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Record W1871470037 · doi:10.1039/c5an01148e

Sensing cisplatin-induced permeation of single live human bladder cancer cells by scanning electrochemical microscopy

2015· article· en· W1871470037 on OpenAlexafffund
Mengni Zhang, Zhifeng Ding, Yi‐Tao Long

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCisplatinMembranePermeationBiophysicsCancer cellChemistryApoptosisMembrane permeabilityScanning electrochemical microscopyPermeability (electromagnetism)Fluorescence microscopeCell membraneCancerBiochemistryFluorescenceElectrochemistryBiologyMedicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Cisplatin is a widely used anti-cancer agent, which was believed to trigger apoptosis of cancer cells by forming DNA adducts. However, recent studies evidenced a cisplatin-induced extrinsic apoptotic pathway through interaction with plasma membranes. We present quantitative time-course imaging of cisplatin-induced permeation of ferrocenemethanol to single live human bladder cancer cells (T24) using scanning electrochemical microscopy (SECM). Simultaneous quantification of cellular topography and membrane permeability was realized by running SECM in the depth scan mode. It was demonstrated that the acute addition of cisplatin to the outer environment of T24 cells immediately induced membrane permeability change in 5 min, which indicated a loosened structure of the cellular membrane upon cisplatin dosage. The cisplatin-induced permeation of T24 cells might be a one-step action, an extrinsic mechanism, since the cell response was quick, and no continuous increase in the membrane permeability was observed. The time-lapse SECM depth scan method provided a simple and facile way of monitoring cisplatin-induced membrane permeability changes. Our study is anticipated to lead to a methodology of screening anti-cancer drugs through their interactions with live cells.

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 score0.003
Threshold uncertainty score0.500

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 head0.024
GPT teacher head0.289
Teacher spread0.265 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

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