Questions related to Biomarkers
I am a post graduate student doing Masters of Dental Surgery and I am doing a study related to Biomarkers and primary teeth.
I am a post graduate student doing Masters of Dental Surgery and I am doing a study related to Biomarkers and primary teeth.
I would love to receive some recommendations from experts in regards to the topic, whether there are valid findings in research on biological markers for anxiety disorders. I am trying to gain some stable insight and be able to argue in favor of the notion, that no anxiety disorder "comes from a malfunction/sickness of the brain".
Thank you in advance!
Hi, I am currently conducting a survival study to investigate the role of several potential biomarkers as prognostic factors in certain cancer. First, I perform Kaplan-Meier analysis for all the biomarkers and other relevant clinicopathologic data. However, only one biomarker fulfilled the proportional hazard criteria from the Kaplan-Meier curve. Other biomarkers and clinicopathologic variables do not fulfill the criteria.
I am wondering, do I still need to proceed to Cox Regression analysis? Can I include the other biomarkers and relevant clinicopathologic data in Cox Regression, even though they do not fulfill proportional hazard criteria during Kaplan-Meier analysis? Thank you.
For a biomarker study in neurodegeneration, we are searching for publicly available data of unique molecular identifier (UMI) counts of microRNAs in plasma from non-neurodegeneration individuals.
Do you have these data by any chance or can you refer me to a database where I can find such data?
My question is, is there a way to know what refractive index is equivalent to the specific concentration of each biomarker (for example, the beta-amyloid biomarker)? Because my goal is to study the refractive index of a sensor, and it is necessary to define changes in the measurement environment based on the refractive index
Thank you for your help
What laboratory-grade permanent marker is your favorite and why? Which is the most resistant to: 1)Alcohol
and is there one pen to best them all?
Only (ultra)fine-tip options need apply)
Tell me your experiences.
Are there any biomarkers with prognostic value in patients with oral squamous cell carcinoma undergoing curative surgery or chemoradiotherapy?
I am doing a study on the efficacy of cotinine as a biomarker for patients who are going to receive implants / could you send me the related articles?
I have determined a number of parameters as the response of a particular treatment. Now I would like to find out the best one which can explain the treatment effect. Which statistical method shall I use to find it out?
Whats the best way to measure/evaluate changes in biomarkers before and after an infection. How could I test statistical difference if:
1. I keep the biomarker as continuius variable (for eg BP reading). Is paired t-test or ANOVA best suited? Or a better way to handle it statistically?
2. If I make BP as a categorical variable, for instance SBP<120 is normal and otherwise is not. With outcome also as a categorical variable that is whether covid infected or not. Then what would be the most suited statistical methods in this case?
I am compiling a list of diseases that have objective biomarker (e.g. blood test result) or an indicator (e.g. inability to walk) that will enable us to track the disease progression.
Would value your help for:
a) Disease name
b) Name of biomarker or indicator
These should be biomarkers or indicators that are used in common practice.
Especially in the field of psychiatry hesitation exist sometimes in the use of laboratory biomarkers like GGT, CDT or PEth for diagnosis of chronic abuse.
The use of questionnaires like CAGE or AUDIT is very usefull in my opinion, but its use demands full and honest cooperation of the patient / client. Questionnaires or a structured interview like MATE are needed to describe the situation and they could give a rough estimate of the degree of abuse.
Biomarkers alone are not enough, since the response to alcohol depends (partly for some markers) on individual factors like gender, age, previous use etc. But biomarkers cannot hide alcohol (ab)use, although they do not tell always the truth: sensitivity and specificity are never perfect.
What is your opinion and experience in this field? What is your choice ? Do you know any professional Guideline that might be helpfull?
I am currently working on developing a fast/cheap assay of a certain protein in the blood that happens to be an excellent biomarker for a variety of diseases. I plan to be able to run my assay using a single drop of blood. I am running into issues because catalase is interfering with the reaction, and I need a quick and cheap way to separate catalase from this protein of interest. Right now I am considering "salting out" the catalase with ammonium sulfate and running my assay using the resulting filtrate, however, if even a small amount of catalase leaks through, it will disrupt the assay. Can the total elimination of catalase from the sample be accomplished with the simple addition of the correct molarity of ammonium sulfate and filtration using filter paper? Or are more advanced filtration techniques needed?
