Effects of Lactobacilli and an acidophilic fungus on the production performance and immune responses in broiler chickens
Bibliographic record
Abstract
Accumulated lines of evidence indicate that inactivated probiotics could have beneficial effects similar to those of live probiotics. Two strains of disrupted, cobalt-enriched, lactic acid bacteria (Lactobacillus acidophilus and Lactobacillus casei) and a disrupted fungal mycelium (Scytalidium acidophilum) were spray-mixed onto a mash basal feed, in 2 concentrations, prior to pelleting. The effects of these probiotics on production performance and immune response in broiler chickens were investigated. The production parameters, including BW, feed intake (FI), BW gain (BWG), and feed conversion ratio (FCR), were monitored weekly during a 6-wk trial. The immune response was evaluated by immunizing the birds with the antigen keyhole limpet hemocyanin (KLH) followed by a serological assay to measure blood IgA and IgG titers. Some of the production parameters were significantly improved by low L. casei (LCL; for BW and BWG), high L. acidophilus (LAH; for BW and BWG), and high fungal (FH; for BW, BWG, and FI) in comparison with the nonadditive control (NC-). However, these 3 treatments (LCL, LAH, and FH) did not enhance the measured immune responses. Instead, the titers of serum KLH-specific IgA in high L. casei (LCH) and low L. acidophilus (LAL) were significantly higher than those of NC-, 10 d after immunization. None of the probiotic treatments increased the titer of KLH-specific IgG in blood. Our results indicate that disrupted and cobalt-enriched L. acidophilus or L. casei was able to enhance production performance of broiler chickens. The fungal mycelium, S. acidophilum, when used at a high concentration, also demonstrated its potential for the first time to be used as a probiotic. In addition, the optimal concentration for administering probiotics is strain dependent. A higher dose does not always result in a better performance.
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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) | 0.001 | 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) | 0.000 | 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".