Investigating the effects of stereotactic body radiation therapy on pancreatic tumor hypoxia and microvasculature in an orthotopic mouse model using intravital fluorescence microscopy
Bibliographic record
Abstract
Despite decades of improvements in cytotoxic therapy, the current standard of care for locally advanced pancreatic cancer (LAPC) provides, on average, only a few months of survival benefit. Stereotactic Body Radiation Therapy (SBRT), a technique that accurately delivers high doses of radiation to tumors in fewer fractions, has emerged as a promising therapy to improve local control of LAPC; however, its effects on the tumor microenvironment and hypoxia remain poorly understood. To explore how SBRT affects pancreatic tumors, we combined an orthotopic mouse model of pancreatic cancer with an intravital microscopy platform to visualize changes to the in vivo tumor microenvironment in real-time. Mice received SBRT (5 × 8 Gy) or were left untreated, and were imaged before and 1, 4, 7, and 14 days after treatment (n = 7/group). A fluorescent human pancreatic cancer cell line (BxPC3-DsRed) engineered to express GFP under hypoxic conditions (driven by hypoxia-inducible factor, HIF) was used to monitor tumor hypoxia. Immunohistochemical staining was also performed on tissues to validate in vivo data. Our findings demonstrate a persistent decrease in pancreatic tumor hypoxia as early as one day after SBRT. This coincided with a decrease in both tumor cell proliferation and cell density in the SBRT group. Reduced demand for oxygen after SBRT (due to cell death and growth arrest from treatment) significantly contributed to reoxygenation of the pancreatic TME. Understanding how this reoxygenation phenomenon occurs in a dose-dependent manner will help improve dosing and fractionation schemes for clinical SBRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".