Probiotic Interventions Alleviate Food Allergy Symptoms Correlated With Cesarean Section: A Murine Model
Bibliographic record
Abstract
Delivery by cesarean section (CS) is linked to an increased incidence of food allergies in children and affects early gut microbiota colonization. Furthermore, emerging evidence has connected disordered intestinal microbiota to food allergies. Here, we investigated the impact of CS on a rat model for food allergy to ovalbumin (OVA). Rats delivered by CS were found to be more responsive to OVA sensitization than vaginally born ones, displaying a greater reduction in rectal temperature upon challenge, worse diarrhea, and higher levels of OVA-specific antibodies and histamine. 16S rRNA sequencing of feces revealed reduced levels of Lactobacillus and Bifidobacterium in the CS rats. Preventative supplementation with a probiotic combination containing Lactobacillus and Bifidobacterium could protect CS rats against an allergic response to OVA, indicating that the microbiota dysbiosis contributes to CS-related response. Additionally, probiotic intervention early in life might help to rebuild aberrant Th2 responses and tight junction proteins, both of which have been linked to CS-related high allergic reactions. Taken together, this study shows that disordered intestinal microbiota plays an essential role in the pathogenesis of food allergy mediated by CS. More importantly, interventions that modulate the microbiota composition in early life are therapeutically relevant for CS-related food allergies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".