Aptamer based Lateral Flow Assays for Rapid and Sensitive Detection of CKD marker Cystatin C
Bibliographic record
Abstract
A novel aptamer-antibody pair-based lateral flow assay was designed to rapidly quantify Cystatin C (CysC). CysC is a small protein that can be expressed by all nucleated cells. It is rarely influenced by factors other than Glomerular filtration rate (GFR), an indicator of renal function chronic kidney diseases (CKD). That makes it a reliable biomarker for the measurement of the GFR. Aptamers bind specifically to the target molecules, it is less expensive, more stable, and lack immunogenicity. The aptamers became a valuable tool in clinical diagnosis and made them a great alternative to antibodies. In this study, we designed and developed an aptamer- antibody pair-based quantitative lateral flow assay for the CysC quantification in the human sample. A highly sensitive and specific CysC sensor was achieved by conjugating CysC selective aptamers to the organic dye Alexafluor-647. When CysC molecules are present in the sample, they form a complex with the designed aptamers to bind, especially with the CysC antibody immobilized on the lateral flow assay strip's test zone. Important parameters that influence the sensitivity in lateral flow assay, such as the concentration of aptamers in the conjugation pad, were evaluated to give the optimum assay performance. The assay was precise and has a limit of detection of 0.013 μg/μl was shown better than the antibody-based kit. In summary, the resulting LFA aptamers-based sensor provides a rapid, sensitive, cost-effective point of care sensor for CysC detection in human samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".