Understanding Factors That Modulate HIV Infection at the Female Genital Tract Mucosae for the Rationale Design of Microbicides
Bibliographic record
Abstract
Women are now becoming the pivot of the epidemiological spread of HIV infection worldwide, especially in developing countries. Therefore, research to develop an efficient microbicide is now a priority for the prevention of HIV-1 acquisition in exposed women. However, recent disappointing failures in microbicide clinical trials revealed major gaps in basic and applied knowledge that hinder the development of effective microbicide formulations. Indeed, the inhibitory power of microbicide molecules may be affected by several physiological and immunological factors present in male and female genital tracts. Furthermore, mucosal crossing of HIV-1 to increase the ability to reach the submucosal target cells (macrophages, lymphocytes, and dendritic cells) may be modulated by supraepithelial factors such as seminal complement components (opsonized HIV-1), by epithelial factors released in the submucosal microenvironment such as antimicrobial soluble factors, cytokines, and chemokines, and by potent intraepithelial and submucosal innate immunity. The design of vaginal microbicide formulations should take into account an understanding of the intimate mechanisms involved in the crossing of HIV through the female genital mucosae, in the context of a mixture of both male and female genital fluids.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".