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Record W2528591354 · doi:10.1038/srep34443

Multiphoton imaging reveals that nanosecond pulsed electric fields collapse tumor and normal vascular perfusion in human glioblastoma xenografts

2016· article· en· W2528591354 on OpenAlexfundno aff
Sylvia M. Bardet, Lynn Carr, Malak Soueid, Delia Arnaud‐Cormos, Philippe Lévêque, Rodney P. O’Connor

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsnot available
FundersUniversité de LimogesMinistère de l'Enseignement Supérieur et de la RechercheAgence Nationale de la RechercheUniversity of AlbertaSlovenská Akadémia Vied
KeywordsGlioblastomaPerfusionNanosecondPathologyCancer researchIntravital microscopyPulse (music)OrganoidMedicineBiomedical engineeringChemistryBiologyMicrocirculationNeuroscienceRadiologyLaserVoltageOpticsPhysics

Abstract

fetched live from OpenAlex

Despite the biomedical advances of the last century, many cancers including glioblastoma are still resistant to existing therapies leaving patients with poor prognoses. Nanosecond pulsed electric fields (nsPEF) are a promising technology for the treatment of cancer that have thus far been evaluated in vitro and in superficial malignancies. In this paper, we develop a tumor organoid model of glioblastoma and apply intravital multiphoton microscopy to assess their response to nsPEFs. We demonstrate for the first time that a single 10 ns, high voltage electric pulse (35-45 kV/cm), collapses the perfusion of neovasculature, and also alters the diameter of capillaries and larger vessels in normal tissue. These results contribute to the fundamental understanding of nsPEF effects in complex tissue environments, and confirm the potential of nsPEFs to disrupt the microenvironment of solid tumors such as glioblastoma.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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)0.0010.000

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 head0.008
GPT teacher head0.254
Teacher spread0.247 · 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 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

Citations27
Published2016
Admission routes1
Has abstractyes

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