Recent Advances of Field-Effect Transistor Technology for Infectious Diseases
Bibliographic record
Abstract
Field-effect transistor (FET) biosensors have been intensively researched toward label-free biomolecule sensing for different disease screening applications. High sensitivity, incredible miniaturization capability, promising extremely low minimum limit of detection (LoD) at the molecular level, integration with complementary metal oxide semiconductor (CMOS) technology and last but not least label-free operation were amongst the predominant motives for highlighting these sensors in the biosensor community. Although there are various diseases targeted by FET sensors for detection, infectious diseases are still the most demanding sector that needs higher precision in detection and integration for the realization of the diagnosis at the point of care (PoC). The COVID-19 pandemic, nevertheless, was an example of the escalated situation in terms of worldwide desperate need for fast, specific and reliable home test PoC devices for the timely screening of huge numbers of people to restrict the disease from further spread. This need spawned a wave of innovative approaches for early detection of COVID-19 antibodies in human swab or blood amongst which the FET biosensing gained much more attention due to their extraordinary LoD down to femtomolar (fM) with the comparatively faster response time. As the FET sensors are promising novel PoC devices with application in early diagnosis of various diseases and especially infectious diseases, in this research, we have reviewed the recent progress on developing FET sensors for infectious diseases diagnosis accompanied with a thorough discussion on the structure of Chem/BioFET sensors and the readout circuitry for output signal processing. This approach would help engineers and biologists to gain enough knowledge to initiate their design for accelerated innovations in response to the need for more efficient management of infectious diseases like COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".