Genetic analyses of HIV env associated with uveitis in antiretroviral-naive individuals
Bibliographic record
Abstract
OBJECTIVE: To analyze and compare HIV-1 env sequences from the eye to those from the blood of individuals with uveitis attributed to HIV with the goal of gaining insight into the pathogenesis of HIV-associated eye disease. DESIGN: A prospective case series of five HIV-infected antiretroviral-naive individuals with uveitis negative for other pathogens. METHODS: RNA from blood plasma and ocular aqueous humor was reverse transcribed using random hexamers. HIV env C2-V5 (HXB2: 6990-7668) sequences were generated by single-genome amplification using nested polymerase chain reaction followed by bidirectional Sanger sequencing. Sequence analyses by Geneious, Geno2Pheno, N-GLYCOSITE, DIVEIN, and HyPhy evaluated relationships between HIV in plasma and aqueous humor. RESULTS: A median of 20 (range: 13-22) plasma and 15 (range: 9-18) aqueous humor sequences were generated from each individual. The frequencies of sequences with predicted-N-linked-glycosylation sites and C-X-C chemokine receptor type 4 were comparable in aqueous humor and plasma of all five patients. Aqueous humor sequences had lower median genetic diversity compared with plasma across all patients, but similar divergence, in four of five patients. Aqueous humor HIV sequences were compartmentalized from plasma across subjects by Critchlow correlation coefficient, Slatkin and Maddison, nearest-neighbor statistic, and Fixation index. CONCLUSION: Among antiretroviral-naive individuals with uveitis attributed to HIV, the universal compartmentalization and decreased diversity of eye compared with blood sequences suggests time-limited passage of a small subset of variants from each patient's viral population into the eye tissues, followed by limited immune selection despite the inflammatory uveitis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".