A 2D transition-metal dichalcogenide MoS<sub>2</sub> based novel nanocomposite and nanocarrier for multiplex miRNA detection
Bibliographic record
Abstract
Nanoscale MoS2 has attracted extensive attention for sensing due to its superior properties. This study outlines a microfluidic and electrochemical biosensing methodology for the multiplex detection of paratuberculosis-specific miRNAs. Herein, we report the synthesis of MoS2 nanosheets decorated with a copper ferrite (CuFe2O4) nanoparticle composite and molecular probe immobilized MoS2 nanosheets as nanocarriers for the electrochemical detection of miRNAs. Paratuberculosis is a bacterial infection of the intestinal tract of dairy cattle, and is a cause of substantial economic and animal losses all over the world. The designed biosensing electrode was modified with the synthesized MoS2-CuFe2O4 nanocomposites for a highly amplified signal generation. Additionally, selective detection of miRNAs was accomplished by functionalizing the MoS2 nanosheets with a miRNA-specific biotin-tagged thiolated molecular probe and ferrocene thiol. The presence of target miRNA triggered the opening of the molecular probe present on the nanocarriers. The interaction of the molecular probe and miRNA resulted in an increase in the electrochemical signal from ferrocene. The optimized microfluidic biosensor was employed to detect a range of miRNA concentrations from the target analyte. Using square wave voltammetric analysis, a detection limit of 0.48 pM was calculated, with a detection range of 1 pM to 1.5 nM. The application of the biosensor was also assessed by detecting miRNAs in spiked serum and positive clinical samples. The developed nanomaterial enabled biosensor easily discriminated between the target miRNAs and other interfering molecules. The developed microfluidic biosensor has the potential to be used as a point-of-care, miRNA based diagnostic tool for paratuberculosis in dairy cows.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".