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Record W2301948148 · doi:10.1038/srep23165

A new hand-held microfluidic cytometer for evaluating irradiation damage by analysis of the damaged cells distribution

2016· article· en· W2301948148 on OpenAlexaff
Junsheng Wang, Zhi-Qiang Fan, Yile Zhao, Younan Song, Hui Chu, Wen‐Dong Song, Yongxin Song, Xinxiang Pan, Yeqing Sun, Dongqing Li

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Waterloo
FundersDalian Maritime UniversityNational Natural Science Foundation of China
KeywordsConfocalMicrofluidicsBiomedical engineeringIrradiationRadiationConfocal microscopyMaterials scienceFluorescenceFluorescence microscopeImmunofluorescenceComputer scienceNanotechnologyOpticsBiologyCell biologyMedicinePhysicsImmunology

Abstract

fetched live from OpenAlex

Space radiation brings uneven damages to cells. The detection of the distribution of cell damage plays a very important role in radiation medicine and the related research. In this paper, a new hand-held microfluidic flow cytometer was developed to evaluate the degree of radiation damage of cells. The device we propose overcomes the shortcomings (e.g., large volume and high cost) of commercial flow cytometers and can evaluate the radiation damage of cells accurately and quickly with potential for onsite applications. The distribution of radiation-damaged cells is analyzed by a simultaneous detection of immunofluorescence intensity of γ-H2AX and resistance pulse sensor (RPS) signal. The γ-H2AX fluorescence intensity provides information of the degree of radiation damage in cells. The ratio of the number of cells with γ-H2AX fluorescence signals to the total numbers of cells detected by RPS indicates the percentage of the cells that are damaged by radiation. The comparison experiment between the developed hand-held microfluidic flow cytometer and a commercial confocal microscope indicates a consistent and comparable detection performance.

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)0.000
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 score0.166
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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