A mouse model of HIV in pregnancy: the EcoHIV+ pregnancy model
Bibliographic record
Abstract
BACKGROUND: HIV infection during pregnancy poses risks to maternal and foetal health. Identifying underlying mechanisms can be challenging in humans. While humanised mouse models exist, they are unsuitable for pregnancy research, highlighting the need for alternative models. Here we introduce a mouse pregnancy model using infection with EcoHIV, a chimeric ecotropic HIV virus. METHODS: pg/mL EcoHIV, or mock infected, either 7 days prior to mating, or on gestational day (GD) 11.5. Dam weight gain was monitored. Pregnant dams were euthanised on GD14.5 or GD18.5. Foetal and placenta weights, foetal viability, litter size and resorptions were recorded. Placenta efficiency (foetal to placental weight ratio) was calculated. Infection was assessed using HIV Gag expression quantified by qPCR in RNA isolated from maternal blood, spleen, and foetal body. FINDINGS: EcoHIV infection was detectable in 90% of dams infected prior to pregnancy and 100% of dams infected during pregnancy. Maternal weight gain was lower in EcoHIV infected mice, with the greatest reduction seen in those infected during pregnancy. EcoHIV infection was associated with significantly lower foetal weight, higher placenta weight, and lower placenta efficiency compared to controls at GD18.5. Perinatal EcoHIV transmission occurred in a portion of foetuses, with litter average transmission rates ranging from 3.1% with infection during pregnancy to 17.9% with infection prior to pregnancy. INTERPRETATION: The EcoHIV pregnancy model mimics clinical aspects and can be a valuable tool to understand HIV infection in pregnancy and its consequences on maternal and foetal health. FUNDING: This project has been funded by the Canadian Institutes of Health Research (CIHR) (award # PJT-180630, PJH-192202, HAL-157984). MR received salary support from NSERC/CIHR Canada Graduate Scholarship, Institute of Medical Science Fellowship Award, and Emerging & Pandemic Infections Consortium (EPIC) Doctoral Award. LS holds a Tier 1 Canada Research Chair in Maternal-Child Health and HIV.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".