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Record W2983438087 · doi:10.1093/neuonc/noz175.196

CSIG-26. IS INTRINSIC APOPTOSIS THE SIGNALING PATHWAY ACTIVATED BY TUMOR-TREATING FIELDS FOR GLIOBLASTOMA?

2019· article· en· W2983438087 on OpenAlexaffabout
Kristen W. Carlson, Jack A. Tuszyński, Zéev Bomzon

Bibliographic record

VenueNeuro-Oncology · 2019
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsApoptosisMicrotubuleDexamethasoneCell cycleChemistryGliomaCancer researchCell cycle checkpointCell biologyMedicineInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Increasingly, tumor-treating fields (TTFields, 2 V/cm, 200 kHz) are accepted as the fourth treatment modality for glioblastoma. Evidence shows that substituting non-steroidal inflammation control (celecoxib) for dexamethasone increases overall survival from 4.8 to 11.0 months, and more recently, up to 60 months. Toward explaining TTFields mechanism of action (MoA), our numerical simulations indicate that TTFields disrupt functionality of microtubules, which in turn trigger the intrinsic apoptotic pathway independent of cell cycle checkpoints. We present the theory and empirical evidence. 1) TTFields act similarly to chemotherapeutic ‘spindle poisons’ by interfering with microtubule (MT) polymerization, increasing free tubulin by 20% in relative terms; 2) Finite element modeling shows TTFields amplify electric field strength, in accord with empirical results, a) along the MT when aligned with the cell axis, where field strength exceeds 10–16 N required to disrupt motor protein transit, and b) 15x at MT ends when orthogonal to cell axis; 3) Either through producing excess free tubulin, which may block voltage-dependent anion channels, or direct effects on the mitochondrial inner and outer membranes, TTFields inhibit expression of pro-survival protein Bcl-2; 4) Decreased Bcl-2 expression activates the intrinsic apoptotic pathway in a novel cell-cycle-checkpoint and caspase-independent manner; 5) Patients using low (< 4.1 mg/day) vs. high (>4.1 mg/day) dexamethasone doses experienced an average 8.7 vs. 3.2 months OS and up to 60 months; 6) Numerous studies in both brain and other tissues show that dexamethasone a) promotes extrinsic, immune-system apoptosis and b) inhibits intrinsic, Bcl-2/Bax mediated apoptosis; 7) Downstream effects of intrinsic apoptosis are remarkably similar to empirically-observed effects of TTFields on tumor cells. Research supported by Novocure Ltd. Dept of Neurosurgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA USA. carlsokw@bidmc.harvard.edu. Dept of Physics, University of Alberta, Edmonton, Canada, Novocure Ltd., Haifa, Israel.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.227
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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