A Short-Term Assessment of Nascent HIV-1 Transmission Clusters Among Newly Diagnosed Individuals Using Envelope Sequence-Based Phylogenetic Analyses
Bibliographic record
Abstract
The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of ≤10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen's kappa (κ) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04–0.66), p = .002 and OR: 0.17, 95% CI (0.10–0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".