The mTORC1-Signaling Pathway and Hepatic Polyribosome Profile Are Enhanced after the Recovery of a Protein Restricted Diet by a Combination of Soy or Black Bean with Corn Protein
Bibliographic record
Abstract
Between 6% and 11% of the world's population suffers from malnutrition or undernutrition associated with poverty, aging or long-term hospitalization. The present work examined the effect of different types of proteins on the mechanistic target of rapamycin (mTORC1)-signaling pathway in: (1) healthy; and (2) protein restricted rats. (1) In total, 200 rats were divided into eight groups and fed one of the following diets: 20% casein (C), soy (S), black bean (B), B + Corn (BCr), Pea (P), spirulina (Sp), sesame (Se) or Corn (Cr). Rats fed C or BCr had the highest body weight gain; rats fed BCr had the highest pS6K1/S6K1 ratio; rats fed B, BCr or P had the highest eIF4G expression; (2) In total, 84 rats were fed 0.5% C for 21 day and protein rehabilitated with different proteins. The S, soy + Corn (SCr) and BCr groups had the highest body weight gain. Rats fed SCr and BCr had the highest eIF4G expression and liver polysome formation. These findings suggest that the quality of the dietary proteins modulate the mTORC1-signaling pathway. In conclusion, the combination of BCr or SCr are the best proteins for dietary protein rehabilitation due to the significant increase in body weight, activation of the mTORC1-signaling pathway in liver and muscle, and liver polysome formation.
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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 | 0.000 |
| 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".