Abstract 1129: A multifaceted study of X-Ray radiation therapy across diverse mouse tumor models
Bibliographic record
Abstract
Abstract In this study, we showcase the application of X-ray radiation therapy of tumor-bearing mice across 13 syngeneic tumors, one intracranial tumor, and in combination with radiosensitizing drugs, aiming to develop potent cancer treatment strategies. 1.We examined the effects of different radiation levels on multiple tumor models using a therapeutic device delivering targeted radiation to the tumor site. 2.We investigated the combined benefits of radiation with the radiosensitizer Gemcitabine on the H22 liver tumor model. 3.We assessed the impact of radiation sensitization on the HCC1975-luc intracranial tumor model in combination with AZD0156, a drug that cannot cross the blood-brain barrier. The integrity of the blood-brain barrier and the presence of the pharmacodynamic marker pRAD50 of AZD0156 were evaluated. The results show that X-ray radiation has anti-tumor effects across diverse models, with combination drug treatment with both Gemcitabine and AZD0156 showing enhanced therapeutic effects. Furthermore, pRAD50 showed decreasing trend in the single dose PD study in HCC1975-luc intracranial model. We conclude that our platform provides robust methods for evaluating the therapeutic effects of X-Ray radiation, offering invaluable insights for the creation of new cancer therapies. Citation Format: Jingqi Huang, Wentao Li, Xiaoyan He, Weiwei Cheng, Lingyun Zhang, Guannan Li, Qiuliang Li, Yongfei Wang, Xuesong Ren, Zhi Wei, Long Shi, Yiran Wei, Jing Jin, Linfeng Li, Wei Yun. A multifaceted study of X-Ray radiation therapy across diverse mouse tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1129.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".