A PCR-Free Genome Detection of Mycobacterium Tuberculosis Complex in Clinical Samples using MWCNT/PPy/KHApNps Modified Electrochemical Nano-Biosensor
Bibliographic record
Abstract
In the perspective of tuberculosis (TB) disease, a necessary issue is the short interval of the correct diagnosis to planning and starting appropriate antibiotic treatment. So, at the first step for the diagnosis of Mycobacterium tuberculosis ( M. tb ) complex, a fast and reliable technique is necessary. The conventional methods have not the sensitivity, discriminatory power, and enough specificity required for immunocompromised persons. The friendly usage, availability, miniaturization, real-time, and continual monitoring properties of nanobiosensors, an interest attracted to them. The formation of a hybridization reaction in DNA biosensors can provide a possibility for point-of-care infectious detection of M. tb in regions with a high burden of tuberculosis. Here, we have developed a rapid, low-cost, PCR-free with high sensitivity and specificity DNA nanobiosensor for M. tb complex detection, using multi-welled carbon nanotubes, polypyrrole, and potassium-substituted hydroxyapatite (KHAp) nanoparticles. The nanocrystalline powder of KHAp was prepared by a facile alkoxide–based sol-gel method. A selectivity assay using Mycobacterium simiae , Rhodococcus , Nocardia , Corynebacterium , exhibited that the proposed biosensor was specific to M. tb complex. This biosensor showed an appropriate linear relationship (R 2 = 0.9906) between the increase in peak current and logarithmic target concentrations from 100 pM to 100 nM, with LOD and LOQ of 50.3 and 167.5 pM, respectively. Its suitable sensitivity was 335.914 μ A nM −1 cm −2 . The response time of this biosensor was 51.3 s. The proposed biosensor remained about 75% of its initial activity after 29 d. The potential application of the nano-biosensor was determined by spike-in experiments to obtain recoveries between 73% and 103.7%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".