COVID-19 Detection Using a 3D-Printed Micropipette Tip and a Smartphone
Bibliographic record
Abstract
The COVID-19 pandemic has caused over 7 million deaths worldwide and over 1 million deaths in the US as of October 15, 2022. Virus testing lags behind the level or availability necessary for pandemic events like COVID-19, especially in resource-limited settings. Here, we report a low cost, mix-and-read COVID-19 assay using a synthetic SARS-CoV-2 sensor, imaged and processed using a smartphone. The assay was optimized for saliva and employs 3D-printed micropipette tips with a layer of monoclonal anti-SARS-CoV-2 inside the tip. A polymeric sensor for SARS-CoV-2 spike (S) protein (COVRs) synthesized as a thin film on silica nanoparticles provides 3,3′,5–5′-tetramethylbenzidine responsive color detection using streptavidin-poly-horseradish peroxidase (ST-poly-HRP) with 400 HRP labels per molecule. COVRs were engineered with an NHS-PEG 4 -biotin coating to reduce nonspecific binding and provide affinity for ST-poly-HRP labels. COVRs binds to S-proteins with binding strengths and capacities much larger than salivary proteins in 10% artificial saliva-0.01%-Triton X-100 (as virus deactivator). A limit of detection (LOD) of 200 TCID 50 /mL (TCID 50 = tissue culture infectious dose 50%) in artificial saliva was obtained using the Color Grab smartphone app and verified using ImageJ. Viral load values obtained in 10% pooled human saliva spiked with inactivated SARS-COV-2 virus gave excellent correlation with viral loads obtained from qPCR ( p = 0.0003, r = 0.99).
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".