Sepsis increases perioperative metastases in a murine model
Bibliographic record
Abstract
BACKGROUND: Cancer surgery can promote tumour metastases and worsen prognosis, however, the effect of perioperative complications on metastatic disease remains unclear. In this study we sought to evaluate the effect of common perioperative complications including perioperative blood loss, hypothermia, and sepsis on tumour metastases in a murine model. METHODS: Prior to surgery, pulmonary metastases were established by intravenous challenge of CT26LacZ colon cancer cells in BALB/c mice. Surgical stress was generated through partial hepatectomy (PH) or left nephrectomy (LN). Sepsis was induced by puncturing the cecum to express stool into the abdomen. Hemorrhagic shock was induced by removal of 30% of total blood volume (i.e. stage 3 hemorrhage) via the saphenous vein. Hypothermia was induced by removing the heating apparatus during surgery and lowering core body temperatures to 30 °C. Lung tumour burden was quantified 3 days following surgery. RESULTS: Surgically stressed mice subjected to stage 3 hemorrhage or hypothermia did not show an additional increase in lung tumour burden. In contrast, surgically stressed mice subjected to intraoperative sepsis demonstrated an additional 2-fold increase in the number of tumour metastases. Furthermore, natural killer (NK) cell function, as assessed by YAC-1 tumour cell lysis, was significantly attenuated in surgically stressed mice subjected to intraoperative sepsis. Both NK cell-mediated cytotoxic function and lung tumour burden were improved with perioperative administration of polyI:C, which is a toll-like receptor (TLR)-3 ligand. CONCLUSIONS: Perioperative sepsis alone, but not hemorrhage or hypothermia, enhances the prometastatic effect of surgery in murine models of cancer. Understanding the cellular mechanisms underlying perioperative immune suppression will facilitate the development of immunomodulation strategies that can attenuate metastatic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".