RTHP-14. TUMOR-TREATING FIELDS FOR GLIOBLASTOMA: NUMERICAL SIMULATION EXPLORES SUB-CELLULAR MECHANISMS
Bibliographic record
Abstract
Tumor Treating Fields (TTFields) 100–500 kHz electric fields at ~1–4 V/cm exert an anti-mitotic effect on cancer cells. Our goal is to uncover TTFields mechanism by numerically modeling their effects on sub-cellular structures, notably microtubules (MTs). We built a geometrically accurate finite element model in COMSOL Multiphysics (tm) of the MT and its micro-environment as a layered 27 nm-diameter cylinder: the inner lumen; 13 helical strands of alpha-beta tubulin dimers; C-termini; counter-ion layer; and an outer non-conductive Bjerrum layer. Modeling current density induced in each layer by TTFields for MTs varying in length from 1 10 µm showed that MTs act as electrical shunts conducting electric current within them. The resulting strongest current flows through the counter-ion layer surrounding the C-termini and energy density in this layer exceeds the level likely to disrupt the motor protein kinesin walk along the C-termini. The energy density is highest predicted at 1e-20 Joules when both the field and the MTs are aligned with the cell axis, in accord with in vitro experiments. A second mechanism predicted by our model is disruption of the foot of kinesin, released from its C-terminus contact by ATP (1e-19 Joules). The final phase of the walk is driven by thermal buffeting of the forward foot randomly positioning it near enough to the C-terminus for electrostatic forces to bind it. A stall force ~1e-19 - 1e-16 N from TTFields would prevent diffusion and disrupt the kinesin walk. Our modeling predicts that TTFields in cytosol induce electric currents along MTs that are strong enough to disrupt key cellular functions such as the kinesin walk and C-termini transitions, which are crucial for motor protein transport. Hence, TTFields disrupt the most delicate mechanisms involved in the carefully-orchestrated succession of steps in mitosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".