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Record W2805724500 · doi:10.1038/s41598-018-25639-7

Preclinical outcomes of Intratumoral Modulation Therapy for glioblastoma

2018· article· en· W2805724500 on OpenAlexafffund
Andrea R. Di Sebastiano, Andrew Deweyert, Simon M. Benoit, Erin Iredale, Hu Xu, Cleusa De Oliveira, Eugene Wong, Susanne Schmid, Matthew O. Hebb

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsWestern University
FundersSchulich School of Medicine and Dentistry, Western UniversitySchulich School of Medicine and DentistryCancer Research Society
KeywordsGlioblastomaMedicineOncologyGliomaBrain tumorCancer researchInternal medicineBioinformaticsPathologyBiology

Abstract

fetched live from OpenAlex

Glioblastoma (GBM) is the leading cause of high fatality cancer arising within the adult brain. Electrotherapeutic approaches offer new promise for GBM treatment by exploiting innate vulnerabilities of cancer cells to low intensity electric fields. This report describes the preclinical outcomes of a novel electrotherapeutic strategy called Intratumoral Modulation Therapy (IMT) that uses an implanted stimulation system to deliver sustained, titratable, low intensity electric fields directly across GBM-affected brain regions. This pilot technology was applied to in vitro and animal models demonstrating significant and marked reduction in tumor cell viability and a cumulative impact of concurrent IMT and chemotherapy in GBM. No off target neurological effects were observed in treated subjects. Computational modeling predicted IMT field optimization as a means to further bolster treatment efficacy. This sentinel study provides new support for defining the potential of IMT strategies as part of a more effective multimodality treatment platform for GBM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.341
Teacher spread0.279 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2018
Admission routes2
Has abstractyes

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