Genetic correlates of HIV resistance
Bibliographic record
Abstract
The Human Immunodeficiency Virus 1 (HIV-1) epidemic continues to claim millions of lives, despite intense research and public health programs. A natural model of resistance is crucial for the development of an effective vaccine. We have identified a group of sex workers in Nairobi, Kenya, who appear to be resistant to infection with HIV. Research on this cohort has identified numerous immunological and genetic correlates to HIV resistance, but has failed to completely explain the phenomenon. Genetic studies have shown that HIV resistance occurs in families, with both sex worker and non-sex worker relatives of HIV resistant women less likely to be HIV infected. In addition, HIV resistance has been associated with altered innate immune responses, as measured by cytokine production to toll-like receptor stimuli. To test the hypothesis that there is a genetic component to HIV resistance, we will address two specific objectives within this thesis: 1) identify known polymorphisms associated with HIV resistance in the kindred of these women; more specifically interferon regulatory factor 1 (IRF-1) polymorphisms, and 2) identify polymorphisms within toll-like receptors (TLRs) that may be responsible for the altered and apparently successful immune responses in HIV resistant women. Our findings show an association between HIV resistant kindred and an IRF-1 microsatellite, as well as, with an IRF-1 single nucleotide polymorphism. No associations were found between HIV resistance and the investigated TLR2 and TLR4 polymorphisms. These results also suggest a genetic component to HIV resistance, but do not fully explain the altered immune responses observed within these 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".