Point-of-care Detection Platforms for Metabolic Biomarker and Infectious Disease
Bibliographic record
Abstract
The metabolic and protein biomarkers are the most significant detection targets for the point-of-care disease diagnosis and healthcare. The chronic diseases are among the world’s leading causes of mortality, and the COVID-19 pandemic is continuously burdening the global health, especially impacting the low-income, resource-limited regions. Accurate point-of-care detection of biomarkers is an important monitoring strategy for chronic disease development. Early detection of infectious disease outbreaks can reduce the ultimate size of the outbreak and the overall morbidity and mortality due to the disease, in which diagnosis plays an important role. As available diagnostic technologies require well-trained personnel, expensive equipment and complex assay procedure and consume a large amount of time in medical laboratories, there is an urgent need for low-cost portable platforms that can provide fast, accurate, and ideally multiplexed diagnosis of infectious diseases at the point of care. This thesis reports on the design, fabrication, and demonstration of electrochemical point-of-care detection systems for metabolic and protein biomarkers. Rapid results have been achieved by employing electrochemical enzymatic reactions on devices with simple fabrication and preparation. The systems have demonstrated high sensitivity and specificity comparable to state-of-the-art laboratory tests. The works reported in this thesis show strong potential to lead to portable systems for point-of-care diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".