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 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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".