Improved appetite of pregnant rats and increased birth weight of newborns following feeding with probiotic Lactobacillus rhamnosus GR-1 and L. fermentum RC-14
Bibliographic record
Abstract
Malnutrition and pathogenic colonization of the vagina are two major contributors to preterm labour, newborn survival and low birth babies at high risk of long term poor quality of life. It was hypothesized that use of probiotics as a food supplement would improve the appetite and health of the mother and newborn babies. Two probiotic strains, Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14 were tested in a Sprague-Dawley albino rat model. The probiotic preparations (1X109cfu/ml) were administered to rats in group A, as supplementation in drinking water for 30 days. Feed intake and the birth weight of the newborns were measured. There was a significant improvement in appetite for the lactobacilli treated animals with a mean weight of 31.16g of feed, compared with 27.16g for the controls, P<0.01. The mortality rate for the control animals was 6% while no death occurred in the probiotic group. There was significantly increased birth weight among 37 newborns whose mothers had been fed probiotics (6.5g), compared to controls (4.5g) (P<0.01). There was a two log increase in total lactobacilli recovered from the stool of the probiotic treated animals with significant presence of the two lactobacilli treatment strains. No adverse effects were noted in the animals. This is the first report of nutritional appetite benefits of probiotics during pregnancy and of improvements in weight of newborn babies. Considering well nourished babies born within the normal weight range have a considerably better long term prognosis, if the present findings were to be duplicated in pregnant women, the implications for health especially of mothers and babies in developing countries could be very significant.
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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".