Natural Killer Cell IFNγ Secretion is Profoundly Suppressed Following Colorectal Cancer Surgery
Bibliographic record
Abstract
Surgical stress results in a significant reduction in natural killer (NK) cell cytotoxicity (NKC), which has been linked to postoperative cancer metastases. However, few studies have measured the impact of surgical stress upon NK cell IFNγ secretion (NKA), a cytokine with essential roles in controlling infection and metastases. The objective of this study was to investigate the impact of surgical stress on NKA in colorectal cancer (CRC) surgery patients. Peripheral blood was collected from CRC surgery patients ( n = 42) preoperatively and on postoperative day (POD) 1, 3, 5, 28, and 56. Healthy donor blood ( n = 27) was collected for controls. We assessed NKA by production of IFNγ following whole blood cytokine stimulation, NKC by 51 Cr-release assay, and immune cell profiling by flow cytometry. The mean reduction in NKA on POD1 compared with baseline was 83.1% (standard deviation 25.2%; confidence interval 75–91), and therefore the study met the primary endpoint of demonstrating a > 75% decrease in a cohort of CRC surgery patients ( p < 0.0001). The profound and universal suppression of NKA persisted with 65.5% (19/29) and 33.3% (4/12) of patients with levels measuring < 75% of baseline on POD28 and POD56 respectively. The NKC was significantly reduced on POD1, but the degree was less pronounced (24.6%, p = 0.0024). Immune cell profiling did not reveal differences in the absolute number of NK cells (CD3 − CD56 + ) or the ratio of CD56 dim -to-CD56 bright subsets. NKA is significantly suppressed for up to two months following surgery in CRC patients, a degree of surgery-induced immunosuppression far worse than previously reported.
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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 | 0.001 |
| 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) | 0.001 | 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".