An Integrated Nanosensor/Smartphone Platform for Point-of-Care Biomonitoring of Human Exposure to Pesticides
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Organophosphorus (OP) compounds are neurotoxins that are among the most widely used pesticides in agriculture in the United States. In this application, a new integrated point-of-care smartphone/resistive nanosensor device is developed for onsite rapid and sensitive detection of exposure to OP pesticides from a drop of finger-stick blood among a sample of farmworkers. The nanosensor leverages the transport properties of a multiwalled carbon nanotube/polyaniline nanofiber (MWCNT/PAnNF) nanocomposite film on a gold interdigitated electrode and acetylcholinesterase/butyrylcholinesterase (AChE/BChE) hydrolysis of their respective substrates generating protons doping PAnNFs, thereby increasing the conductance of the film. As such, a conductance change can be used to quantify cholinesterase activity, enabling assessment of acute/chronic OP poisoning. Additionally, a mobile app was developed for the nanosensor to process, display, track, and share results. Under optimal conditions, the nanosensor demonstrated exceptional sensitivity with the detection limits of 0.11 U/mL for AChE and 0.093 U/mL for BChE, physiologically relevant dynamic ranges of 2.0–18.0 U/mL for AChE and 0.5–5.0 U/mL for BChE in whole blood, and high reproducibility with the relative standard variation of <4%. The nanosensor was further validated with widely used radiometric and Ellman’s methods, utilizing both in vitro pesticide-spiked blood samples and blood samples from 22 farmworkers. The results between this nanosensor and those two methods demonstrated a strong agreement. This platform provides a new avenue for the simple, rapid, and sensitive biomonitoring of OP pesticide exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".