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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".