(Digital Presentation) Development of an Electrochemical Microfluidic Device with on-Platform Sample Collection
Bibliographic record
Abstract
As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to develop, the need for portable rapid testing platforms remains prevalent to provide patients with accurate and quantitative diagnostic and serosurveillance information at the point-of-care. The current gold standard detection techniques like RT-PCR and ELISA require trained personnel to perform lengthy protocols, resulting in a long turnover from sample collection to result acquisition. Herein, we propose an electrochemical microfluidic device for on-platform detection of viral proteins and antibodies at the point-of-care in a multiplexed manner. Miniaturization technology through the use of microfluidic devices offers numerous advantages including low reagent consumption, high fluidic control, reduced reaction times, inexpensive applications, and the possibility of throughput analysis. Electrochemical detection can provide advantages in cost effective fabrication, high sensitivity and simple instrumentation using a standard 3-electrode (working, reference, and counter) setup. Our platform proposes the design of an electrochemical cell with an enhanced working electrode to act as the detection assay with microfluidic channels to facilitate sample collection and pre-treatment; an integrated saliva collection kit and lancing device enabled the use of both untreated saliva from direct self-collection and whole blood from a finger prick. Automated fluid manipulation reduced the potential of user contamination through the implementation of suction-based flow. The electrochemical microfluidic device was encased in a 3D-printed cartridge for the fabrication of a fully integrative technology on a single platform with the potential to be used at the point-of-care in both clinical and commercial applications using direct biofluids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".