Electrochemical-based biosensors for detection of<i>Mycobacterium tuberculosis</i>and tuberculosis biomarkers
Bibliographic record
Abstract
Early detection of tuberculosis (TB) reduces the interval between infection and the beginning of treatment. However, commercially available tests cannot discriminate between BCG-vaccinated healthy persons and patients. Also, they are not suitable to be used for immunocompromised persons. In recent years, biosensors have attracted great attention due to their simple utility, accessibility, and real-time outputs. These sensors are increasingly being considered as pioneering tools for point-of-care diagnostics in communities with a high burden of TB and limited accessibility to reference laboratories. Among other types of biosensors, the electrochemical sensors have the advantages of low-cost operation, fast processing, simultaneous multi-analyte analyzing, operating with turbid samples, comparable sensitivity and readily available miniaturization. Electrochemical biosensors are sub-divided into several categories including: amperometric, impedimetric, potentiometric, and conductometric biosensors. The biorecognition element in electrochemical biosensors is usually based on antibodies (immunosensors), DNAs or PNAs (genosensors), and aptamers (aptasensors). In either case, whether an interaction of the antigen–antibody/aptamer or the hybridization of probe with target mycobacterial DNA is detected, a change in the electrical current occurs that is recorded and displayed as a plot. Therefore, impedimetric-based methods evaluate resistance to electron transfer toward an electrode by a Nyquist plot and amperometric/voltammetric-based methods weigh the electrical current by means of cyclic voltammetry, square wave voltammetry, and differential pulse voltammetry. Electrochemical biosensors provide a promising scope for the new era of diagnostics. As a consequence, they can improve detection of Mycobacterium tuberculosis traces even in attomolar scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".