Next-Generation Sequencing of Dried Blood Spot Specimens: A Novel Approach to HIV Drug-Resistance Surveillance
Bibliographic record
Abstract
BACKGROUND: HIV drug-resistance (DR) surveillance in resource-limited settings can be performed using dried blood spots (DBS) because of ease of collection, transportation and storage. Analysis of pooled specimens on next-generation sequencing (NGS)-based platforms, such as the 454 pyrosequencing, is an efficient sequencing method for determining HIV DR rates. In this study, we conducted HIV DR surveillance on DBS using NGS and identified minority variants in individual patients. METHODS: A total of 48 extracts of DBS from an HIV DR surveillance study in Mexico City were re-amplified using primers tagged with multiplex identifiers, pooled and pyrosequenced. Consensus sequences were generated for each specimen with mixtures identified at positions where >20% of the reads contained a variant. Individual consensus sequences were then analysed for DR mutations and compared with those derived from Sanger sequencing. RESULTS: DBS analysed with tagged pooled pyrosequencing (TPP) were highly concordant with Sanger sequencing genotypes from matching plasma and DBS (99.21% and 99.51%, respectively). An exception was an M184I mutation only detected with TPP of DBS at a frequency of 20.4%. Multiple specimens had minority variant reads below the 20% mixture threshold. CONCLUSIONS: TPP using DBS is an effective method for HIV DR surveillance. TPP for genotyping results in cost savings of 40% over conventional in-house methods. The effect of low-abundance DR mutations, undetectable by conventional methods, remains to be determined. This technology might be applied to any HIV specimen (plasma/serum) and can also be used for other diagnostic assays where DNA sequencing is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".