TMIC-77. UNVEILING THE DEPTHS: EXPLORING DIFFUSE MIDLINE GLIOMA, TUMORIGENESIS, AND RADIATION RESISTANCE
Bibliographic record
Abstract
Abstract Diffuse Midline Glioma (DMG) is a highly aggressive form of brainstem cancer that primarily affects children, leading to a grim prognosis and limited treatment options. Despite extensive research efforts, many crucial aspects of DMG development and progression remain poorly understood. One significant knowledge gap lies in comprehending the invasive nature of DMG cells, which enables their escape from the primary tumor site and dissemination to other regions of the brain. Additionally, understanding the molecular basis that drives this invasive behavior is essential but remains largely unexplored, necessitating further investigation. To address these critical knowledge gaps, we have generated one of the largest single-cell multi-omic datasets ( >300,000 cells) specifically focusing on pons development in humans and its relation to DMG. This dataset provides a valuable resource for investigating the intricacies of tumorigenesis and the development of resistance to radiation therapy. Comprehensive analyses of this dataset shed light on the molecular events driving DMG progression and radiation resistance, ultimately paving the way for the discovery of novel therapeutic targets and the development of innovative strategies to combat this devastating pediatric cancer. In summary, our study aims to deepen our understanding of DMG by elucidating the mechanisms associated with histone mutations, unraveling the invasive behavior of DMG cells, and exploring the factors contributing to radiation resistance. Through these endeavors, we hope to make significant advancements in the field and improve the outcomes for patients affected by DMG.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".