16S rRNA gene-based analysis of fecal microbiota from preterm infants with and without necrotizing enterocolitis
Bibliographic record
Abstract
Neonatal necrotizing enterocolitis (NEC) is an inflammatory intestinal disorder affecting preterm infants. Intestinal bacteria have an important function; however no causative pathogen has been identified. The purpose of this study was to determine if there are differences in microbial patterns that may be critical to the development of this disease. Fecal samples from 20 preterm infants, 10 with NEC and 10 matched controls (including 4 twin pairs) were obtained from patients in a single site level III neonatal intensive care unit. Bacterial DNA from individual fecal samples was PCR-amplified and subjected to terminal restriction fragment length polymorphism analysis and library sequencing of the 16S rRNA gene to characterize diversity and structure of the enteric microbiota. The distribution of samples from NEC patients distinctly clustered separately from controls. Intestinal bacterial colonization in all preterm infants was notable for low diversity. Patients with NEC had even less diversity, an increase in abundance of Gammaproteobacteria, a decrease in other bacteria species, and had received a higher mean number of previous days of antibiotics. Our results suggest that NEC is associated with severe lack of microbiota diversity that may accentuate the impact of single dominant microorganisms favored by empiric and widespread use of antibiotics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".