I am also open to other suggestions for how to neutralize/remove catalase, I have tried numerous chemical inhibitors/denaturants (most interfere with the reaction), size exclusion chromatography (too expensive given how similar in size these proteins are), and changing the pH of the assay solution (both proteins work optimally at similar pH values).
Thank you for your time!
I am working on looking for a biomarker for Alzahimer diseases.
When I compared the results for treatment cells to basal in AD group and treated cells from healthy group vs basal, I see a significant difference. (Basal vs treated in the same group).
But when I compared the treatment cells from AD with treated cells from healthy group, I see no differences between them ( treated cells from AD vs healthy group).
Could I say that my treatment could be a biomarker because the cells from healthy group almost reach the level of diseases patients? also, because there is significant difference when compared to basal in the same group?
Or I cannot say it because it should be a significant difference between healthy and diseases patients in treated cells if I am looking for a biomarker?
If I cannot say my treatment could be a biomarker, what is the possible way to explain my results when these is no differences?
I hope you understand my question.
Thank you so much for your help.
Hello, everyone. I obtained DEGs from RNAseq analysis for normal and infected samples. Then I decreased the number of them by some downstream analysis. Now I have 120 DEGs, and I want to select between them the best combination of biomarkers that can recognize normal from infected samples (biomarker panel). So I want to use machine learning methods (At first, I want to perform feature selection and then draw ROC curve, count MCC, Spe, Sen, ....for the combined set of selected biomarkers by different algorithms such as the neural network and random forest). Because I don't have experience in machine learning, I have some questions. And please let me know if you think I am doing any steps that explain here wrong!
1- What kind of RNAseq files should I enter into machine learning software? count file, FPKM, tpm, or any other files?
2- Should that be normalized?
3- Should the entry be log2 transformed?
4- Can the training and discovery dataset be the same?
5- Is what I write below a correct study design?: The use of a dataset for obtaining DEGs then, partitioning it into k subsets of equal size. Of the k subsets, a single subset is retained as the test data set. The remaining k - 1 subset is used as training data sets. The cross-validation process is then repeated k times, with each of the k subsets used exactly once as the test data. The k results from the k iterations are averaged (or otherwise combined) to produce a single estimation. And then performing a test for the model with an external dataset to validate the model.
6- Can the validation dataset be from a different technology like microarray? Is any pre-processing needed for the datasets to be tuned before performing machine learning methods in this case?
Thank you to answer my questions
I want to know how can I combine multiple predictors in one ROC curve.
I want to know whether AUC increased if I used two predictors instead of using each one separately.
I.e Alfapheto protein, carcinoembryonic antigen, and both in the diagnosis of GIT malignancy.
suppose AUC for the first biomarker is 0.7, for the second is 0.75 what is the AUC if I used both?
As we know that the sperms function test is widely used for the management of male factor issues worldwide. However, similar parallel test is not available for the management of egg factor issue in female. Do the number of oocytes available for the purpose or the unavailability of the potential biomarkers for oocyte quality and other factors limit the development of oocyte function test? Kindly give your expert opinion.
There are many markers for ferroptosis as listed in the link below:
And different literature probes different set of biomarkers. Some markers (NRF2, FTH1, ACSL4, SLC7A11, etc.) were examined in some literatures while they were not in others. I would like to detect ferroptosis efficiently because budgets are limited for primary antibodies for detection of ferroptosis using Western blot. Guys, is there any suggestion on narrowing the to-blot list? I guess it needs taking into consideration what ferroptotic sub-pathway my research subject is involved. Maybe some preliminary experiments such as RNA-seq can help me out to determine the sets of markers I will be blotting?
Your help is appreciated!
Early detection of a number of diseases is possible through the development of very highly sensitive senors. This will have a large impact on the reduction of treatment costs since it will allow early diagnosis of patients. However, there are currently a number of challenges which have to be met in order to realize those goals. Listing the main challenges is the first step in the right direction.
