THE EFFECTS OF DOLUTEGRAVIR-BASED THERAPY ON IN VIVO OXIDATIVE STRESS USING A PREGNANCY MODEL
Bibliographic record
Abstract
Dolutegravir-based therapy (DTG) is the preferred first-line regimen for all people living with HIV. However, in 2018 the Tsepamo study in Botswana reported a higher incidence of neural tube defects (NTDs) in women on DTG-based therapy from conception. With additional follow-up the original signal has since weaned. What caused the original NTD signal remains unknown and the discrepancy of findings surrounding the mechanisms that may have led to this signal has motivated the characterization of DTG’s effects on maternal and fetal metabolic pathways. As of current, no studies exploring the impact of DTG on oxidative stress during pregnancy in vivo have been conducted. We aimed to characterize the effects of DTG on oxidative stress and as a possible contributor to congenital anomalies and the observed incidence of NTDs reported in 2018. The first subset of studies investigated oxygen-dependent enzymes in response to DTG-based therapy in placental and embryonic tissue among two gestational timepoints reflective of neural tube closure. The second subset of studies explored oxygen-dependent transcription factors and associated growth factors that may be the cause of the altered expression found in oxygen-dependent enzymes. We found changes in the expression of oxygen-dependent enzymes and transcription factors suggesting the possibility of an oxidative stress state. Alterations to the expression of these enzymes and factors, suggests that DTG readily alters Reactive Oxygen Species (ROS) pathways and may be one of the possible mechanisms to induce NTDs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".