Transmission networks of drug resistance acquired in primary/early stage HIV infection
Bibliographic record
Abstract
OBJECTIVES: Population-based sequencing of primary/recent HIV infections (PHIs) can provide a framework for understanding transmission dynamics of local epidemics. In Quebec, half of PHIs represent clustered transmission events. This study ascertained the cumulative implications of clustering on onward transmission of drug resistance. METHODS: HIV-1 pol sequence datasets were available for all genotyped PHI (<6 months postseroconversion; n = 848 subtype B infections, 1997-2007). Phylogenetic analysis established clustered transmission events, based on maximum likelihood topologies having high bootstrap values (>98%) and short genetic distances. The distributions of resistance to nucleoside and nonnucleoside reverse transcriptase inhibitors and protease inhibitors in unique and clustered transmissions were ascertained. RESULTS: Episodic clustering was observed in half of recent/early stage infections from 1997-2008. Overall, 29 and 28% of new infections segregated into small (<5 PHI/cluster, n = 242/848) and large transmission chains (> or =5 PHI/cluster, n = 239/848), averaging 2.8 +/- 0.1 and 10.3 +/- 1.0 PHI/cluster, respectively. The transmission of nucleoside analogue mutations and 215 resistant variants (T215C/D/I/F/N/S/Y) declined with clustering (7.9 vs. 3.4 vs. 1.2 and 5.8 vs. 1.7 vs. 1.1% for unique, small, and large clustered transmissions, respectively). In contrast, clustering was associated with the increased transmission of viruses harbouring resistance to nonnucleoside reverse transcriptase inhibitors (6.6 vs. 6.0 vs. 15.5%, respectively). CONCLUSION: Clustering in early/PHI stage infection differentially affects transmission of drug resistance to different drug classes. Public health, prevention and diagnostic strategies, targeting PHI, afford a unique opportunity to curb the spread of transmitted drug resistance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".