Additive manufacturing leveraged microfluidic setup for sample to answer colorimetric detection of pathogens
Bibliographic record
Abstract
Colorimetric readout for the detection of infectious diseases is gaining traction at the point of care/need owing to its ease of analysis and interpretation, and integration potential with highly specific loop-mediated amplification (LAMP) assays. However, coupling colorimetric readout with LAMP is rife with challenges including, rapidity, inter-user variability, colorimetric signal quantification, and user involvement in sequential steps of the LAMP assay, hindering its application. To address these challenges, for the first time, we propose a remotely smartphone-operated automated setup consisting of (i) an additively manufactured microfluidic cartridge, (ii) a portable reflected-light imaging setup with controlled epi-illumination (PRICE) module, and (iii) a control and data analysis module. The microfluidic cartridge facilitates sample collection, lysis, mixing of amplification reagents stored on-chip, and subsequent isothermal heating for initiation of amplification in a novel way by employing tunable elastomeric chambers and auxiliary components (heaters and linear actuators). PRICE offers a new imaging setup that captures the colorimetric change of the amplification media over a plasmonic nanostructured substrate in a controlled and noise-free environment for rapid minute-scale nucleic acid detection. The control and data analysis module employs microprocessors to automate cartridge operation in tandem with the imaging module. The different device components were characterized individually and finally, as a proof of concept, SARS-CoV-2 wild-type RNA was detected with a turnaround time of 13 minutes, showing the device's clinical feasibility. The suggested automated device can be adopted in future iterations for other detection and molecular assays that require sequential fluid handling steps.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".