Low dietary protein and high carbohydrate infant formula affects the microbial ecology of the large intestine in neonatal rats
Bibliographic record
Abstract
The aim of this study was to investigate the effects of a low dietary protein and high carbohydrate infant formula on the large intestine of neonatal rats. A total of 24 neonatal Sprague-Dawley rats (14-days-old) were randomly assigned to the low protein, high carbohydrate infant formula-fed group (I group) and a human breast milk-fed group (H group). After 7 days, we selected 6 rats at random from each group to study. No significantly different microbial colonization patterns were observed in the 2 groups at the phylum level. At the family level, Enterobacteriaceae and Bacteroidaceae were the dominant bacteria in I and H rats. While Bacteroides was the most abundant bacteria at the genus level, no significant difference was observed between the 2 groups. Methanoic acid, acetate, and butyrate increased in concentration in the I group compared with the H group. Protease activities, ammonia, and indole in the large intestine were lower in I rats than H rats. A significant increase in the expression of GADPH and decrease in the expression of aquaporin 8, aminopeptidase A, cathepsin F precursor, and ubiquitin carboxyl-terminal hydrolase FAF-Y were observed in I rats compared with H rats. These results suggest that a low protein diet could modulate the microbial ecology in the large intestine of neonatal rats.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".