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A Microfluidic Cytometer for Quantitative Evaluation of Radiation Dose by γ-H2AX

2014· article· en· W2051736932 on OpenAlexaff
Jun Sheng Wang, You Nan Song, Jin Yang Sun, Hui Chu, Jin Hu Jiang, Xin Xiang Pan, Ye Sun, Dong Qing Li

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsDosimeterMicrofluidicsMaterials scienceIonizing radiationRadiationIrradiationFluorescenceBiomedical engineeringIntensity (physics)OpticsOptoelectronicsNanotechnologyMedicinePhysics

Abstract

fetched live from OpenAlex

Evaluation of radiation dose is very important for the detection of radiation damage. γ-H2AX is a popular biological dosimeter to evaluate the radiation effect. Typically, bulky and expensive commercial flow cytometers are used to detect γ-H2AX. This paper presents a miniaturized and highly sensitive cytometer using a microfluidic chip for evaluating the radiation dose by detecting the mean immunofluorescence intensity of γ-H2AX. A compact optical focusing system and a shift-phase differential amplifier are designed to improve the detection sensitivity. Sample lymphocyte cells are stained by FITC fluorescent dye after being irradiated by UVC. Comparison experiments between the developed miniature cytometer and a commercial flow cytometer were conducted under different radiation doses. The developed microfluidic cytometer can also demonstrate a good linear correlation between the measured fluorescence intensity and the irradiation dose with a detection limit similar to that of the commercial flow cytometer. The developed cytometer can evaluate quantitatively the radiation dose by the mean fluorescence intensity of γ-H2AX with a significantly smaller amount of blood samples than a commercial flow cytometer.

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 score0.057
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.307
Teacher spread0.281 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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