Engineering Nanoparticles for Multiplexed Point-of-Care and Clinical Diagnostics
Bibliographic record
Abstract
Point-of-care diagnostics promptly provide a diagnostic output that allows a healthcare worker to make a clinical decision at a respective testing site. Their development has been encouraged by the World Health Organization (WHO) and other agencies for use in developing nations that lack proper medical infrastructure. Despite several academic pursuits, only a few point-of-care-based diagnostics have made it to field trials or through regulatory approval. Nanomaterials have gained popularity as ideal building blocks for developing point-of-care diagnostics due to their unique optical properties for chemical sensing and large surface areas for the dense attachment of biomolecules. In this thesis, I engineer nanoparticle-based assays and detection strategies to develop multiplexed and point-of-care diagnostics. First, I design a modular nucleic acid structure that improves the detection sensitivity of nanoparticle-based assays for infectious diseases. This strategy stabilizes oligonucleotides on the surface of nanoparticles and leads to a 114-fold improvement in detection sensitivity. Next, I demonstrate a multiplexed diagnostic system to genotype viral variants. I demonstrate that viral variants can be genotyped by analyzing the signal they produce in response to different nucleic acid particles in a solution. Using this barcode system, I tracked the emergence of the N501Y SARS-CoV-2 variant with 94% accuracy. Lastly, I integrate my work to develop a portable smartphone-based multiplexed assay for real-time surveillance of patients infected with SARS-CoV-2. The device uploads results to a database to provide instantaneous results to inform patients, physicians, and public health agencies. The culmination of my work results in a device for real-time surveillance of SARS-CoV-2 seroprevalence. With each aim in this thesis, I built and validated nanoparticle-based assays and integrated them into a multiplexed and point-of-care diagnostic device. Researchers can leverage the developments in this thesis to develop the next generation of point-of-care diagnostics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".