Impact of formulation on solid oxygen‐entrapping materials to overcome tumor hypoxia
Bibliographic record
Abstract
Abstract Tumor hypoxia, resulting from rapid tumor growth and aberrant vascular proliferation, exacerbates tumor aggressiveness and resistance to treatments like radiation and chemotherapy. To increase tumor oxygenation, we developed solid oxygen gas‐entrapping materials (O2‐GeMs), which were modeled after clinical brachytherapy implants, for direct tumor implantation. The objective of this study was to investigate the impact different formulations of solid O2‐GeMs have on the entrapment and delivery of oxygen. Using a Parr reactor, we fabricated solid O2‐GeMs using carbohydrate‐based formulations used in the confectionary industry. In evaluating solid O2‐GeMs manufactured from different sugars, the sucrose‐containing formulation exhibited the highest oxygen concentration at 1 mg/g, as well as the fastest dissolution rate. The addition of a surface coating to the solid O2‐GeMs, especially polycaprolactone, effectively prolonged the dissolution of the solid O2‐GeMs. In vivo evaluation confirmed robust insertion and positioning of O2‐GeMs in a malignant peripheral nerve sheath tumor, highlighting potential clinical applications.
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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 | 0.000 |
| 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".