UTMC effect on cancer cell apoptosis, proliferation, and vascular inflammation in wild type and CD39 knock out mice model of MC38 colon cancer
Bibliographic record
Abstract
Purinergic signaling, governed by ATP and its conversion to adenosine, significantly impacts the immune responses within the tumor microenvironment. Ultrasound Targeted Microbubble Cavitation (UTMC) has been shown to enhance local ATP release in muscle and tumors. This study aimed to investigate the modulation of inflammatory responses following UTMC by ATP signaling within the tumor microenvironment. The expression of key markers related to vascular inflammation, apoptosis, and cell proliferation were examined 24h after UTMC treatment in wild-type (WT) and CD39 knockout (KO) mice. MC38 tumor cells were implanted in mice, and UTMC treatments (1MHz, 5000 cycles, 120 pulses total) were administered at different pressures (400 and 850 kPa). Immunohistochemistry and immunofluorescence analyses revealed that UTMC treatment led to increased apoptosis (CC3) and reduced cell proliferation (Ki67) in both WT and CD39KO mice, with the effect being more pronounced at p=850 kPa in CD39KO mice. Notably, UTMC treatment induced an increase in vascular inflammation markers (ICAM-1 and VCAM-1) in CD39KO mice, implying a potential role of ATP in mediating the immune responses triggered by UTMC. These findings highlight the intricate interplay between purinergic signaling, UTMC, and immune responses within the tumor microenvironment, offering insights into novel approaches for enhancing cancer immunotherapy.
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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".