Effect of feeding traditionally prepared fermented milk dahi (curd) as a probiotics on nutritional status, hindgut health and haematology in dogs
Bibliographic record
Abstract
According to Indian system of traditional medicine (Ayurveda), dahi is beneficial in promotion of health and vitality due to its antibacterial action against pathogenic microbes and improvement in nutrient digestibility. Hence, in the present study traditionally prepared Indian fermented milk dahi/curd was evaluated as a probiotics for its health benefits in canine model. Eight Labrador dogs were divided in completely randomized design (CRD) in two groups, one control (CON, without supplement) and other treatment group (dahi, supplemented with measured amount (100 ml) of dahi/curd having ~10 cfu of Lactobacillus sp /ml). Nutrient digestibility and hindgut health was assessed after 6 weeks and haematology was done after 7 weeks of experimental feeding. There was a slight (P>0.05) increase in dry matter (DM, P=0.055), organic matter (OM, P=0.073), crude fibre (CF, P=0.104) digestibility while that of calcium improved significantly (P<0.05) due to feeding of dahi. Significant reduction in feacal pH (P<0.001) and ammonia (P<0.01), whereas lactate (P<0.001) and total short chain fatty acids (SCFAs, P<0.05) were increased in dahi fed group. The health positive microbial count (lactobacilli and bifidobacteria) were significantly (P<0.05) increased with decrease in health negative coliforms (P<0.01) in dahi fed animals. Total erythrocyte count (TEC) was increased (P<0.01) and mean corpuscular volume (MCV) decreased (P<0.01) in dahi compared to CON. In conclusion, traditionally prepared dahi/curd can be used as a probiotics with beneficial effect on digestibility of some nutrients, hindgut health characteristics, intestinal microbial balance and haematolgy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".