Biosensors; nanomaterial-based methods in diagnosing of Mycobacterium tuberculosis
Bibliographic record
Abstract
Diagnosis of Mycobacterium tuberculosis (Mtb) before the progression of pulmonary infection can be very effective in its early treatment. The Mtb grows so slowly that it takes about 6–8 weeks to be diagnosed even using sensitive cell culture methods. The main opponent in tuberculosis (TB) and nontuberculous mycobacterial (NTM) epidemiology, like in all contagious diseases, is to pinpoint the source of infection and reveal its transmission and dispersion ways in the environment. It is crucial to be able to distinguish and monitor specific mycobacterium strains in order to do this. In food analysis, clinical diagnosis, environmental monitoring, and bioprocess, biosensing technologies have been improved to manage and detect Mtb. Biosensors are progressively being considered pioneering tools for point-of-care diagnostics in Mtb discoveries. In this review, we present an epitome of recent developments of biosensing technologies for M. tuberculosis detection, which are categorized on the basis of types of electrochemical, Fluorescent, Photo-thermal, Lateral Flow, Magneto-resistive, Laser, Plasmonic, and Optic biosensors.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".