CD4 T Follicular Helper Cells and HIV Infection: Friends or Enemies?
Bibliographic record
Abstract
Follicular T helper (Tfh) cells, a subset of CD4 T lymphocytes, are essential for memory B cell activation, survival, and differentiation and assist B cells in the production of antigen-specific antibodies. Work performed in recent years pointed out the importance of Tfh cells in the context of HIV and SIV infections. The importance of tissue distribution of Tfh is also an important point since their frequency differs between peripheral blood and lymph nodes compared to the spleen, the primary organ for B cell activation, and differentiation. Our recent observations indicated an early and profound loss of splenic Tfh cells. The role of transcriptional activator and repressor factors that control Tfh differentiation is also discussed in the context of HIV/SIV infection. Because Tfh cells are important for B cell differentiation and antibody production, accelerating the Tfh responses early during HIV/SIV infection could be promising as novel immunotherapeutic approach or alternative vaccine strategies. However, because Tfh cells are infected during the HIV/SIV infection and represent a reservoir, this may interfere with HIV vaccine strategy. Thus, Tfh represent the good and bad guys during HIV infection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".