RDNA-01. TUBULIN AND MICROTUBULES AS MOLECULAR TARGETS FOR TTFIELD THERAPY
Bibliographic record
Abstract
Abstract TTField (Tumor-treating field) therapy utilizes low intensity intermediate frequency AC electric fields to reduce the spread of cancer. While it has attained FDA approval for the treatment of recurrent glioblastoma multiforme, the exact molecular targets of TTField therapy are not well understood. Microtubules are pipe-like polymers of the highly charged (–31 e) and strongly dipolar (dipole moment 1666 D) protein, α, β- tubulin. Studies on the electrical properties of microtubules have recently gained interest, with them being modelled as molecular targets of TTFields. Here, we experimentally show that while tubulin polymerized into microtubules leads to an increase in solution capacitance, unpolymerized tubulin has no appreciable effect. To the best of our knowledge, we present the first experimental quantification of the capacitance of a 20 μm-long microtubule. Using these results, we calculate the resonant frequency of a microtubule meshwork in a cell-like environment to be in the TTField regime. Our results utilize high ionic strength solutions and cell-like concentrations of tubulin to show the potential of microtubules as the targets of TTField action and as intracellular charge-storage devices. We conclude with a hypothesis of an electrically-tunable cell, where the dielectric properties of the cytoskeleton alter local and global charge storage and transport.
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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) | 0.002 | 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".