MétaCan
Menu
Back to cohort
Record W2140384109 · doi:10.1021/ac101524v

Deconvoluting Topography and Spatial Physiological Activity of Live Macrophage Cells by Scanning Electrochemical Microscopy in Constant-Distance Mode

2010· article· en· W2140384109 on OpenAlexafffund
Xiaocui Zhao, Piotr M. Diakowski, Zhifeng Ding

Bibliographic record

VenueAnalytical Chemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryScanning electrochemical microscopyBiophysicsMicroscopyOrganelleReactive oxygen speciesAmperometryConstant currentConstant (computer programming)NucleusElectrochemistryRedoxAnalytical Chemistry (journal)Current (fluid)NanotechnologyElectrodeCell biologyBiochemistryOpticsChromatographyInorganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Detection of reactive oxygen species (ROS) released from live macrophage cells (RAW264.7) without any addition of external redox mediators using constant-height and constant-distance mode scanning electrochemical microscopy (SECM) was presented in this Letter. The successful separation of the ROS profile from the topography of cells in the physiological condition was demonstrated by recording the amperometric current and probing position in the z-direction along with lateral coordinates at each pixel where an alternating current (AC) was kept constant. It was discovered that the nucleus region of the cell releases more ROS than other organelle regions and the height of the cell is approximately 4.8 μm. To our best knowledge, this work reports the first spatially monitored ROS release without the influence of cell morphology using SECM.

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: Methods · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.005
GPT teacher head0.248
Teacher spread0.243 · 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
GenreMethods

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

Citations70
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicElectrochemical Analysis and ApplicationsFrench-language works237,207