An Integrated Sweat Sensor for Synchronous Detection of Multiple Atherosclerosis Biomarkers
Bibliographic record
Abstract
Atherosclerosis conditions are often assessed in the clinic by measuring blood viscosity, blood flow, and blood lesion levels. In alignment with precision medicine, it is essential to develop convenient and noninvasive approaches for atherosclerosis diagnostics. Herein, an integrated electrochemical sensor was successfully demonstrated for simultaneously detecting cholesterol, transferrin, and K + in sweat, all biomarker indicators of atherosclerosis. The sensing substrate was based on carbon quantum dots integrated within multiwalled carbon nanotubes, creating a hybrid framework with low electron transfer resistance and highly efficient electron transfer rate, yielding a highly electrochemical active platform for ultrasensitive detection of trace sweat biomarkers. To ensure specificity to corresponding targets, the sensing mechanisms were based on molecular recognition reactions of cholesterol and β-cyclodextrin, transferrin and molecular cavities, and K + and ion-selective permeation membrane. Moreover, the integrated nonenzymatic sensor exhibited excellent long-term stability. Furthermore, the practical utility of the sensor was successfully demonstrated by the simultaneous detection of three atherosclerosis biomarkers in sweat from volunteers who underwent predesigned daily activities. The sensor shows promise for convenient indexing of atherosclerosis conditions in a noninvasive way.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".