By leveraging huge amounts of patient data, AI is enabling more accurate diagnoses and tailored treatment plans. ## Transforming Diagnosis AI-powered systems are revolutionizing medical diagnosis in several ways: 1. Image Analysis: AI can analyze medical images with remarkable accuracy, often outperforming human experts in detecting subtle abnormalities in X-rays, MRIs, and CT scans. 2. Predictive Diagnostics: Machine learning models can predict the likelihood of diseases based on a patient's genetic profile and health history, enabling early intervention. 3. Pattern Recognition: AI algorithms can identify patterns in symptoms and test results that might be overlooked by human clinicians, leading to faster and more accurate diagnoses. ## Personalizing Treatment In treatment, AI is enabling a level of personalization previously thought impossible: 1. Tailored Treatment Plans: By analyzing a patient's genetic makeup, AI can recommend treatments most likely to be effective for that individual. 2. Drug Discovery: AI is accelerating the drug discovery process, identifying potential new treatments faster and more cost-effectively than traditional methods. 3. Precision Dosing: AI models can determine the optimal drug dosage for each patient, minimizing side effects while maximizing efficacy. if you have or are in the process of preparing a manuscript in the area pf AI in presonalized medicine, please don't hesitate to submit your article to our special issue "Application of Artificial Intelligence in Personalized Medicine: Diagnosis and Treatment" which is now open for submission.

Probiotics related.

How is pharmacogenomics transforming personalized medicine, and what are the current challenges in its implementation?

We're putting together a topic collection on tumor organoids and personalized medicine for Frontiers in Pharmacology and are looking for co-editors. Anyone interested in joining us for this project?

Currently, there is a high prevalence of breast cancer in women and prostate cancer in men. What are the main factors driving this prevalence, and how can we establish personalized medicine tailored to each population and its cultural context?

What techniques should be included for get involved in personalized medicine through Cytogenetic studies?

It aims to understand how AI technologies benefit the stem cell field.., potentially leading to advancements in areas like disease modeling, disability, and personalized medicine.

The untransduced T cells are produced by mock lentiviral transduction of human primary CD4+CD8+ T cells. These cells are subjected to comparable manipulations as CAR-T cells: activation, spinoculation (without lentivirus), and expansion. These T cells are meant to be negative controls in experiments using lentivirus-transduced primary CAR-T cells.

Why not to use Scramble or a vector without a construct? Why UTDs are used?

Currently, I am looking for a solution to develop chemotherapeutic drug resistance in a primary cancer cell line. But after a quick look at the literature, it is indicated that the management of acquirement of drug resistance takes plenty of time, more than eight months! Is there any convenient method to subculture chemoresistant cell lines in a short time? Or any other suggestions rather than eight months interval with an increasing dose of chemotherapeutic agent?

Best regards.

Recently, I try to expand antigen-specific T cell with peptide pool stimulation.

Each sample was stimulated with 9 to 21 peptides, and the concentration was treated with 1 uM. DMSO concentrations ranged from 0.09% (v/v) to 0.25%.

The medium used was AIM-V; BSA supplement + FBS 5%. On day 0, stimulation with the peptide pool was given and treatment with anti-PD1 antibody (5 ug/mL). And on days 3 and 6, IL-2 (200U/mL), IL-7 (10ng/mL), and IL-15 (100ng/mL) were treated, and ELISpot was performed on day 10.

In ELISpot, autologous PBMCs were put in the role of APC, and the number of expanded T cells + PBMCs was adjusted to 1x10^5/well.

As a result, two problems occurred. One is that a background signal(spot) is very high, and the other is that antigen-specific T cell enrichment does not occur well. If the background signal can be reduced, I think it will be easier to evaluate the enrichment because it is easier to compare fold changes. However, it is difficult because two problems occur at the same time.

In Results_2.jpg, peptide pool was CEF peptide pool. Sample 11 seems to have succeeded in enrichment, but sample 12 seems to have failed.

T cell means Expanded T cell and PBMC means thawed autologous PBMC.

In my opinion, too high a concentration of cytokine may be the cause of background singal, or the use of BSA and FBS supplement media without using Human AB serum may be the cause.

