Electrochemical and surface plasmon insulin assays on clinical samples
Bibliographic record
Abstract
Diabetes is a complex immune disorder that requires extensive medical care beyond glycemic control. Recently, the prevalence of diabetes, particularly type 1 diabetes (T1D), has significantly increased from 5% to 10%, and this has affected the health-associated complication incidences in children and adults. The 2012 statistics by the American Diabetes Association reported that 29.1 million Americans (9.3% of the population) had diabetes, and 86 million Americans (age ≥20 years, an increase from 79 million in 2010) had prediabetes. Personalized glucometers allow diabetes management by easy monitoring of the high millimolar blood glucose levels. In contrast, non-glucose diabetes biomarkers, which have gained considerable attention for early prediction and provide insights about diabetes metabolic pathways, are difficult to measure because of their ultra-low levels in blood. Similarly, insulin pumps, sensors, and insulin monitoring systems are of considerable biomedical significance due to their ever-increasing need for managing diabetic, prediabetic, and pancreatic disorders. Our laboratory focuses on developing electrochemical immunosensors and surface plasmon microarrays for minimally invasive insulin measurements in clinical sample matrices. By utilizing antibodies or aptamers as the insulin-selective biorecognition elements in combination with nanomaterials, we demonstrated a series of selective and clinically sensitive electrochemical and surface plasmon immunoassays. This review provides an overview of different electrochemical and surface plasmon immunoassays for insulin. Considering the paramount importance of diabetes diagnosis, treatment, and management and insulin pumps and monitoring devices with focus on both T1D (insulin-deficient condition) and type 2 diabetes (insulin-resistant condition), this review on insulin bioassays is timely and significant.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".