Portable Sensing Devices for Detection of COVID-19: A Review
Bibliographic record
Abstract
The coronavirus pandemic is the most challenging incident that people have faced in recent years. Despite the time-consuming and expensive conventional methods, point-of-care diagnostics have a crucial role in deterrence, timely detection, and intensive care of the disease's progress. Hence, this detrimental health emergency persuaded researchers to accelerate the development of highly-scalable diagnostic devices to control the propagation of the virus even in the least developed countries. The strategies exploited for detecting COVID-19 stem from the already designed systems for studying other maladies, particularly viral infections. The present report reviews not only the novel advances in portable diagnostic devices for recognizing COVID-19, but also the previously existing biosensors for detecting other viruses. It discusses their adaptability for identifying surface proteins, whole viruses, viral genomes, host antibodies, and other biomarkers in biological samples. The prominence of different types of biosensors such as electrochemical, optical, and electrical for detecting low viral loads have been underlined. Thus, it is anticipated that this review will assist scientists who have embarked on a competition to come up with more efficient and marketable in-situ test kits for identifying the infection even in its incubation time without sample pretreatment. Finally, a conclusion is provided to highlight the importance of such an approach for monitoring people to combat the spread of such contagious diseases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".