Do patients with advanced cancer have any potential for protein anabolism in response to amino acid therapy?
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is evidence that protein anabolism is achievable before cancer evolves into refractory cachexia with attenuation of muscle loss and even muscle gain. This review summarizes recent observations on the role of total and specific amino acids in promoting protein anabolism in human cancer and revisits prior studies in this context. RECENT FINDINGS: Analysis of muscle changes in advanced cancer patients indicated opportunities for inducing anabolism. Maintenance and gain in muscle was reported in a majority of patients, from initiation of oncologic treatment and before the final refractory stage. In addition to being substrates, some amino acids, for example leucine, act as intracellular signals to promote protein synthesis. Recent acute studies demonstrated that provision of amino acids, sufficient to considerably elevate circulating leucine concentrations concurrent with other amino acid and nutrient availability, resulted in significant protein anabolism in cancer patients. This occurred even during weight loss and inflammation. SUMMARY: Patients with cancer have an anabolic potential to be exploited early on in cachexia development. High-leucine and protein supplements are worth testing as part of a multimodal anabolic approach in long-term trials to confirm their efficacy to sustain anabolism, and attenuate or even reverse muscle wasting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".