Higher skeletal muscle protein synthesis and lower breakdown after chemotherapy in cachectic mice
Bibliographic record
Abstract
The influence of cancer cachexia and chemotherapy and subsequent recovery of skeletal muscle protein mass and turnover was investigated in mice. Cancer cachexia was induced using colon 26 adenocarcinoma, which is characteristic of the human condition, and can be cured with 100% efficacy using an experimental nitrosourea, cystemustine (C(6)H(12)CIN(3)O(4)S). Reduced food intake was not a factor in these studies. Three days after cachexia began, healthy and tumor-bearing mice were given a single intraperitoneal injection of cystemustine (20 mg/kg). Skeletal muscle mass in tumor-bearing mice was 41% lower (P < 0.05) than in healthy mice 2 wk after cachexia began. Skeletal muscle wasting was mediated initially by decreased protein synthesis (-38%; P < 0.05) and increased degradation (+131%; P < 0.05); later wasting resulted solely from decreased synthesis (~-54 to -69%; P < 0.05). Acute cytotoxicity of chemotherapy did not appear to have an important effect on skeletal muscle protein metabolism in either healthy or tumor-bearing mice. Recovery began 2 days after treatment; skeletal muscle mass was only 11% lower than in healthy mice 11 days after chemotherapy. Recovery of skeletal muscle mass was affected initially by decreased protein degradation (-80%; P < 0.05) and later by increased protein synthesis (+46 to +73%; P < 0.05) in cured compared with healthy mice. This study showed that skeletal muscle wasted from cancer cachexia and after chemotherapeutic treatment is able to generate a strong anabolic response by making powerful changes to protein synthesis and degradation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".