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Record W2050237133 · doi:10.1088/0031-9155/46/4/306

Monitoring of cell and tissue responses to photodynamic therapy by electrical impedance spectroscopy

2001· article· en· W2050237133 on OpenAlexaff
Andrea Molckovsky, Brian C. Wilson

Bibliographic record

VenuePhysics in Medicine and Biology · 2001
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsOntario Institute for Cancer Research
FundersNational Cancer InstituteDartmouth College
KeywordsMaterials sciencePhotodynamic therapyPermittivitySpheroidElectrical impedanceIn vivoDielectric spectroscopyBiophysicsNecrosisBiomedical engineeringConductivityChemistryElectrodePathologyIn vitroOptoelectronicsMedicineDielectricBiologyPhysics

Abstract

fetched live from OpenAlex

Electrical impedance spectroscopic (EIS) monitoring of photodynamic therapy (PDT) was investigated in vivo in rat liver and in vitro in multicellular spheroids. Liver impedance was continuously measured with two needle electrodes before, during and up to 3 hours following Photofrin-PDT. EIS spectra were altered immediately after PDT, with significant changes in conductivity at approximately 10 kHz, and in permittivity at approximately 30 kHz and 1 MHz. The change in permittivity at high frequencies was related to oedema, while low-frequency effects were attributed to cell necrosis and vascular changes. Photofrin-PDT-treated spheroids showed dose-dependent decreases in permittivity and conductivity at frequencies above 10 and 100 kHz, respectively. Histology showed concomitant development of a damaged rim containing sparsely distributed cells with compromised membranes and lightly staining cytoplasm. Different EIS responses to apoptotic versus necrotic modes of cell death further verified the sensitivity of impedance to purely cellular changes in the spheroid model. In conclusion, EIS sensitivity to PDT-induced damage, at both the cell and tissue level, varies with dose and time, and can be correlated qualitatively to biological changes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.047
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.075
GPT teacher head0.404
Teacher spread0.329 · 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

Citations25
Published2001
Admission routes1
Has abstractyes

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