Perioperative arginine prevents metastases by accelerating natural killer cell recovery after surgery
Bibliographic record
Abstract
Profound natural killer (NK) cell suppression after cancer surgery is a main driver of metastases and recurrence, for which there is no clinically approved intervention available. Surgical stress is known to cause systemic postoperative changes that negatively modulate NK cell function including the expansion of surgery-induced myeloid-derived suppressor cells (Sx-MDSCs) and a marked reduction in arginine bioavailability. In this study, we determine that Sx-MDSCs regulate systemic arginine levels in the postoperative period and that restoring arginine imbalance after surgery by dietary intake alone was sufficient to significantly reduce surgery-induced metastases in our preclinical murine models. Importantly, the effects of perioperative arginine were dependent upon NK cells. Although perioperative arginine did not prevent immediate NK cell immunoparalysis after surgery, it did accelerate their return to preoperative cytotoxicity, interferon gamma secretion, and activating receptor expression. Finally, in a cohort of patients with colorectal cancer, postoperative arginine levels were shown to correlate with their Sx-MDSC levels. Therefore, this study lends further support for the use of perioperative arginine supplementation by improving NK cell recovery after surgery.
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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) | 0.000 | 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) | 0.002 | 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".