MétaCan
Menu
Back to cohort
Record W4378803082 · doi:10.32920/23271968

The Impact Of Human Immunodeficiency Virus (HIV) Antiretroviral Drug Exposure In-Utero On Later Fertility

2023· preprint· en· W4378803082 on OpenAlexaff
Saba Zafar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCartIn uteroFertilityOffspringPregnancyBiologyPhysiologyImmunologyMedicineVirologyFetusEnvironmental healthPopulationGenetics

Abstract

fetched live from OpenAlex

Combination antiretroviral therapy (cART) reduces the risk of vertical human immunodeficiency virus (HIV) transmission. Two nucleoside reverse transcriptase inhibitors (NRTIs) are usually included in cART. The efficacy of cART has been extensively studied, but a significant knowledge gap exists concerning the effects of in-utero exposure on developing fetuses. In this project, we assessed the effects of HIV antiretrovirals on the reproductive health of offspring. Using Schizosaccharomyces pombe, we have shown that mutagenicity, but not toxicity, increases over time after dual-NRTI exposure. Our mouse pregnancy model suggests that in-utero cART exposure does not affect female germ cell development but alters the testes structure, thus adversely impacting male germ cell development. However, our study is limited in sample size and we aim to improve this by quantifying more gonad sections. Ultimately, we hope to provide more insight on the potential long-term fertility issues faced by children exposed inutero to antiretrovirals.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.053
GPT teacher head0.369
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicReproductive Health and TechnologiesFrench-language works237,207