Non-invasive, non-enzymatic, non-serodiagnostic, and home-detecting paper-based “abnormal UA alarm” for early diagnosis of UA associated diseases
Bibliographic record
Abstract
Uric acid (UA) level is a pivotal clinical human-health biomarker providing predictive feedback for multitudinous well-known kidney, cardiovascular and metabolic syndrome diseases. Off-the-shelf UA detection methods clinically rely on uricase suffer from limitations such as high costs, longstanding result acquisition, circumscribed testing locations, rigorous expertise requirements, and difficulty in home-detecting due to serum testing systems. Here, inspired by the pH-paper, a scaleable, rapid, non-invasive/-enzymatic/-serodiagnostic, and home-detecting “abnormal UA alarm” platform for UA detection in saliva was developed by strategically integrating the proposed paper-based fluorescent sensing-materials (NIFP-SM) with a user-orientated intelligent red-green-blue (RGB) analysis device. Therefore, NIFP-SM is nano-engineered through straightforward interfacial interactions of functional building blocks of on-demand naphthyl imide-derived fluorescent self-assembled micro-particles (NIFS) with lamellar structure and commercially-used filter paper. The NIFS possesses dominantly wide detection range (0–5 000 µmol/L) and high sensitivity (limit of detection = 0.91 µmol/L). Surprisingly, NIFS exhibited outstanding identifiability for uric acid even in the presence of 16 interferents, substantiating accurate detection-capability in intricate environments. Thus NIFP-SM equipped with NIFS resoundingly achieved efficient, rapid, and on-site visual detection of UA in saliva, urine-simulants, and foods. Further, the NIFP-SM-based automatic analysis platform integrated with an intelligent RGB analysis device was manufactured and enabled accurate quantitative, low-cost, non-invasive/-enzymatic/-serodiagnostic, rapid, home-detecting for UA, eliminating the need for costly equipment and specialized personnel and thereby facilitating early-warning detection of abnormal UA-levels associated diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".