Biomimetic Trehalose Biosensor Using Gustatory Receptor (Gr5a) Expressed in Drosophila Cells and Ion-Sensitive Field-Effect Transistor
Bibliographic record
Abstract
The development of potential applications of biosensors using the sensory systems of vertebrates and invertebrates has progressed rapidly, especially in clinical diagnosis. The biosensor developed here involves the use of Drosophila cells expressing the gustatory receptor Gr5a and an ion-sensitive field-effect transistor (ISFET) sensor device. Gustatory receptor Gr5a is expressed abundantly in gustatory neurons and acts as a primary marker for tastants, especially sugar, in Drosophila. As a result, it could potentially serve as a good candidate for potential biomarkers of diseases in which the current knowledge of the cause and treatment is limited. The developed ISFET was based on the outstanding electrical characteristics of the metal–oxide–semiconductor field-effect transistor (MOSFET) with a subthreshold swing of 85 mV/dec, low leakage current of <10-12 and high on/off current ratio of 7.3×106. The SiO2 sensing membrane with a pH sensitivity of 34.9 mV/pH and drift rate 1.17 mV/h was sufficient for biosensing applications. In addition, the sensor device also showed significant compatibility with the Drosophila cells expressing Gr5a and their response to sugar, particularly trehalose. Moreover, the interactions between the transfected Drosophila cells and trehalose were consistent and reliable. This suggests that the developed ISFET sensor device could have potential use in the future as a screening device in 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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".