Optimising effective parameters to improve performance quality in lateral flow immunoassay for detection of <i>PBP2a</i> in methicillin-resistant <i>Staphylococcus aureus</i> (MRSA)
Bibliographic record
Abstract
Lateral flow immunoassay (LFIA) is the most widely used platform of the point-of-care (POC) detection. Since the most important factor in deciding the results of this technique is the visual quality of the test, it should be at its optimum. To address this issue, the present study examines optimising critical parameters to improve the quality of LFIA performance. In this regard the penicillin-binding protein 2a (PBP2a) in methicillin-resistant staphylococcus aureus (MRSA) was selected as a bacterial model. The targeted parameters included a) size of gold nanoparticles (AuNP), b) antibody conjugation conditions with AuNP, c) antibody concentrations in the capturing zones, d) the effects of blocking substances and different chemical buffers for pre-treatment of the strips. Visual quality of different manufactured strips revealed that the diameter of 14 ± 2, PH = 9.5 was the optimum for improvement of MRSA detection quality. The optimum concentration of labelling antibody in conjugate pad and concentration of the capturing antibody in the test band were 40 µg/ml and 1.1 µg/μl in PBS buffer (pH 7.4) respectively. In conclusion by finding the critical factors affecting the detection limit of lateral flow and optimising them, the visual quality of LFIA results can be increased.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".