Effects of diet type and enzyme addition on growth performance and gut health of broiler chickens during subclinical Clostridium perfringens challenge
Bibliographic record
Abstract
The effects of diet type (corn- vs. wheat-based) and multicarbohydrase addition on growth performance, digesta pH and viscosity, intestinal populations of Clostridium perfringens and lactic acid bacteria, and gut lesion score (from 0 to 4, where 0 = no gross lesions, 4 = severe extensive necrosis) of broiler chickens during oral challenge with C. perfringens (none or 10(8) cfu/bird on d 13) were studied in a 39-d experiment. A total of 1,216 male Ross-308 chickens was assigned to 8 dietary treatments in a randomized complete block design providing 8 replicate pens per treatment. Diets were formulated to meet the NRC protein requirement but were suboptimal in energy level. When compared with birds fed corn-based diets, chickens fed wheat-based diets had inferior (P < 0.01) final BW (2.49 vs. 2.59 kg) and feed conversion ratio (FCR; 1.83 vs. 1.78). Pathogen challenge significantly (P < 0.05) impaired growth performance and increased C. perfringens numbers and average lesion score. Increased (P < 0.01) C. perfringens counts (2.4 vs. 1.5 log(10) cfu/g of digesta) and intestinal lesion score (0.9 vs. 0.4) were observed for challenged birds fed wheat-based diets. No difference in digesta pH and lactic acid bacteria numbers were found among the treatments. Enzyme addition to both the corn- and wheat-based diets increased bird final BW (2.57 vs. 2.51 kg; P < 0.01), decreased overall FCR (1.78 vs. 1.83; P < 0.01), and, in those consuming wheat-based diets, reduced digesta viscosity (from 4.1 to 2.7 mPa.s; P < 0.01). Enzyme supplementation assisted the challenged birds in maintaining their optimal growth performance by improving (P < 0.05) average daily gain (59.5 vs. 56.9 g) in those consuming corn-based diets and FCR (1.83 vs. 1.90) in those consuming wheat-based diets to values similar to those observed in control birds (59.7 g/d and 1.84, respectively). In conclusion, enzyme addition improved growth performance and mitigated the negative effects of C. perfringens challenge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".