Is maternal SARS-CoV-2 infection in the first trimester associated with congenital heart defects?
Bibliographic record
Abstract
Emerging evidence suggests a potential link between maternal SARS-CoV-2 infection during early pregnancy and the development of congenital heart defects (CHD) in offspring. Although vertical transmission of SARS-CoV-2 is rare, the virus has been associated with placental complications and increased maternal morbidity. Recent studies from China report increased rates of CHD and anomalies such as situs inversus when infection occurs during gestational weeks 4-6, a critical window for cardiac development. Additional reports from different parts of the world also highlight post-pandemic increases in specific cardiac anomalies, including ventricular septal defects (VSDs). Multiple mechanisms may underlie these associations. SARS-CoV-2 can induce placental inflammation, compromise the cytotrophoblast barrier, and impair nutrient and gas exchange, potentially leading to fetal hypoxia and disrupted morphogenic signalling. Furthermore, maternal inflammation and elevated cytokines, along with viral effects on ACE2-expressing fetal cardiac progenitors, could further affect proliferation, differentiation, and apoptosis during cardiac development. Co-infections, hormone disruption, and maternal stress could also contribute. There is an urgent need for longitudinal studies with comprehensive maternal-fetal data, including infection timing, vaccine status, and biological sampling. These will be essential to delineate the multifactorial impacts of maternal infection on fetal cardiac development and long-term outcomes. Special focus should be placed on infections during early pregnancy (weeks 4-7), the period of cardiac septation and left-right asymmetry establishment, to determine causality and inform prevention strategies.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".