All the conclusions of personalized medicine go through AI applications on enormous masses of biomedical information. Molecular data play a crucial role in obtaining metabolic models to be used for patient analysis. Data relating to proteins and their functions in almost all cases have to do with protein forms that have undergone PTMs (Post-translational modifications). We are speaking about 100,000 PTMs or so, for about 20.000 – 25,000 protein-coding genes. These numbers point to an estimate in humans of around 6 million protein species, that is, the human proteome. Obviously they are not all present at the same time but perform their function in different spatiotemporal contexts.

PTMs of proteins change the protein structure, its chemical-physical characteristics and makes possible new functions with specific molecular partners. The response of the modified protein to the environment also changes, because we are dealing with a new molecular form, with new properties. In a nutshell with a new molecule. From the number and types of potential sites for PTMs on a protein, it is possible to calculate how many molecular forms a single protein can produce. For example, 4 phosphorylation sites on a protein are enough to have 15 distinct combinations for 15 different molecular forms. In cell, each molecular form is generated by the specific space-time context in which it occurs, because only, and only in that cell context, it can exist with its specific functional role. So, when we want to analyze a molecular form experimentally, we should simulate as much as possible the metabolic context in which we think that function should take place, or in vivo studies we should extract and purify the protein from the tissue. Without context, we have inappropriate results on the molecular form because it is not identifiable in space and time. Thus the context should be explicitly reported in papers. Unfortunately, this is a very rare information. What commonly happens is that these data without spatio-temporal context flow into the databases and are used for network analysis, where we find them all collapsed on the native protein. This generates static metabolic models and most of the analyzes are therefore flawed with the possibility that the models used for personalized medicine may be wrong, with possible damage to patients. Another problem then arises, how to eliminate these errors from biomedical Big-data systems? A fundamental rule of Big-data systems is that in order to have reliable results the data must be characterized by a high index of Veracity. Today, this is not true.

What do supporters of personalized medicine think about?

For our postdoctoral project, me and a colleague want to isolate circulating tumour cells (CTC) from the blood of cancer patients. Following the isolation, we aim to culture these CTCs for use in genetic sequencing, analysing exosomes and to test different cytotoxic chemotherapy agents to assess whether these patients have treatment resistance. We were wondering if there are labs that routinely perform CTC isolation that are open to collaborative visits or that are willing to provide brief training opportunities?

Imagine the following scenario where 100 people walking into a clinic, each one of them presenting with a variety of neurological symptoms. Some of these symptoms are similar, some are different and a couple of these patients have kids. The doctors must employ their knowledge, expertise, experience and maybe use medical imaging and other methods to try to diagnose each of them.

It becomes clear that the possibility of a false positive or false negative is high since different neurological disorders present with similar symptoms or a single disorder presents with variable symptomatology.

What if we could use wearable sensors under a task common for all subjects and extract objective biometrics that characterize the temporal, spatial and dynamical behavior of their neuromotor activity? Then, we no longer have a collection of random symptomatology but objective data in a parameter space that stratifies a random cohort of the population. Different clusters within the population could be used to identify different neurological disorders as well as different subtypes of each disorder. This paves the way towards personalized medicine, since each patient is now a unique point in some parameter space that the clinician can track through time.

Check out our latest article at Springer Nature journal of Scientific Reports from Sensory Motor Integration Lab at Rutgers University (Prof. Elizabeth Barbara Torres) in collaboration with researchers at Stevens Institute of Technology and Columbia University.

Hi to everyone! For those interested, the Laboratory of Biomarkers, Biomolecular targets and personalized medicine in Oncology of the University of Ferrara (Italy) is looking for three different post doc positions. You can find attached the details and contact information. You can also contact me in private for further details. Have a nice afternoon!

I am searching for an aid that helps to find the most relevant intervention studies for a given person's (bio)medical profile and intervention objective.

This aid should apply NLP or ACA techniques to extract from publication resources (e.g. NLM) (a) profile information (inclusion and exclusion criteria) of study participants, (b) type of intervention, (c) effect.

My extensive literature search has not thrown up any existing system that meets this description. I'd be grateful for relevant information, either about such systems or about individuals/groups with the necessary expertise/experience to develop such a system.

