Developing Tools for Multiplexed Point of Care Infectious Disease Diagnostics
Bibliographic record
Abstract
There is a need for comprehensive testing to identify infected individuals and guide theirtreatment during infectious disease epidemics. It also enables public health authorities to track, trace, and isolate those individuals and formulate a coordinated response. Yet, the COVID-19 pandemic highlighted diagnostic shortcomings such as low test coverage and false-negative results. An ideal diagnostic test would be sensitive, specific, easy to use, multiplexed, and deployed at the point of care. However, current point-of-care multiplex diagnostic devices suffer from limitations, including a lack of multiplexing scalability and incomplete automation. Quantum dot barcode (QDB) technology presents a promising solution to overcome these limitations. This technology enables the scaling up of multiplexed biomarker detection without the need for additional optical hardware. Despite this advantage, widespread adoption of QDB assays has been hindered by their multi-step manual procedures requiring laboratory infrastructure, lacking a sensitive portable optical readout system, and an ad-hoc labour-intensive assay design process. This thesis intends to address these challenges by developing integrated tools for multiplex infectious disease diagnostics using quantum dot barcode technology. Specifically, we aimed to advance the field in three areas: (1) automated and portable diagnostic instrumentation, (2) portable and sensitive fluorescence microscopy, and (3) automated assay design leveraging large language models (LLMs). For the first aim, we developed the QBox. It is a fully automated, portable diagnostic platform capable of executing all QDB assay steps with minimal user input. For the second aim, we engineered an epifluorescence microscopy system utilizing a multi-bandpass dichroic filter. It enables sensitive epifluorescence imaging of quantum dot barcode assays and other samples in a compact form factor. In the third aim, we identified optimal instructions for LLMs to accurately design QDB assays. They resulted in an LLM- powered chatbot that automates the entire assay design process. This work integrates advancements in automation, miniaturization, and large language models to enhance the accessibility, adaptability, and flexibility of multiplex point-of-care diagnostics. Better diagnostic tools enable rapid and informed responses to emerging infectious disease threats.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".