A proinflammatory tumor that activates protein degradation sensitizes rats to catabolic effects of endotoxin
Bibliographic record
Abstract
Chronic or acute inflammation may participate in the etiology of cancer cachexia. To investigate the interaction between tumor and a secondary inflammatory stimulus on muscle wasting, rats with and without tumors (Yoshida ascites hepatoma) received low doses of endotoxin (LPS, 400 microg/kg sc) or saline. Nitrogen balance was measured 24 h before and after LPS/saline. Epitrochlearis muscle was used to measure in vitro protein metabolism, and gastrocnemius muscle was used for quantification of the mRNA for components of the ubiquitin proteolytic pathway. The YAH reduced muscle mass (P = 0.002), increased muscle protein degradation (P = 0.042), and elevated mRNA expression of components of the ubiquitin proteolytic pathway (P < 0.01) including ubiquitin, ubiquitin-conjugating enzyme E2(14k), and ubiquitin ligases muscle RING Finger 1 and atrogin-1. Although the selected low dose of LPS had no impact on protein metabolism in control rats, LPS in rats bearing YAH caused weight loss (P = 0.0007), lowered nitrogen balance (P = <0.0001), and increased muscle protein degradation (P = 0.0336). In conclusion, the presence of a tumor can potentiate whole body and muscle-specific catabolic losses of protein in response to a stimulus that is not catabolic in healthy animals. This effect might be dependent on the inflammatory nature of the tumor.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".