The role of Trappin-2 and RANTES in mediating resistance to HIV-1 infection
Bibliographic record
Abstract
There are currently more than 33 million people worldwide who are infected with HIV-1 despite development of novel treatments and knowledge of prevention strategies. Within the Pumwani area of Nairobi, Kenya there is a group of commercial sex workers who are highly exposed to HIV-1. A small subset of these women have been classified as resistant to HIV-1 infection as they remain HIV un-infected despite as many as 60 unprotected sexual exposures to HIV each year. A better understanding of such a natural model of HIV resistance would be invaluable to inform the development of a protective HIV vaccine or microbicide.\nGlobally, heterosexual transmission of HIV across mucosal surfaces is responsible for the bulk of new infections and thus it is important to examine both the macro and the micro environments of the vaginal mucosa in efforts to determine what enhances and what thwarts HIV-infection. Previous studies have shown elevated levels of RANTES, a natural ligand for the dominant HIV co-receptor CCR5, in cervicovaginal secretions of HIV-resistant women. Additionally, a novel HIV-inhibitor, Trappin-2 was previously shown to be elevated in cervicovaginal secretions of HIV-resistant women. To test the hypothesis that RANTES and Trappin-2 in cervicovaginal fluid are important mediators of HIV resistance we will: 1) measure RANTES in a much larger group of women from the Pumwani cohort, and 2) measure Trappin-2 levels in samples taken at different time points, and 3) correlate Trappin-2 levels in cervicovaginal fluid with biological confounding variables, and 4) investigate whether SDF-1 plays a role in HIV-disease progression in HIV-positive women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".