Effects of Birth Delivery Mode and Antibiotic Use on Gut Microbiota in Preterm Newborns: A Cohort Study
Bibliographic record
Abstract
Background: The establishment of the gut microbiome begins very early in life. Bacterial colonization is influenced by several factors, especially the mode of delivery and antibiotic intake. In this study, we examined the composition of the neonatal gut microbiota within the first three weeks after birth, focusing on the impact of delivery mode and antibiotic use. Methods: This cohort study included 29 preterm newborns recruited between the first and second day of life at the National Reference Center for Neonatology and Nutrition. Stool samples were collected from diapers and stored at 4°C for up to 6 hours before being stored at -80°C until analysis. The gut microbiota was identified using RT-PCR targeting four phyla: Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. Results: The comparison of gut microbiota by delivery mode shows that the microbiota of newborns delivered by cesarean section was less diverse than that of those delivered vaginally. During the first 48 hours of life, Enterobacteriaceae, including Escherichia coli, were predominantly present in vaginal births, while Enterococcus spp. (25%), Staphylococcus spp. (20%) and Lactobacillus spp. (5%) were present only in vaginal births. From the second week onwards, Bacteroides fragilis (15%) and Bifidobacterium spp. (10%) were mainly present in vaginal births. By the end of the third week, Enterobacteriaceae and Enterococcus spp. were present in all newborns. All newborns received empiric antibiotic therapy upon admission, with 41% receiving antibiotics for more than 5 days. Conclusion: This study made it clear that microbiota requires time to progress inside the newborn's intestine, depending on the birth mode, either natural or cesarean section.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".