RNA‐Seq Reveals the Mechanism of Synergistic Hydrogen‐Chemotherapy Based on Active Magnesium Micromotors for Inhibiting Glioblastoma Recurrence by Modulating Tumor Microenvironment
Bibliographic record
Abstract
Abstract Postoperative recurrence of glioblastoma (GBM) is a key contributing factor to the unfavorable prognosis of patients. Chemotherapy has been extensively employed as a postoperative treatment for GBM; however, the produced drug resistance significantly undermines the chemotherapeutic efficacy. Herein, a multifunctional system based on magnesium micromotor (Mg‐Motor‐DOX) is designed and fabricated that can generate hydrogen gas in situ and actively deliver the chemotherapeutic drug doxorubicin (DOX). Utilizing a temperature‐sensitive hydrogel, Mg‐Motor‐DOX is administrated in situ to the residual cavity of the tumor after subtotal GBM resection. The produced H2 by the Mg‐water reaction not only propels the motion of motors but also functions as an antioxidant to effectively alleviate the neuroinflammation caused by GBM resection. The H2 bubbles create a pronounced vortex flow in situ, greatly enhancing the DOX penetration and the sensitivity of GBM cells to DOX. Therefore, synergistic hydrogen‐chemotherapy significantly inhibits the recurrence of the in situ GBM model. RNA‐Seq technology further elucidates the role of the strategy in modulating the tumor immune microenvironment via converting cold tumors into hot tumors, thereby establishing a theoretical foundation for the clinical implementation of synergistic hydrogen‐chemotherapy.
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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".