Mechanisms of T follicular helper (Tfh) impairment in chronic HIV infection
Bibliographic record
Abstract
Abstract Human immunodeficiency virus (HIV) infects millions of people worldwide. In secondary lymphoid organs, site where the anti-HIV immune response is mounted, the virus has been shown to induce a predominant dysregulation and disturbance of the microvenvironment. Previous work in our laboratory has identified the lack of protective anti-HIV antibodies in patients to be due, at least in part, to an inadequate T follicular helper (Tfh) cell help to germinal center (GC) B cells in draining lymph nodes through impaired PD-1/PD-L1 interactions. Since the mechanism of this impairment is not yet fully elucidated, we have used our co-culture assay followed by a unique gene array analysis to screen for key molecular pathways that could be accountable for the difference between helper programs in Tfh cells of HIV positive and negative individuals. The major driver of gene profile clustering was observed within Tfh proliferation programs as well as infection status. Gene set enrichment analysis included repair mechanisms, cell cycle regulation, metabolism, signal transduction and co-stimulation in the top 10 enriched pathways. Careful examination of gene fold changes in proliferating vs non-proliferating Tfh cells between healthy and HIV individuals showed a decrease with HIV, in immune regulatory gene expression such as the transcription factor c-maf and its mediators, responsible for Tfh differentiation, development, survival and performance. Investigating this pathway would contribute to a better understanding of the HIV-mediated dysregulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".