Effects of long-term exposures to low iron and branched-chain amino acid containing diets on aging skeletal muscle of Fisher 344 × Brown Norway rats
Bibliographic record
Abstract
Aging skeletal muscle displays an altered iron status that may promote oxidative stress and sarcopenia. A diet containing low iron (LI) could reduce muscle iron status and attenuate age-related muscle atrophy. Supplemental branched-chain amino acids (BCAA) may also alleviate sarcopenia by promoting muscle protein synthesis and iron status improvement. This study examined individual and combined effects of LI and BCAA diets on anabolic signaling and iron status in skeletal muscle of aging rats. Twenty-nine-month-old male Fisher 344 × Brown Norway rats consumed the following control-base diets: control + regular iron (35 mg iron/kg) (CR; n = 11); control + LI (∼6 mg iron/kg) (CL; n = 11); 2×BCAA + regular iron (BR; n = 10); and 2×BCAA + LI (BL; n = 12) for 12 weeks. Although LI and/or 2×BCAA did not affect plantaris muscle mass, 2×BCAA groups showed lower muscle iron content than did CR and CL groups (P < 0.05). p70 ribosomal protein S6 kinase phosphorylation was greater in 2×BCAA and LI animals compared with CR animals (P < 0.05). Interactions between IRON and BCAA were observed for proteins indicative of mitochondrial biogenesis (peroxisome proliferator-activated receptor gamma coactivator 1 alpha) and oxidative capacity (cytochrome c oxidase subunit 2 and citrate synthase) (P < 0.05) wherein the combined diet (BL) negated potential benefits of individual diets. Antioxidant capacity, superoxide dismutase activity, and oxidative injury (3-nitrotyrosine, protein carbonyls, and 4-hydroxynonenal) were similar between groups. In conclusion, 12 weeks of LI and 2×BCAA diets showed significant impacts on increasing anabolic signaling as well as ameliorating iron status; however, these interventions did not affect muscle mass.
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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) | 0.001 | 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) | 0.001 | 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".