Pork Meat Proteins Alter Gut Microbiota and Lipid Metabolism Genes in the Colon of Adaptive Immune‐Deficient Mice
Bibliographic record
Abstract
Scope Excessive consumption of processed meat has been linked to an increasing risk of gut diseases. It is investigated how pork meat proteins affect colon homeostasis between normal and immune‐compromised mice. Methods and results Immune‐deficient mice (Rag1−/−) and wild‐type mice are fed a diet that contains 20% casein or protein isolated from cooked pork or dry‐cured pork for 3 months. Rag1−/− mice show greater variations in transcriptome responses and higher microbial diversity than wild‐type mice after consumption of the pork meat protein diets. Intake of pork meat protein diets also increases body weight and induces colonic oxidative stress, low‐grade inflammation, and gene expression involved in immune function, cell cycle, and migration. Key genes like Hmox1, Ppara, and Pparg are highly upregulated by pork meat protein. These changes are associated with decreased abundances of Blautia, Bifidobacterium, and Alistipes and increased abundances of Akkermansia muciniphila and Ruminococcaceae. Conclusion Pork meat proteins affect colon health in both wild‐type and Rag1−/− mice by altering the microbiome profile under the complex interaction with adaptive immunity. The findings herein give a new insight into the understanding of meat intake, immunity, and gut health.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".