Highly Sensitive and Specific Detection of Myoglobin in the Urine Based on Enhanced Photothermal Effect
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".