EXTH-29. EVALUATION OF THE ANTITUMOR EFFECT OF A CHEMOTHERAPY-LOADED DEVICE FOR THE TREATMENT OF PRIMARY BRAIN TUMORS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Tumor recurrence is therefore inevitable, and median survival is just over 14 months. Currently available treatments are inadequate and do not provide optimal tumor control. We believe that treatment localized directly at the tumor site will eliminate a greater number of infiltrating tumor cells while significantly reducing systemic toxicity. For this project, we are collaborating with a team specializing in bioengineering, who have produced a biomaterial enabling the gradual release of various chemotherapeutic agents. Our aim is to use this biomaterial to deliver chemotherapeutic agents to the tumor site through the surgical cavity. Cell-based assays were carried out using glial tumor cells in the presence of the biomaterial containing chemotherapeutics. A survival study was realized with the Fischer F98 rat model, which underwent partial tumor resection followed by injection of biomaterial containing chemotherapy. Histological studies of brains and resected tissues were also carried out to assess the effect of the biomaterial on the brain environment. In vitro assays showed a strong cytotoxic effect against glial tumor cells at 24h, 48h and 72h of biomaterial-drug incubation. The combination of two chemotherapeutic agents showed the best cytotoxic effect on glial tumor cells. We also performed a partial tumor resection and insertion of the chemotherapy-loaded biomaterial into the surgical cavity of the F98 glioma bearing rat. Similar to in vitro studies, the combination of two antineoplastic agents shows a significant improvement in animal survival. In conclusion, the in vitro and in vivo results of this study show that the combination of chemotherapy has the greatest antitumor effect. We hope that these exciting results will lead to the development of a new treatment for patients with glioblastoma and potentially other diseases where complete tumor resection is impossible.
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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) | 0.002 | 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".