Microfluidic methods for the diagnosis of acute respiratory tract infections
Bibliographic record
Abstract
Acute respiratory tract infections (ARTIs) are caused by sporadic or pandemic outbreaks of viral or bacterial pathogens, and continue to be a considerable socioeconomic burden for both developing and industrialized countries alike. Diagnostic methods and technologies serving as the cornerstone for disease management, epidemiological tracking, and public health interventions are evolving continuously to keep up with the demand for higher sensitivity, specificity and analytical throughput. Microfluidics is becoming a key technology in these developments as it allows for integrating, miniaturizing and automating bioanalytical assays at an unprecedented scale, reducing sample and reagent consumption and improving diagnostic performance in terms of sensitivity, throughput and response time. In this article, we describe relevant ARTIs-pneumonia, influenza, severe acute respiratory syndrome, and coronavirus disease 2019-along with their pathogenesis. We provide a summary of established methods for disease diagnosis, involving nucleic acid amplification techniques, antigen detection, serological testing as well as microbial culture. This is followed by a short introduction to microfluidics and how flow is governed at low volume and reduced scale using centrifugation, pneumatic pumping, electrowetting, capillary action, and propagation in porous media through wicking, for each of these principles impacts the design, functioning and performance of diagnostic tools in a particular way. We briefly cover commercial instruments that employ microfluidics for use in both laboratory and point-of-care settings. The main part of the article is dedicated to emerging methods deriving from the use of miniaturized, microfluidic systems for ARTI diagnosis. Finally, we share our thoughts on future perspectives and the challenges associated with validation, approval, and adaptation of microfluidic-based systems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".