An electrochemical biosensor for the detection of Mycobacterium tuberculosis DNA from sputum and urine samples
Bibliographic record
Abstract
Tuberculosis (TB) is a major global public health problem with high mortality and morbidity. In low-middle income countries (LMIC) a large number of respiratory symptomatic cases that require TB screening per year demands more accurate, fast and affordable testing for TB diagnostics. Sputum smear is the initial screening test in LMICs, however, its sensitivity is limited in patients with low sputum bacilli load. The same limitation is observed in the currently available molecular tests. We designed, standardized and evaluated an electrochemical biosensor that detects the highly specific DNA insertion element 6110 (IS6110). A PCR amplified DNA product is hybridized on the surface of the working electrode built on FTO-Glass with immobilized specific DNA probes, after which cyclic voltammetry is performed with an Ag/AgCl reference electrode and a platinum counter electrode. The response of the sensor was measured by the ratio (cathodic peak current of the hybridized sensor) / (cathodic peak current of the non-hybridized sensor). We tested the biosensor, using positive hybridization control sequences, genomic DNA extracted from M. tuberculosis strains and sputum of TB patients, and extracted DNA from the urine of healthy controls spiked with M. tuberculosis DNA. This biosensor was effective for the detection of M. tuberculosis DNA with a detection limit of 16 fM in sputum sample and 1 fM in spiked urine samples. The low cost and the relatively brief duration of the assay make this an important TB screening tool in the fight against tuberculosis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".