Rotavirus Interactions With Host Intestinal Epithelial Cells
Bibliographic record
Abstract
Rotavirus (RV) is the foremost enteric pathogen associated with severe diarrheal illness in young children (<5years) and animals worldwide. RV primarily infects mature enterocytes in the intestinal epithelium causing villus atrophy, enhanced epithelial cell turnover and apoptosis. Intestinal epithelial cells (IECs) being the first physical barrier against RV infection employs a range of innate immune strategies to counteract RVs invasion, including mucus production, toll-like receptor signaling and cytokine/chemokine production. Conversely, RVs have evolved numerous mechanisms to escape/subvert host immunity, seizing translation machinery of the host for effective replication and transmission. RV cell entry process involve penetration through the outer mucus layer, interaction with cell surface molecules and intestinal microbiota before reaching the IECs. For successful cell attachment and entry, RVs use sialic acid, histo-blood group antigens, heat shock cognate protein 70 and cell-surface integrins as attachment factors and/or (co)-receptors. In this review, a comprehensive summary of the existing knowledge of mechanisms underlying RV-IECs interactions, including the role of gut microbiota, during RV infection is presented. Understanding these mechanisms is imperative for developing efficacious strategies to control RV infections, including development of antiviral therapies and vaccines that target specific immune system antagonists within IECs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".