Q1: Is there a website where we can check whether a virus is a DNA or RNA virus, and beyond?
A1: You can check out this website: International Committee on Taxonomy of Viruses: ICTV.
Q2: How to define a successful pathogen?
A2:
Although the definition of a successful pathogen can be uncertain and controversial, in recent years, this term has been used to define those microorganisms that manage to establish a successful relationship with their host, which guarantees its transmissibility and its permanence on Earth. Thus, the ability of a microorganism to rapidly adapt to changes in the microenvironment through the deployment of specialized strategies, and survive within its host despite the deadly attack of the innate and adaptive immune response seems to be the key leading to evolutionary success.
C. glabrata (光滑念珠菌，真菌) is a successful pathogen and an artist manipulating the immune response like M. tuberculosis (结核分枝杆菌，细菌).
Microbiol Res. 2022. PMID: 35430489
Then, how to define a successful person?
Q3: How long can Mycobacterium tuberculosis “hold its breath”?
A3:
For example, Corper and Cohn, showed that sealed Mtb cultures held at 37°C for 12-30 years yielded viable bacilli in their anaerobic sediments.
Q4: How long ago was the first case of tuberculosis (TB) discovered?
A4:
Tuberculosis has been a scourge of humanity for millennia: the oldest known cases are in a pair of 9,000-year-old skeletons.
Q5: What are the historical vaccination policies for the BCG tuberculosis vaccine in various countries?
A5: Visit THE BCG WORLD ATLAS.
Q6: What are the differences between QuPath and ImageJ (in brief)?
A6:
There’s a lot that QuPath can do that ImageJ can’t, especially when it comes to working with whole slide images and object classification. But it works the other way too: there’s also a lot that’s possible with ImageJ, but which isn’t possible with QuPath alone.
Fortunately, you don’t have to choose one or the other. They are both open and can be used together.
Q7: How do we know we can trust the bioimage analysis results?
A7:
Excellent question - stay skeptical!
Image analysis lets you generate numbers, but these can be sensitive to how precisely you do the analysis (with or without machine learning). QuPath gives you the tools to make the measurements and to visualize them, but the meaning and validation is up to the user.
This is why image analysis results (from any software) should be carefully validated and treated with caution. Doing this is challenging because the ‘truth’ can be hard to define. One way is to compare with expert evaluation. Another (in some cases) is to use clinical outcome. Both these have limitations; validation is almost always hard – but crucial.
There is also the possibility of bugs in any software. If you find any discrepancies, please report them so they can be investigated and fixed if necessary.
Q8: What is the relationship between image analysis & objectivity?
A8:
One of the claimed advantages of image analysis over visual assessment is that it is objective.
I hope this tutorials helps demonstrate why this is completely wrong!
Even for this very simple example, we need to make decisions:
- At what resolution will we perform our detection?
- What should be included in the tissue? Do we want areas of fat or not?
- How to we define our stain colors?
- What thresholds do we use?
All of these can impact the results. Different people may (subjectively) make different decisions, and the results of their analysis will differ accordingly.
QuPath does not answer these questions for you, but gives you the tools for you to construct your own analysis pipelines.
However, while it is important to be aware that image analysis is not objective, this does not make it useless. Used cautiously – and with careful validation – image analysis may be faster, more reproducible, and more quantitative than visual assessment.
Q9: How are the Nobel Prize laureates selected?
A9:
Thousands of members of academies, university professors, scientists, previous laureates, members of parliamentary assemblies and others, submit nominations for the Nobel Prizes every year. These nominators have been chosen to ensure that as many countries and universities as possible are represented over time. After a long selection process, the Nobel Prize awarding institutions finally make their choice and announce them in October.
Q10: What endogenous molecules in human tissue could exhibit autofluorescence?
A10:
Some human endogenous pigments that might exhibit fluorescence and thus might interfere with fluorescence-based imaging:
- lipofuscin (脂褐质).
- nicotinamide adenine dinucleotide (NAD, 烟酰胺腺嘌呤二核苷酸).
- flavins (黄素).
- hemoglobin (血红蛋白) and its breakdown products.
- bisretinoids (双视黄醇类).
Q11: What chemical agents are widely exployed to mitigate the issue of autofluorescence?
A11:
Among the chemicals used are copper sulfate (硫酸铜), ammonium chloride (氯化铵), Sudan Black B (苏丹黑B), and sodium borohydride (钠硼氢化物)…the chemical modification of the tissue can obscure true signals or compromise tissue integrity.
Q12: What is photobleaching?
A12:
Photobleaching is an irreversible process in which fluorophores, upon repeated absorption of excitation photons, undergo chemical modifications that result in a permanent loss of their fluorescence.
Q13: For general microscopy image analysis, what software platforms are available?
A13:
Several software platforms support general microscopy image analysis - including open-source tools such as QuPath, CellProfiler, Fiji, and Napari, as well as commercial packages like HALO (Indica Labs, Albuquerque, NM, United States) and Visiopharm (Hørsholm, Denmark).
Q14: What is clonal hematopoiesis of indeterminate potential (CHIP)?
A14:
CHIP is the presence of a clonally expanded hematopoietic stem cell caused by a leukemogenic mutation in individuals without evidence of hematologic malignancy, dysplasia, or cytopenia.
CHIP is associated with a 0.5-1.0% risk per year of leukemia. Remarkably, it confers a two-fold increase in cardiovascular risk independent of traditional risk factors.
Roughly 80% of patients with CHIP have mutations in epigenetic regulators DNMT3A, TET2, ASXL1, DNA damage repair genes PPM1D, TP53, the regulatory tyrosine kinase JAK2, or mRNA spliceosome components SF3B1, and SRSF2.
Citation
@online{wu2026,
author = {Wu, Kang},
title = {The Biological-Relevant Questions},
date = {2026-02-01},
url = {https://iamhealthy.github.io/nutrition/the_biological-relevant_questions/},
langid = {en}
}