We will conduct a big study for finding different biomarkers for depression. We will combine different modalities (fMRI, MRI, microbiome, genomics etc) in order to explore possible biomarkers (and combination of information between different modalities)
Is it possible to compute a power sample since the variables and combination of them are multiple (actually thousands) and there is not a specific biomarker that we want to test
what are the reasons that make the result of serum biomarkers not closely related (with vast differences) between studies articles around the world in healthy control groups....
although the same unit and same protocol and method principle...
let the difference in race , procedure,, analytical instrument,, a company of kit
make the logic difference
but not related value for what reason can give in your opinion?
I want to see whether a biomarker level at baseline can be used to predict the prognosis after a treatment alone as compared to a clinical parameter?
Which statistical model will be best to investigate it?
I am proposing the cohort that will search for a predictive biomarker for treatment response. I used the formula using 95% CI and 0.05% error. And I used p as the response rate from the previous study. Any suggestions?
In the literature for immunohistochemical detection of NETs (Neutrophil Extracellular Traps) are cited many biomarkers: neutrophil elastase, myeloperoxidase etc. Which combination do you think would be most indicative? And what antibodies would you recommend?
I am currently doing a project related to cryo preservation and I need to ensure that the mitochondria in the cells preserved using my method stays intact (i.e., the mitochondrial integrity remains the same). Is it okay for me to use the presence and/or the quantity of the membrane surface proteins of these cells as biomarkers to determine whether the mitochondria inside these cells are intact?
the question in another way
can give me an example of any biomarker correlated with itself in level; when measuring from different sites in the body
like level of calcium in bone, serum, and CSF?
can this apply to all biomarkers like
CTLA4 in tissue by IHC
CTLA4 in flowcytometry
are the three-level from the same gene expression and have a linear correlation between them?
or anyone can reflect the level of others?
I have a bottle of plasma sample and I want to characterize whether it is from mouse. I'm considering to detect some plasma biomarker but I'm not sure what can be used as mouse plasma biomarker. Could you give me some advice?
In some crude oils or source rocks reported in previous publications, it is sometimes mentioned that the organic matter exhibite an pararent contribution of bacterial reworking of terrestrial organic matterial. So, which biomarkers are typical compounds resulting from bacterial reworking of terrestrial organic material ? In addition, which parameters can qualitatively characterize the degree of bacterial reworking ?
I have a study which analyzes the correlation between a biomarker and outcomes. In my protocol I specified a specific time point (i.e early = < 3 days).
My question is, what if a certain study analyzed two time points for the same biomarker and same outcome, but both time points are within the specified time point (example: biomarker measured with 1 day and 3 days)?
Should I choose one time point, and exclude the other one?
Now is the time to begin thinking of biomarkers of sentience and consciousness. The desiderata of a biomarker are:
"The ideal biomarker would be
– Perfectly correlated with the clinical endpoint – Have little to no variability under normal circumstance – Have very good signal to noise ratio – Change quickly and reliably in response to changes in the clinical endpoint.
As such, this ideal state is impossible to find in a complex system such as the human body. But what level of quantitative certainty is required? This again is subjective. It depends on whether it can outperform the alternatives."
Joachim Pimiskern kindly made a list of more studies that try to detect consciousness by evaluating measured data from the brain; for the links, please look for his message below. These findings may qualify as a "neural correlate" of types of conscious processes, or as biomarkers of consciousness in the sense of indicating the existence of conscious experiences.
Our lab has started working with the enteroid derived from the fetal intestinal tissue. I have 2 challenging queries as follows-
1. For mincing the intestine, so far, I have been using a scalpel but still, the mincing is not fine and smooth, rather I get fragmented tissue that results in unsuccessful cell line establishment. How can I mince the biopsied intestine very well to get a single cell line from that?
2. How can I prove that my working tissue is from the intestine i.e., is there any specific biomarker I can detect that will help to prove that the enteroid established from the biopsied tissue is actually fetal intestinal tissue?
