A Low-Cost Handheld Impedimetric Biosensing System for Rapid Diagnostics of SARS-CoV-2 Infections
Bibliographic record
Abstract
Current laboratory diagnostic approaches for virus detection give reliable results, but they require a lengthy procedure, trained personnel, and expensive equipment and reagents; hence, they are not a suitable choice for home monitoring purposes. This paper addresses this challenge by developing a portable impedimetric biosensing system for the identification of COVID-19 patients. This sensing system has two main parts: a throwaway two-working electrode (2-WE) strip and a novel read-out circuit, specifically designed for simultaneous signal acquisition from both working electrodes. Highly reliable electrochemical signal tracking from multiplex immunosensors provides a potential for flexible and portable multi-biomarker detection. The electrodes' surfaces were functionalized with SARS-CoV-2 Nucleocapsid Antibody enabling the selective detection of Nucleocapsid protein (N-protein) along with self-validation in the clinical nasopharyngeal swab specimens. The proposed programmable highly sensitive impedance read-out system allows for a wide dynamic detection range, which makes the sensor capable of detecting N-protein concentrations between 0.116 and 10,000 pg/mL. This lightweight and economical read-out arrangement is an ideal prospect for being mass-produced, especially during urgent pandemic situations. Also, such an impedimetric sensing platform has the potential to be redesigned for targeting not only other infectious diseases but also other critical disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".