Microfluidic designs and techniques using lab-on-a-chip devices for pathogen detection for point-of-care diagnostics
Bibliographic record
Abstract
Effective pathogen detection is an essential prerequisite for the prevention and treatment of infectious diseases. Despite recent advances in biosensors, infectious diseases remain a major cause of illnesses and mortality throughout the world. For instance in developing countries, infectious diseases account for over half of the mortality rate. Pathogen detection platforms provide a fundamental tool in different fields including clinical diagnostics, pathology, drug discovery, clinical research, disease outbreaks, and food safety. Microfluidic lab-on-a-chip (LOC) devices offer many advantages for pathogen detection such as miniaturization, small sample volume, portability, rapid detection time and point-of-care diagnosis. This review paper outlines recent microfluidic based devices and LOC design strategies for pathogen detection with the main focus on the integration of different techniques that led to the development of sample-to-result devices. Several examples of recently developed devices are presented along with respective advantages and limitations of each design. Progresses made in biomarkers, sample preparation, amplification and fluid handling techniques using microfluidic platforms are also covered and strategies for multiplexing and high-throughput analysis, as well as point-of-care diagnosis, are discussed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".