Sequencing of human genome at the center of interest in the biomedical field over the past several decades and is now leading toward an era of personalized medicine. During this time, DNA sequencing methods have evolved from the labor intensive slab gel electrophoresis, through automated multicapillary electrophoresis systems.

It is utmost importance to understand the microbiome profile associated with genetic disorders. I studied the composition and function of microbiome profile associated with OSCC tissues compared with FEP controls. In disease with well established aietiology involving genetics and epigenetics factors,microbiome may or may not play a causative role. Nevertheless, they can adopt to the tumour microenviroment while modifying it. Furthermore, their metabolic pathways contribute to increase the inflammation in tumour micro environment by changing the composition to dysbiotic state.That means microbiome profile may influence the prognosis of OSCC. Thus, animal experiments are useful to study all ecological theories in any eco system such symbiosis,dysbiosis, cooperate evolution and competitive exclusion. Of course, CF associated will be able to use for therapeutic purposes (microbiomics) in the era of personalized medicine.

I would like to read all updates on this project.

Dear Scientist, I wish you all the best!

Could you please share your approach and experience if you are using any platform for personalized cancer treatment?

Hi,

I am currently conducting a systematic review in the field of pharmacogenomics. In your opinion, which one is the most appropriate criteria, q-Genie or STREGA? What are their advantage and disadvantage? Other suggestions besides those two criteria are very welcomed.

Link (both are open-access):

q-Genie: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431044/ 

STREGA: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1000022

Thank you for your response.

Hello. I am applying for the position of PDF at NRC for the project you are leading "Reprogramming Cell Death in CART-T Cell".

I have a background in cell therapies and manipulation of primary immune cells. Can you please send me some more info on the project, I want to make sure that i am a good fit for your group.

Thank you

Branka

Recently genomics have made unprecedented inroads into cancer diagnostics. With the cost of DNA/RNA sequencing coming down significantly in the last few years and the next-generation-sequencing (NGS) bringing speed, accuracy & reliability, we are certainly in the SCIENTIFIC BREAKTHROUGH ERA! Scientists are developing "DNA/RNA CHIPS" or some Biotechs are already in beta-testing for diagnostics/prognostics/drug discovery kits, which intend to reduce timelines and will be less resource-intensive. These kits are now on the move from LAB-TO-CLINIC-TO-OR (Operating room)......We are witnessing a REVOLUTION IN PERSONALIZED MEDICINE, where ultimate beneficiary will be THE PATIENT! Obviously, in the right scenario, TARGETED THERAPY for the SPECIFIC STAGE(S) OF TUMOR DEVELOPMENT, instead of relying primarily on PHENOTYPIC PATHOLOGY? In addition, portable instruments reading RNA/DNA CHIPS will provide accurate diagnostics at POINT-OF-CARE and hopefully, will make the life of pathologist/oncologist/physician/scientist less stressful......Your thoughts-please.

Hi,

I found a post on RG from 2014 regarding this system but I wondered if there had been any improvements in the technology since.

The general consensus in 2014 was that the system was not fit for purpose. How are people fairing with it now?

cheers,

David.

As example: Drug A (Km=2uM), drug B (Km=45 uM). If we do inhibition or DDI study with known inhibitor and found IC50 for drug A is higher than drug B. what will be the explanation of the result (IC50 or Ki) for two drugs (A and B)?

A recent report in Nature (Dec. 15, 2016) has identified 17 unique gene expression signatures for AML in leukemic stem cells, which could be useful in predicting “targeted therapies” and has the potential to become valuable prognostics indicators? How we use this info for extrapolating these (and other) differentially expressed genomic profiles for solid tissue cancers? Do we expect to see altogether different gene expression signatures in solid cancer's stem cells? How these differentially expressed individual genes (and their corresponding proteins) could help us find better biomarkers for early diagnosis and reliable prognosis prediction of individual patient's response to therapy. In the immediate near future, scientists and clinicians might be able to “design personalized medicine” strategies against key “driver genes” with the ultimate hope of making cancer “a manageable disease”. Furthermore, how we may use this info for futuristic and personalized new drug discovery research tool is certainly optimistic, and will lead to designing "magic bullets" against various cancers, which we have been hoping for a while now........

