High level of primary drug resistance in Mali
Bibliographic record
Abstract
BACKGROUND: As access to antiretroviral drugs increases in developing countries, it will become increasingly important to monitor the emergence of resistance and to define the molecular pathways involved to identify optimal therapeutic regimens. METHODS: We performed genotypic resistance testing on plasma obtained from 101 HIV-infected treatment-naïve individuals from Mali. Genotyping was carried out using the Virco protocols and HXB2 was used as the reference strain. RESULTS: CRF02_AG was the most common subtype, present in 71.3% of our patient population. Other subtypes included B, C, G, CRF06_CPX, CRF09_CPX, CRF01_AE, A2/CRF16_A2D, A1 and CRF13_CPX. A total of 9.9% [95% confidence interval (CI) 6.9-12.9%] of patients had at least one resistance mutation. The prevalences of mutations conferring resistance to nucleoside reverse transcriptase inhibitors (NRTIs), nonnucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs) were 5% (95% CI 0.7-9.2%), 6% (95% CI 1.3-10.6%) and 0%, respectively. The most frequent mutations were T215A/Y for NRTIs and K103N/T for NNRTIs. One patient harboured three NRTI resistance mutations and one NNRTI mutation. This is the first reported case of multi-drug-resistant viral transmission in Mali. Polymorphisms at protease codons 10I/V and 33F potentially associated with resistance were observed in 18.8% and 1% of patients, respectively. Several polymorphisms in the C-terminal domain of reverse transcriptase were observed: A371V (in 63.4% of patients), G335D (76.2%), E399D (10.9%) and G333E (1%). CONCLUSION: Primary resistance was seen in 9.9% of subjects, which is higher than previously reported in Mali. Taking into consideration other polymorphisms in protease such as L10I/V and 33F, primary resistance could reach 28.7% (95% CI 19.9-37.5%). Our study reflects the need to monitor the evolution of resistance on a regular basis and trends of transmitted resistance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".