First-trimester exposure to anti-cytomegalovirus medications: a study of the effects of valaciclovir, letermovir and maribavir on early placental development
Bibliographic record
Abstract
Abstract Congenital cytomegalovirus (CMV) infection is the leading preventable cause of childhood disability, including sensorineural hearing loss and cerebral palsy in high-income countries. Early maternal treatment with valaciclovir has been shown to reduce fetal infection. However, newer, more potent anti-CMV medications such as letermovir and maribavir are not routinely used in pregnancy due to limited safety data. This study aimed to assess the effect of aciclovir (the active metabolite of valaciclovir), letermovir, and maribavir on cell viability, functionality, and key cell signalling pathways in first-trimester human placental tissue. First-trimester placental tissue was obtained from individuals undergoing surgical abortion for psychosocial indications at 9-13 weeks of gestation. The effect of anti-CMV medications on primary cytotrophoblast cell survival was measured using a standard cell viability assay. The effects of anti-CMV medication exposure on Wnt and epithelial-to-mesenchymal transition (EMT) signalling pathways were evaluated in placental explant cultures. Their impact on cellular proliferation and migration was assessed using placental outgrowth cultures. Exposure to physiological or supraphysiological levels of aciclovir, letermovir, or maribavir for 48 hours did not significantly alter cytotrophoblast cell viability compared to vehicle-treated controls. Aciclovir did not significantly impact Wnt and EMT signalling gene expression in first-trimester placental explants; however, letermovir and maribavir induced significant gene dysregulation. None of the three anti-CMV medications altered first-trimester trophoblast proliferation or migration. These results provide reassuring data supporting the safety of maternal valaciclovir therapy in the first trimester. They may also inform the selection of newer anti-CMV medications for future clinical trials for use in pregnancy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".