Electronic Mosquito: A semi-invasive glucose analysis device
Bibliographic record
Abstract
Diabetes currently affects 346 million people worldwide, and the numbers are continuously climbing [1]. Frequent self-monitoring of their glucose levels have been proven to reduce the risk of long-term complications and cost [2], but current methods suffer from low compliance, inaccuracy, or short periods of usability [3]. The electronic mosquito is an innovative design that is painless, minimally invasive, and it can automatically monitor blood glucose levels. The aim is to have a disposable 3cm x 3cm patch that can sample and analyze blood on a regular basis; it will be composed of hundreds of single-use microneedles. The patient merely has to replace the patch when needed; the replacement frequency has yet to be determined. It will have the ability to communicate wirelessly with an external processor, artificial pancreas, insulin injector, etc. The glucose sensor will consist of a micropotentiostat and an analog digital converter. A potentiostat consists of three electrodes: working, reference and counter (or auxiliary); it measures the current created by a chemical reaction at a working electrode. In this case, the current is caused by a reaction between glucose oxidase (GOx) and glucose. By maintaining the voltage between a reference electrode and the working electrode, the current flowing from the working electrode is directly proportional to the ‘resistance’ or the glucose concentration. The glucose concentration can be calculated using Michaelis-Menten kinetics. Currently a prototype of the e-Mosquito is being developed; it contains the needle and its actuator, and a wireless transceiver. Work on the glucose sensor is in progress. This device has the potential to significantly impact a diabetic’s quality of life, initially removing the need to manually test their own blood, to having an artificial pancreas that can automatically inject insulin when needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".