Handheld impedance based biosensor system for glucose monitoring
Bibliographic record
Abstract
ABSTRACT Biosensors play an important role in various applications including environmental monitoring, food and beverage industry, biomedical/clinical monitoring, and national security. Biosensors are devices or systems that monitor various physical, chemical or biological parameters in the surrounding environment and provide representative signals that can be measured or stored. The rapid developments in semiconductor technologies have sprouted newer integrated sensor technologies leading to the development of biosensor microsystems which offer the advantages of easy to use, point-of-care, low power and low cost. These microsystems allow highly sensitive and rapid detection with low sample volumes in cases of disease epidemics. This thesis focuses on the development of integrated, accurate, rapid and continuous monitoring glucose biosensors. With growing numbers of aging population and rising obesity rates, chronic diseases continue to be a major health problem in Canada and around the world. For instance, diabetes is a chronic disease which currently affects around 3 million Canadians. The cost of health care for diabetes and its complications amount to about $9 billion a year for Canada. In diabetes, the body either does not produce or ineffectively uses insulin, the hormone which regulates movement of glucose from the blood to the cells. It is generally agreed that the future of diabetes management depends on the success in the development of sensor-based continuous glucose monitoring systems. Although continuous glucose determination is presently available, it has evolved from single glucose determination methodology, which was not primary designed for continuous glucose sensing; hence several aspects of present day glucose sensors are not optimal. Notable shortcomings arethat they are not reagent-less or non-replenishable, and often are multiple enzyme-coupled, which can be complicated and prone to
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".