A Low-Cost Cellulose-Based POC Device for Detection of COVID-19
Bibliographic record
Abstract
Point-of-care diagnostic (POC) is of utmost importance for the fight against pandemics which provides easy, instant results right at your place. POC can transform healthcare management and create superior infrastructure. In POC, employing lateral flow assays (LFAs) can accelerate the assessment and shorten the treatment time. LFA is a small handheld device made from paper that controllably delivers the analyte without the need of an active pump to the detection area to produce the signal, generally coloured nanoparticles (Au, carbon). Conventional LFA device is made of four components, sample pad, conjugate pad, nitrocellulose membrane and absorbent pad arranged sequentially. At the sample pad, the analyte is dispensed. From here, the analyte moves to the conjugate pad and hybridizes with recognition antibodies (placed onto nanoparticles) to form analyte-antibody-gold nanoparticles conjugate. Further movement takes this to the nitrocellulose membrane. Here, other recognizant antibodies are placed at the test and control line to immobilize by forming the sandwich assay with the analyte-antibody-gold nanoparticles. The excess liquid is absorbed in the absorbent pad. From these components of the LFA device, nitrocellulose membrane and antibodies need replacements as they increase the device's overall cost. Cellulose paper holds promise to counter nitrocellulose membrane; however, the development of fabrication methods for cellulose paper-based LFA devices is needed. In comparison to antibodies, aptamers improve batch-to-batch variability, ruggedness, and longevity of LFA devices. This work describes the fabrication of half-strip cellulose paper (Whatman® Grade 4 filter paper) based LFA device exploiting aptamers as the recognition element for antigen detection. The covalent bonding of aptamers was achieved by NanoCheck-ATH® (NC) and p-phenylene disothiocyanate (PDITC). NC provides plentiful amine functional groups and constricts the test line width by covalently bonding aptamer to the NC region only. The chitosan-based NanoCheck on paper assists in realizing sharp test and control lines by providing the controlled surface for PDITC reaction through amine groups. PDITC functions as a linker between NC and aptamers. Finally, a proof-of-concept nucleocapsid protein of SARS-CoV-2 as an antigen detection was shown with the minimal detectable concentration down to ~20 ng/mL with excellent selectivity towards casein, BSA, and albumin. We envisage that using facile modification chemistry of NC, PDITC, and aptamers for antigen detection will allow the fabrication of paper-based LFA devices in a roll-to-roll setting and, thus, decrease the overall cost. The developed method is generic and can be adapted to detect other antigens to fight future pandemics.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.006 |
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".