HIV-1 drug resistance surveillance using dried whole blood spots
Bibliographic record
Abstract
BACKGROUND: Field-friendly methods for HIV drug resistance (HIVDR) surveillance in resource-limited regions are urgently needed. Despite evidence that dried blood spots (DBS) are suitable for HIV serology, viral load and CD4+ T-cell enumeration, no study has evaluated DBS for HIVDR genotyping. We assessed the feasibility of genotyping HIV-1 from field-collected DBS stored under challenging environmental conditions. METHODS: We prospectively collected specimens from newly diagnosed, treatment-naive HIV-positive subjects in Mexico. Whole blood was spotted onto filter cards, air dried at ambient temperature and stored with desiccant at 37 degrees C and 85% humidity for 3 months. Genotypes obtained from DBS-extracted nucleic acids using an in-house nested reverse transcription-PCR method were compared to genotypes derived from matched plasma. RESULTS: Genotypes from 103 phylogenetically matched plasma and DBS were compared. In total, 90.1% of all DBS specimens could be amplified in either the region of HIV protease or the region of reverse transcriptase. Failure to amplify from DBS did not correlate with low plasma viral loads. Between paired specimens, the median nucleotide similarity was 99.95%. In the nine specimens with drug resistance mutations, all differences between pairs were partial discordances. Mutations identified in plasma were found in the majority of replicate DBS amplifications. CONCLUSION: The results suggest that genotypes obtained from DBS are equivalent to those from plasma. DBS are a promising public health tool for HIVDR surveillance of treatment-naive subjects, especially in regions where specimens might be exposed to severe environmental conditions and where logistical difficulties could prevent timely specimen processing. More studies are needed to validate DBS for patient monitoring.
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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 | 0.001 |
| 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) | 0.001 | 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".