A searchable database of publications using fluorescent probes and flow cytometry to study antigen-specific B cells
Bibliographic record
Abstract
ABSTRACT The study of antigen-specific B cells has resulted in important advances in all fields of immunology, the development of experimentally and/or clinically useful antibodies, and as a starting point for rationally designed vaccine antigens. A key innovation allowing for widespread study of antigen-specific B cells was the development of fluorescent antigen probes for use with flow cytometry. Initially these studies were mostly focused upon B cells specific for a variety of model antigens, but over the past decade focus has shifted towards the study of B cells specific for antigens from pathogens such as SARS-CoV-2, HIV, and Influenza virus. Importantly however, these types of approaches have been used for hundreds of different antigens and could be used for thousands more. Unfortunately, studies of B cells specific for an antigen of interest are not easily searchable on current publication databases since these assays are often a small portion of a larger publication. To overcome this, we built a searchable database of studies analyzing antigen-specific B cells by flow cytometry using fluorescent antigen probes that is located at www.immunology.virginia.edu/Taylor/Bcell/Database.php . Using this database, we assessed the number of publications per year revealing rapid growth in the use of this approach in recent years. While much of this rapid growth was focused upon the assessment of B cells specific for SARS-CoV-2, HIV-1, or Influenza virus, studies assessing B cells specific for hundreds of different antigens derived from numerous microbes, animals, plants, or other sources can be found in the database. Combined, the antigen-specific B cell database was built to facilitate identification of studies assessing these cells and for analysis of the field as a whole. Abstract Figure
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.071 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.036 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".