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Record W4311520871 · doi:10.1109/jsen.2022.3218816

Highly Sensitive and Specific Detection of Myoglobin in the Urine Based on Enhanced Photothermal Effect

2022· article· en· W4311520871 on OpenAlexfundno aff
Yuansheng Yin, Yinping Miao, Yi Li, Yibo Zheng

Bibliographic record

VenueIEEE Sensors Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsnot available
FundersTianjin Research Innovation Project for Postgraduate StudentsNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaMinistry of Natural Resources
KeywordsPhotothermal therapyMyoglobinBiomoleculeDetection limitMaterials scienceSIGNAL (programming language)Photothermal effectUrineChemistryAnalytical Chemistry (journal)NanotechnologyChromatographyComputer science

Abstract

fetched live from OpenAlex

Acute myocardial infarction can be diagnosed by measurement of myoglobin concentration in urine. In this article, a label-free sensor is proposed based on the photothermal effect of biomolecules, which is used for the immediate detection of myoglobin concentration in urine. The sensor was fabricated by a tapered microfiber (TMF) coated with the ultraviolet-curable polymer (UVCP) that has a high thermo-optical coefficient. The photothermal signal of biomolecules is only related to their selective absorption, while the scattering and reflection losses do not generate the photothermal signal. Therefore, specific identification could be realized by detecting the photothermal signal of myoglobin biomolecules. The results showed that the UVCP could enhance the detection sensitivity of photothermal signal due to the high thermo-optic coefficient. The sensitivity of the device is −267.652 nm/(mg/mL) in the concentration range of 0.0001–0.10 mg/mL, the limit of detection (LOD) is 74.7 ng/mL, and the response time is only 30 s. It can meet the demand for detecting the myoglobin concentrations in the urine analysis of related diseases, which has potential applications in the early diagnosis and quantitative analysis of conditions.

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 score0.003
Threshold uncertainty score0.252

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 head0.006
GPT teacher head0.268
Teacher spread0.262 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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