Investigating Programmed Cell Death and Tumor Invasion in a Three-Dimensional (3D) Microfluidic Model of Glioblastoma
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is a rapidly progressive and deadly form of brain tumor with a median survival rate of ~15 months. GBMs are hard to treat and significantly affect the patient's physical and cognitive abilities and quality of life. Temozolomide (TMZ)-an alkylating agent that causes DNA damage-is the only chemotherapy choice for the treatment of GBM. However, TMZ also induces autophagy and causes tumor cell resistance and thus fails to improve the survival rate among patients. Here, we studied the drug-induced programmed cell death and invasion inhibition capacity of TMZ and a mevalonate cascade inhibitor, simvastatin (Simva), in a three-dimensional (3D) microfluidic model of GBM. We elucidate the role of autophagy in apoptotic cell death by comparing apoptosis in autophagy knockdown cells (Atg7 KD) against their scrambled counterparts. Our results show that the cells were significantly less sensitive to drugs in the 3D model as compared to monolayer culture systems. An immunofluorescence analysis confirmed that apoptosis is the mechanism of cell death in TMZ- and Simva-treated glioma cells. However, the induction of apoptosis in the 3D model is significantly lower than in monolayer cultures. We have also shown that autophagy inhibition (Atg7 KD) did not change TMZ and Simva-induced apoptosis in the 3D microfluidic model. Overall, for the first time in this study we have established the simultaneous detection of drug induced apoptosis and autophagy in a 3D microfluidic model of GBM. Our study presents a potential ex vivo platform for developing novel therapeutic strategies tailored toward disrupting key molecular pathways involved in programmed cell death and tumor invasion in glioblastoma.
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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) | 0.000 | 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".