Severity of COVID-19 in Pregnant Women: A Review on the PotentialRole of Regulatory T Cells
Bibliographic record
Abstract
As a physiological condition, pregnancy may cause temporary alterations in the hematological, cardiopulmonary, and immune responses, affecting the maternal susceptibility to viral infections. Pregnant women are vulnerable to infection with the influenza A virus, hepatitis E virus, MERS CoV, and SARS CoV. The agent of Coronavirus disease (COVID-19) is the SARS coronavirus (SARS CoV-2), which affects the cells upon binding to the angiotensin-converting enzyme-2 (ACE2). However, ACE2 expression is elevated in the placental tissue. However, surprisingly, COVID-19 infection in pregnant women tends to have a lower severity and mortality. Therefore, it is interesting to find the immunological mechanisms related to the severity of COVID-19 in pregnancy. Regulatory T cells (Tregs) are a subset of CD4+T cells that may play a central role in maintaining maternal tolerance by regulating immune responses. Pregnancy-induced Tregs are developed to control immune responses against paternal antigens expressed by the semi-allograft fetus. The role of uncontrolled immune responses in COVID-19 pathogenesis has already been identified. This review provides insight into whether pregnancy- induced regulatory T-cell functions could influence the severity of COVID-19 infection during 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".