Amino acid metabolism in cancer cachexia and chemotherapy myotoxicity
Bibliographic record
Abstract
Cancer-induced skeletal muscle wasting (cachexia) is responsible for over 20% of cancer-related deaths, yet much about the pathophysiology of the condition remains unknown. Importantly, cancer cachexia does not seem wholly responsive to traditional anabolic stimuli such as nutritional interventions. It is possible that tumors directly or indirectly target skeletal muscle for their dynamic and abundant pool of amino acids that can be reliably used by tumors to supplement energy production and biomass synthesis. Therefore, understanding how the presence of a tumor alters circulating and tissue-level amino acid pools could provide valuable insight into tumor-induced muscle wasting. The purpose of this review was to examine the current body of research that has studied amino acids in the context of cancer cachexia to better understand how amino acids behave during the development of cancer, cachexia, and various cancer chemotherapies. Distinct heterogeneity was observed in the behavior of amino acids when comparing weight-stable versus patients with cachectic cancer, and there may be important temporal considerations based on rhythmic changes in amino acid turnover and mealtimes. Overall, there was very little consistency in the reported fluctuations of amino acids from study to study, suggesting that there may be heterogeneous pathophysiology based on tumor type, stage, patient age, and comorbidities. Further work is required to characterize longitudinal changes in amino acid metabolism with consideration for these factors. Enhancing our understanding of amino acid metabolism during cancer cachexia could provide opportunities for advancement in practical methodologies in cachexia research and treatment strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".