A comprehensive review on the applications of nano-biosensor-based approaches for non-communicable and communicable disease detection
Bibliographic record
Abstract
The outstretched applications of biosensors in diverse domains has become the reason for their attraction for scientific communities. Because they are analytical devices, they can detect both quantitative and qualitative biological components through the generation of detectable signals. In the recent past, biosensors witnessed significant changes and developments in their design as well as features. Nanotechnology has revolutionized sensing phenomena by increasing biodiagnostic capacity in terms of specificity, size, and cost, resulting in exceptional sensitivity and flexibility. The steep increase of non-communicable diseases across the world has emerged as a matter of concern. In parallel, the abrupt outbreak of communicable diseases poses a serious threat to mankind. For decreasing the morbidity and mortality associated with various communicable and non-communicable diseases, early detection and subsequent treatment are indispensable. Detection of different biological markers generates quantifiable signals that can be electrochemical, mass-based, optical, thermal, or piezoelectric. Speculating on the incumbent applicability and versatility of nano-biosensors in large disciplines, this review highlights different types of biosensors along with their components and detection mechanisms. Moreover, it deals with the current advancements made in biosensors and the applications of nano-biosensors in detection of various non-communicable and communicable diseases, as well as future prospects of nano-biosensors for diagnostics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".