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Record W4390885563 · doi:10.1186/s40104-023-00972-8

Effects of dietary Clostridium butyricum and rumen protected fat on meat quality, oxidative stability, and chemical composition of finishing goats

2024· article· en· W4390885563 on OpenAlexaff
Meimei Zhang, Zhiyue Zhang, Xinlong Zhang, Changming Lu, Wenzhu Yang, Xiaolai Xie, Hangshu Xin, Xiaotan Lu, Mingbo Ni, Xinyue Yang, Xiaoyang Lv, Peixin Jiao

Bibliographic record

VenueJournal of Animal Science and Biotechnology/Journal of animal science and biotechnology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaHeilongjiang Provincial Postdoctoral Science FoundationNortheast Agricultural UniversityNatural Science Foundation of Heilongjiang ProvinceKey Technologies Research and Development ProgramGovernment of Jiangsu Province
KeywordsClostridium butyricumPolyunsaturated fatty acidFood scienceIntramuscular fatRumenDry matterRandomized block designChemistrySoybean mealGlutathione peroxidaseNeutral Detergent FiberAnimal scienceAntioxidantComposition (language)BiologyFatty acidBiochemistryFermentationSuperoxide dismutaseAgronomy

Abstract

fetched live from OpenAlex

BACKGROUND: Clostridium butyricum (CB) is a probiotic that can regulate intestinal microbial composition and improve meat quality. Rumen protected fat (RPF) has been shown to increase the dietary energy density and provide essential fatty acids. However, it is still unknown whether dietary supplementation with CB and RPF exerts beneficial effects on growth performance and nutritional value of goat meat. This study aimed to investigate the effects of dietary CB and RPF supplementation on growth performance, meat quality, oxidative stability, and meat nutritional value of finishing goats. Thirty-two goats (initial body weight, 20.5 ± 0.82 kg) were used in a completely randomized block design with a 2 RPF supplementation (0 vs. 30 g/d) × 2 CB supplementation (0 vs. 1.0 g/d) factorial treatment arrangement. The experiment included a 14-d adaptation and 70-d data and sample collection period. The goats were fed a diet consisted of 400 g/kg peanut seedling and 600 g/kg corn-based concentrate (dry matter basis). RESULT: (P = 0.009), a* values (P = 0.007), total antioxidant capacity (P = 0.050), glutathione peroxidase activities (P = 0.006), concentrations of 18:3 (P < 0.001), 20:5 (P = 0.003) and total polyunsaturated fatty acids (P = 0.048) were increased, whereas the L* values (P < 0.001), shear force (P = 0.050) and malondialdehyde content (P = 0.044) were decreased by adding CB. Furthermore, CB supplementation increased essential amino acid (P = 0.027), flavor amino acid (P = 0.010) and total amino acid contents (P = 0.024) as well as upregulated the expression of lipoprotein lipase (P = 0.034) and peroxisome proliferator-activated receptor γ (PPARγ) (P = 0.012), and downregulated the expression of stearoyl-CoA desaturase (SCD) (P = 0.034). The RPF supplementation increased dry matter intake (P = 0.005), averaged daily gain (trend, P = 0.058), hot carcass weight (P = 0.046), backfat thickness (P = 0.006), concentrations of 16:0 (P < 0.001) and c9-18:1 (P = 0.002), and decreased the shear force (P < 0.001), isoleucine (P = 0.049) and lysine content (P = 0.003) of meat. In addition, the expressions of acetyl-CoA carboxylase (P = 0.003), fatty acid synthase (P = 0.038), SCD (P < 0.001) and PPARγ (P = 0.022) were upregulated due to RPF supplementation, resulting in higher (P < 0.001) content of IMF. CONCLUSIONS: CB and RPF could be fed to goats for improving the growth performance, carcass traits and meat quality, and promote fat deposition by upregulating the expression of lipogenic genes of Longissimus thoracis muscle.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.276
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2024
Admission routes1
Has abstractyes

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