Distance-based lateral flow immunoassay for quantitative detection of C-reactive protein in cardiovascular risk assessment
Bibliographic record
Abstract
C-reactive protein (CRP) is a key biomarker of inflammation, widely utilized for predicting cardiovascular disease (CVD) risk. Conventional detection methods often require sophisticated equipment and skilled personnel, limiting their accessibility in point-of-care testing (POCT). This study presents a novel distance-based lateral flow immunoassay (Dist-LFIA) for semi-quantitative CRP detection, offering a simple, portable, and cost-effective alternative. The device visually categorizes CRP levels into risk groups—low (<1 mg/L), intermediate (1–3 mg/L), and high (4–10 mg/L)—based on the length of a colorimetric signal, enabling easy interpretation without the need for advanced instrumentation. The Dist-LFIA demonstrated excellent analytical performance, achieving a detection limit of 0.12 mg/L, comparable recovery rates to immunoturbidimetry , and sustained stability over prolonged periods. Observer reliability tests confirmed strong intra- and inter-reader agreements, ensuring consistent and reproducible results. Additionally, the device features a straightforward fabrication process, making it highly scalable and suitable for resource-limited settings. Unlike traditional methods, which often depend on auxiliary devices or complex protocols, the Dist-LFIA provides immediate, equipment-free results. This innovation enhances accessibility to CRP-based diagnostics, facilitating early CVD risk assessment in home settings, remote areas, and under-resourced healthcare environments. Its versatility promises broader applications in diagnostics beyond CVD prediction.
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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".