Neurodevelopmental implications of COVID-19-induced gut microbiome dysbiosis in pregnant women
Bibliographic record
Abstract
The global public health emergency of COVID-19 in January 2020 prompted a surge in research focusing on the pathogenesis and clinical manifestations of the virus. While numerous reports have been published on the acute effects of COVID-19 infection, the review explores the multifaceted long-term implications of COVID-19, with a particular focus on severe maternal COVID-19 infection, gut microbiome dysbiosis, and neurodevelopmental disorders in offspring. Severe COVID-19 infection has been associated with heightened immune system activation and gastrointestinal symptoms. Severe COVID-19 may also result in gut microbiome dysbiosis and a compromised intestinal mucosal barrier, often referred to as 'leaky gut'. Increased gut permeability facilitates the passage of inflammatory cytokines, originating from the inflamed intestinal mucosa and gut, into the bloodstream, thereby influencing fetal development during pregnancy and potentially elevating the risk of neurodevelopmental disorders such as autism and schizophrenia. The current review discusses the role of cytokine signaling molecules, microglia, and synaptic pruning, highlighting their potential involvement in the pathogenesis of neurodevelopmental disorders following maternal COVID-19 infection. Additionally, this review addresses the potential of probiotic interventions to mitigate gut dysbiosis and inflammatory responses associated with COVID-19, offering avenues for future research in optimizing maternal and fetal health outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".