Recent Progress and Challenges on the Microfluidic Assay of Pathogenic Bacteria Using Biosensor Technology
Bibliographic record
Abstract
Microfluidic technology is one of the new technologies that has been able to take advantage of the specific properties of micro and nanoliters, and by reducing the costs and duration of tests, it has been widely used in research and treatment in biology and medicine. Different materials are often processed into miniaturized chips containing channels and chambers within the microscale range. This review (containing 117 references) demonstrates the significance and application of nanofluidic biosensing of various pathogenic bacteria. The microfluidic application devices integrated with bioreceptors and advanced nanomaterials, including hyperbranched nano-polymers, carbon-based nanomaterials, hydrogels, and noble metal, was also investigated. In the present review, microfluid methods for the sensitive and selective recognition of photogenic bacteria in various biological matrices are surveyed. Further, the advantages and limitations of recognition methods on the performance and efficiency of microfluidic-based biosensing of photogenic bacteria are critically investigated. Finally, the future perspectives, research opportunities, potential, and prospects on the diagnosis of disease related to pathogenic bacteria based on microfluidic analysis of photogenic bacteria are provided.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".