AI‐Assisted Plasmonic Enhanced Colorimetric Fluidic Device for Hydrogen Peroxide Detection from Cancer Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Hydrogen peroxide (H 2 O 2 ) is an essential molecule to various physiological processes and is commonly used for the detection and monitoring of glucose and cell viability. Furthermore, it is identified as a signal of oncogenic growth due to its widespread presence within the cancer cell environment. However, the low concentrations of H 2 O 2 released by cancer cells' metabolism challenge current detection methods' capabilities and their practicality for translation to clinical applications. Colorimetric assays with simple readouts are a promising solution, provided that their sensitivity and rapidity in detecting H 2 O 2 improve. Here, a plasmonic enhanced nanopatterned platform is proposed coupled with an Amplex Red assay to monitor the color change of H 2 O 2 released from cancer cells. The nanopatterned platform embedded into a multiplexed microfluidic device enhances the kinetics of the reaction ≈7 times. This approach has reached a limit of detection of 1 p m when tested in breast (MCF‐7) and prostate (PC‐3) cancer media. The collected color images are processed and analyzed by a machine learning algorithm that categorizes them into “high” or “low‐to‐no” concentrations of H 2 O 2 with 91% accuracy. This study is a step toward developing a device for highly sensitive H 2 O 2 detection that is easily adaptable, user‐friendly, portable, and automated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it