Portable analytical platforms for disease diagnostics and environmental monitoring
Bibliographic record
Abstract
High-performance analytical platforms have gained significant research interests recently and proved themselves as powerful tools for enabling low-cost, portable, and reliable analysis for a variety of practical applications.This thesis aims to develop novel portable analytical platforms for two important types of applications including disease diagnostics and environmental monitoring.In one branch of this research, new types of cellulose-based microfluidic biosensors are developed for quantifying physiologically relevant biomarkers in human fluids, with target applications of point-of-care diagnosis and physiological condition monitoring.Specifically, an electrochemical microfluidic paper-based immunosensor array (E-µPIA) is proposed, featuring ultralow cost, high portability, high throughput, excellent user friendliness.To interface with the E-µPIA, a customized, handheld electrochemical reader (potentiostat) is designed for multiplexed readout of electrochemical signals from the E-µPIA with high resolution.Based on this platform, multiplexed detection of three metabolites (glucose, lactate, and uric acid) in urine samples is first demonstrated with comparable performance to existing standard tests.Then, the E-µPIA biosensing platform was further optimized for diagnosis of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) co-infections in serum samples.Indirect ELISA of HIV/HCV antibodies is realized on the E-µPIA with LODs of 300 pg/ml and 750 pg/ml, respectively, both lower than that of standard HIV/HCV tests.Targeting wearable biosensing for physiological condition monitoring, a wearable microfluidic thread-based zinc-oxide-nanowire (ZnO-NW) biosensor is developed for continuous monitoring of lactate and sodium concentrations in sweat during perspiration.Based on this platform, multiplexed detection of lactate and sodium in human sweat is demonstrated with dynamic ranges of 0-25 mM and 0.1-100 mM, and LODs of 3.61 mM and 0.16 mM, respectively, both covering the clinical sweat levels.Accurate measurements on real sweat samples from a healthy donor are conducted, and the results (13.16 ± 0.83 mM for lactate and 92.9 ± 5mM for sodium) are in good agreement with standard test results.Along the other branch of this research, novel portable chemical analyzers are developed for rapid, on-site detection of pollutants (metal ions and total nitrogen) in water.Heavy metal ions released into various water systems have severe impact on the environment, and excess exposure
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".