Influence of Common Mucosal Co‐Factors on HIV Infection in the Female Genital Tract
Bibliographic record
Abstract
Women constitute almost half of HIV-infected population globally, and the female genital tract (FGT) accounts for approximately 40% of all new HIV infections worldwide. The FGT is composed of upper and lower parts, distinct in their morphological and functional characteristics. Co-factors in the genital microenvironment, such as presence of hormones, semen, and other sexually transmitted infections, can facilitate or deter HIV infection and play a critical role in determining susceptibility to HIV. In this review, we examine some of these co-factors and their potential influence. Presence of physical and chemical barriers such as epithelial tight junctions, mucus, and anti-microbial peptides can actively block and inhibit viral replication, presenting a significant deterrent to HIV. Upon exposure, HIV and other pathogens first encounter the genital epithelium: cells that express a wide repertoire of pattern recognition receptors that can recognize and directly initiate innate immune responses. These and other interactions in the genital tract can lead to direct and indirect inflammation and enhance the number of local target cells, immune activation, and microbial translocation, all of which promote HIV infection and replication. Better understanding of the dynamics of HIV transmission in the female genital tract would be invaluable for improving the design of prophylactic strategies against HIV.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".