Drug-selective pressure effect on HIV integrase mutations in antiretroviral naïve and experienced patients
Bibliographic record
Abstract
Introduction:Resistance to antiretroviral drugs is a serious problem often related to selective druginduced pressure and sub-optimal drug dosing.This study aimed to investigate drug resistance-associated mutations in human immunodeficiency virus type 1 (HIV-1) integrase gene caused by the drug pressure of reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs).Material and methods: For this purpose, RNA of 50 HIV-infected patients (25 drug-naïve patients and 25 patients under antiretroviral therapy [INI naive]) was extracted and one step RT-nested PCR was carried out on HIV integrase (IN).Then, gene sequences were analyzed to determine sub-types and antiretroviral resistance-associated mutations (RAMs).Results: Phylogenetic analysis revealed that recombinant sub-type CRF35-AD was the most prevalent in all patients (87.2%), followed by A1 sub-type (12.8%).Among the 25 ART-experienced patients, two mutations (N155I, G163R) associated with resistance to integrase inhibitors (INI) were found.Among the 25 naïve patients, several polymorphisms were observed, which was also lower in this group than in the ART group. Conclusions:The results of this study indicated that the integrase mutations can be caused by the effect of selective pressure induced by antiviral agents, such as RTIs and PIs.Therefore, examination of the integrase drug resistance mutations is recommended before starting treatment in Iran.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".