I am seeking to understand what are some challenges in the discovery of biomarkers that can be overcome by Mass Spectrometry?
Looking for some markers, that would show interconnection between cognitive decline and age-related muscle loss and lower physical performance in aged rats.
I would also like to know In which part of the brain is it best to identify biomarkers and which biomarkers are best to determine when detecting interconnections between the two conditions?
I am doing research on rats that take omega 3 fatty acids and exercise, looking at the effect of these interventions on aging.
If you have some skills in this area, I would appreciate your help.
I thought it is reseonable to speculate that if one PI3K inhibitor can reduce p-ERK, pAKT or p-S6 more dramatically in cell line A compare to cell line B (A cells were crispr knockouted one gene, and B cells is the control), this drug is more sensitive in A than B, given the fact that the basal level of phospho protein is higher in A and A cell is also grows quicker. Dose anyone has experience on this? is cell proliferation the only way to check drug sensitivity?
I was wondering if anyone had experience in the following:
I would like to translate two separate continuous biomarkers (A and B), that are mechanistically linked to each other, to the same scale. However, I want to avoid using an equation to estimate A from B or B from A.
Both biomarkers are rarely measured in the same cohort and so some cohorts measure biomarker A while others measure biomarker B. This is due to different companies making assays for either A or B and the established relationships between these companies and individual laboratories. Transforming both biomarker levels so that measurements of A or B can be used would help with statistical power and with repeatability of studies.
I was thinking that maybe several cut points can be established for biomarkers A and B (to make high, medium, and low count categories or something), separately, based on each biomarker's association with prognosis so that each can be mapped to the same scoring system. In doing this, I would like to have at least 3 categories.
But I am not sure how to do this and if it is even the best solution.
I hope that all makes sense. Any help will be much appreciated.
Biomarker can be developed for three main purposes (1) diagnostic (to classify as having a disease), (2) prognostic (to make predictions on who will develop a disease), or (3) theranostic (to predict an individual response to a particular therapy).
I am working on identifying biomarkers of the outcome of treatment in a disease. I have 20 potential biomarkers and more than 30 potential confounders. Actually, I know that some of those potential confounders affects treatment outcome. However, before determining the association between each biomarker and the therapeutic outcome, I want to know which of these confounders affects the expression level of each biomarker. I want to understand the impact of covariates on the expression of biomarkers to include them as confounders in multivariate analysis.
I am looking for a biological response of fish exposed to trinitrotoluene or its amino metabolites.
Maybe a more or less specific gene expression could help?
Oxidative stress is one good example but its not specific for nitroaromates.
My current project involves measurements of CA-125 and some of the apparatus we use gives an output in pg/ml whereas the standard measurement unit of concentration in serum for this antigen is u/ml.
u/ml is a unit usually used for measurement of enzymes and for concentration of immunoglobulins. CA-125 is neither of those. It has no enzymatic activity one can measure.
So, how do I convert between the two units of concentration if I want to compare?
Can anyone tell me how to calculate biomarker response index (BRI)? Suppose I have three treatment sets and one control set of plants. I have considered many cytotoxicity, genotoxicity and biochemical endpoints. But now if I want to use BRI to summarize the plants' responses to the toxicant in comparison to the control, how to do it? Please help!
Thanks in advance.
Hi. I'm working with soil enzymes and I have an issue because i saw several papers that used "integrated biological response" or IBRv2 to integrate data from enzymes and build a hexagonal star plots (based on this paper from Beliaeff and Burgeot "Integrated biomarker response: A useful tool for ecological risk assessment"
See some examples:
Is a software (R package? specifical software? excel file?) available to calculate this index and build the hexagonal star plots? Or is only to calculate the data and after this, make the hexagonal star plot (radar chart) in SPSS?
I know the "Biomarker Integration Data Expert System", but this system is more appropriated for worm enzymes than soil enzymes.
Thank you in advance!!!
Currently, I am validating a new machine learning approach. But, I need to find different cancer datasets composed by clinical markers and gene expressions. For now, a just found MMRF data set available at https://research.themmrf.org/.
Can you help me indicating other open data sets like this one?