It should  determine an urgent restructuring  of medical education ,clinical method and research  for impeding medical education based on wrong and reductionist principles or is there a general and spread ignorance about the importance of epistemology for medicine ? Do you believe  that there is a general ignorance about the meaning of the word "epistemology" “ethics” and generally of philosophy ( eg the concept of “person”- “interactionism”-“teleology” )  and the  human sciences  (eg Psychoanalysis) relations with Medicine, resulting  in wrong  curricula oriented only to bio-technology  or to teach an obsolete and partially wrong clinical method.

Do you believe that there is a general ignorance about the change of the definition of “Health” ?

There is the problem to develop Medicine and health science on “Truth” and not on opinions or plagiaristic surrogates ( Personalized medicine) well financed by the health “stock market” not interested to the people health and their freedom to be healthy and “human” persons.

There is the problem also of a general ignorance of politicians, only interested to warrant electoral or religious consensus and their well financed power or of investigators and clinicians sold to the good bidder/ vendu au meilleur offrant/ le vendidos al más buen ofreciente/ verkauft an den guten Bewerber/ بيعت الى مزايد جيدة/售出的良好的竞标者!/ проданный хорошему претенденту/ מכור למציע ההצעה הטובה

 Question published also on the blog www. personcenteredmedicineblog.wordpress.com

We (at least GenXPro) can now predict which cancer drug will work best for a particular patient. Why are so many people still treated following protocols that work only in 25% of cases?

I am researching the notion of personalized medicine and public responses to it in different jurisdictions.

Science has provided us with countless discoveries thought to potentially improve cancer outcomes. However, only a handful of them have been translated into clinical care, and at a quite prohibitive price tag (eg new generation TKIs, monoclonal antibodies, genomic testing etc). Some other, more cost effective, are yet to be fully adopted by health care providers. Among the later I would count maximizing use of metformin in patients diagnosed with type 2 diabetes and cancer. While use of metformin as an anti-tumor agent is currently tested in a concerning high (cost-wise) number of clinical trials, maximizing its benefit among patients with type 2 diabetes is yet to be a focus despite the drug being the first line therapy.

What are your thoughts?

There has been a market explosion of smart wearable sensors. The sales are predicted to raise considerably in the next 5 years and several large companies are adopting the technologies to further develop the products. The New York Times has published several pieces in their Science section pointing out the inaccuracies of the devices. Yet it is thought that these gadgets will disrupt clinical research, see ongoing discussion here

I think that using proper statistical algorithms in these devices will bring us closer to truly personalized medicine. Yet as it stands today they do not seem accurate enough and seem to be using inadequate statistics for individualized assessment. I think that the Researchgate community could actually make a difference and open a platform for data standardization, exchange and analyses with the potential to change the diagnosis, tracking and treatment methods for various disorders of the nervous systems.

A physician wants to initiate a specific medication according to the patient's genotype. All he needs is a drop of patient's blood on a microchip and in a few minutes the snp of the the patient and the suitable medication is displayed on the screen [lab on chip]

We know that 99.9% of humans share the same gene pools and therefore the targeted difference, i.e. pre-test probability is less than 0.1%. Therefore, by applying the Bayesian theorem, an acceptable healthy person's margin of error for false positive result is 1/100, the accuracy of the genomic testing should be near absolute . But what is it in reality? Can someone give me the number?

With the growing number of biomarkers regularly discovered, going through clinical trials and more slowly translating into the clinic, what options are available to shortlist and find biomarkers? For example for:

- Developing new diagnostics/drugs

- Researchers seeking to make new biomarker discoveries 

- Selecting biomarkers for disease diagnosis/treatment 

- other ways biomarkers are used/needed for projects

I am looking for publications or ongoing projects involved with the development of personalized medicine enabled by nanotechnology. In particular I am interested in applications that can be expected to Reach the market within 5-10 years.

I was going through a couple of studies where the spectrum of cancer cells within a single biopsy site were shown to be heterogeneous having different expression of genes and showing non-Darwinian evolution. What are the options of treating patients using personalized medicine if we have to take into account each and every single cancer cell throughout the body which, downs the argument in favor of personalized therapy.

We are working on a new wiki for precision cancer medicine. If you are working in the field, please let me know what you think (registration is required):