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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".