Vascular progenitor clusters from peripheral blood in cancer patients following oncologic surgery
Bibliographic record
Abstract
INTRODUCTION: Vascular progenitor cells (VPCs) are recruited into the peripheral blood (PB) following ischemia and inflammation and correlate with vascular health. The impact of recruiting VPCs on surgical recovery and cancer progression following tumor resection remain unknown. METHODS: We measured VPC clusters and enumerated circulating CD34+ VEGFR2+ angiogenic cells in 18 patients with oral cancer (OC) undergoing resection and free flap reconstruction (high vascular injury) and in 18 patients undergoing colorectal cancer resection (CRC) (low vascular injury) at baseline and multiple timepoints after surgery. RESULTS: VPC clusters increased following OC resection, peaking on Day +3 and returning to baseline by Day 28. In contrast, VPC clusters decreased sharply on Day +3 in patients with CRC before returning to baseline. CD34+ VEGFR2+ cells did not increase significantly after surgery. More rapid clinical recovery following OC resection was observed in patients with greater VPC cluster levels on Day +3. Tumor size and subsequent progression of cancer did not correlate with recruitment of VPC cluster-forming cells. CONCLUSION: VPC recruitment following cancer resection may depend on cancer subtype and may relate to the degree of surgical stress and vascular injury. Recovery after surgery for OC may be accelerated in patients with greater VPC recruitment.
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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 | 0.000 |
| 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 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".