CA19-9 is a commonly performed tumour marker in pancreatico -biliary malignancy,but the levels keeps varying ? depending on the severity of obstruction causing cholangitis.In these circumstances if the levels are high does it mean it is a disseminated malignancy or it is probably due to cholangitis !
Current genomics tools to to discover new or study current biomarkers of neurodegenerative diseases.
When one says that a certain biomarker is associated with a certain disease "independent of other risk factors", what does this really mean? I think it means that, regardless if a person has that other risk factor or not, that person would still get that disease if the biomarker is positive. Am I correct?
I've recently gotten results for Biomarkers studio through GC-MS, but I'm not sure how interpret it.
I'd like to know if you know any book or booklet where I can find information about how to interpret GC-MS results in Biomarkers studio
Thanks a lot
Several molecules are known as relevant biomarkers for the detection or followup of cancer, however, is difficult to get from research articles which ones are considered as the most useful in a clinical setting. Can you provide a good biomarker and a justification for their usefulness and reliability?
I used DHEA-S (Dehydroepiandesteron Sulphate) in a study as the biomarker of ageing. DHEA-S is hormone secreted from Supra renal gland, gonads and even adipose tissue. DHEA-S is gradually decreasing when age advances.
In addition to this, what are possible biomarkers to measure the intensity of biological ageing process in humans?
I am doing Principle Component Analysis of my proteomics data.
There are two groups, one is healthy control and the other one is patients.
Originally I have 90 biomarkers, and after feeding all the 90 biomarkers for PCA analysis, I have PC1 (23.3%) and PC2 (19.2%).
Then I reduced biomarkers to 23, which I did t-test and found the 23 biomarkers are significanly different between control and patients.
Then after feeding only the 23 biomarkers for PCA analysis, I have PC1 (39.3%) and PC2 (27.9%).
Now my question is, how should I interprate the results?
In the first time, PC1+PC2 is 42.5%; when I reduce the number of biomarkers, PC1+PC2 is 67.2%, so can I draw the conclusion that the two groups are clustered better in the second time than in the first time?
Or, are there any specific number that we could refer to evaluate the quality of a PCA?
When you purify extracellular vesicles, such as exosomes, what are your downstream applications/analyses? I'm especially interested in understanding what your next steps are if you're doing biomarker discover or diagnostic research.
Thanks in advance for your input!
I am planning to conduct p16Ink4a/Cdkn2a immunohistochemstry in cryosections of mouse tissues.
Should you empirically know good antibody for this purpose, your suggestions would be much appreciated, as the quality of the antibody is pivotal to successful staining.
Priority is immunohistochemical staining, and the use for western blotting is optional.
For example CD44 is a stem cell marker, whereas Vimentin is an EMT marker. Mesenchymal cells are called MSCs (Mesenchymal Stem Cells), then why are there different markers for stem cells and mesenchymal cells?
I would like to know whether the BIOENV routine in PRIMER could be used to identify a subset of biological data (gut content items in my case) best explaining environmental data (biomarkers and condition indices in my case).
In my research I just found out 3 microRNA associated with Esophageal Cancer. And I want to prove they are biomarkers for diagnosis or prognosis of Esophageal cancer.
The miRNAs is also a biomarker in the serum. Could i detect the level of miRNAs, and/or targeting the miRNAs in clinical application? Has anybody try to isolate/generate the antibodies to miRNAs from mouse, rabbit or even human subjects?
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'm working on a project to evaluate chemotherapy-induced cognitive impairment with cisplatin administration. I'm planning to use a biomarker, evaluated serially over time. My questions are:
1. What is the single best biomarker for this scenario (i.e. best in terms of sensitivity, specificity, and exclusively pointing to neuronal inflammation and oxidative stress)? I want it to be very specific, displaying aberrant results only when the neurons are injured. Many biomarkers such as interleukin or MDA increases in the event of systemic inflammation, which is almost inevitably happen under this research scenario (i.e. chemo almost always induces systemic inflammation).
2. Should it be derived from CSF or plasma?
Thank you in advance for anyone who can provide me with answers.