Calcium Signalling in Glioblastoma Networks of Different Topologies and Possible Treatments
Bibliographic record
Abstract
Glioblastoma cells form connected cell networks, utilizing tumor microtubes to transmit calcium between cells. A new cell type called "periodic cell" is integral in sustaining calcium signalling in a glioblastoma network. Periodic cells are rare, can sustain consistent intracellular calcium transients, are likely to have KCa3.1 pumps, and have on average more tumor microtubes than other glioma cells. Here we adapt an ordinary differential equation model for intracellular as well as intercellular calcium signalling and apply it to a large glioma cell network. Using the model, three main hypotheses for the driving mechanism of periodic cells were tested: 1.\ a fixed and elevated IP$_3$ concentration, 2.\ added benefit from influx of calcium due to KCa3.1 pumps, or 3.\ oscillation in calcium influx into the cell through the plasma membrane. All three hypotheses yield similar calcium oscillation patterns resembling the trends seen in the data of Hausmann et al. 2023. In vivo, glioma networks were shown to have small-world and scale free network properties. We apply our model to small-world, scale-free and random networks. For these networks, we test how communication is inhibited through removal of cells, removal of tumor microtubes, and inhibition of KCa3.1 pumps. All three network types were more vulnerable to random cell damage than to random TM damage. We find that inhibition of KCa3.1 pumps can have a significant impact on the inhibition of network communication, however, to fully degrade the calcium signalling network, all periodic cells must be eradicated, confirming experimental observations.
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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 | 0.001 |
| 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 | 0.001 | 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".