Effect of colostrum feeding strategies on the expression of neuroendocrine genes and active gut mucosa-attached bacterial populations in neonatal calves
Bibliographic record
Abstract
Colostrum feeding is vital for the development of the immune system and gastrointestinal tract in neonatal calves; however, it is currently unknown whether different colostrum feeding strategies affect their neuroendocrine system and potentially the gut-brain axis. The present study investigated the effect of 3 different colostrum feeding regimens on the expression of neuroendocrine genes in adrenal glands and gastrointestinal tissues and on the abundance of intestinal commensal bacteria. Holstein bull calves were fed colostrum immediately after birth and randomly assigned to 3 groups: whole milk (n = 8), mixture of 50% colostrum and 50% whole milk (n = 8), and colostrum (CF; n = 8) for 72 h with 12-h intervals. Adrenal glands, ileum, and colon tissues were collected at 75 h and were subjected to the expression of 11 targeted neuroendocrine genes and the abundance of tissue mucosa-associated bacteria measurement using quantitative real-time PCR and quantitative PCR, respectively. The expressions of all targeted genes were detected, and the expression of α-adrenergic receptor (ADRA1A) gene was affected by CF in adrenal glands and gut tissues. In addition, CF upregulated the expression of HTR4 (serotonin receptor) and SLC4A4 (serotonin transporter) genes in the ileum and increased the abundance of active Lactobacillus spp. and Escherichia coli (as detected at RNA level) associated with ileum and colon tissue. Furthermore, there were positive correlations between the abundance of active Lactobacillus spp. and E. coli with expression of HTR2B and HTR4 genes in the colon, suggesting that extended colostrum feeding strategies may affect the interaction between gut microbiota and host endocrine functions in neonatal calves.
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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) | 0.000 | 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) | 0.000 | 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".