Integrating machine learning with lateral flow immunoassay for ultrafast and sensitive tadalafil detection
Abstract
Tadalafil, a phosphodiesterase type 5 inhibitor frequently detected in functional foods and dietary supplements, poses significant risks. To enable sensitive on-site detection, an anti-tadalafil monoclonal antibody (3C5) with an IC₅₀ of 0.32 ng mL-1 was prepared. Antibody-antigen binding kinetics revealed distinct variations in the early detection stage, prompting the integration of lateral flow immunoassay with machine learning (LFIA@ML) to further speed up testing. A Random Forest model trained on multi-dimensional signal features achieved fast detection and high accuracy, with a coefficient of determination of 0.995 and a mean absolute error of 0.038. For 140 samples, the LFIA@ML demonstrated a 94.3 % prediction accuracy and significantly reduced detection time to 3 min. The assay exhibited a strong correlation with LC-MS/MS, with recoveries ranging from 92.3 % to 112 % (coefficient variations ≤15.37 %). The LFIA@ML showcased the ability to reduce detection time by using the Random Forest algorithm and was validated in wine samples.
Read the paper
Related studies
- Protective effects of tadalafil and N-acetyl cysteine therapy on cisplatin-induced testicular toxicity
- The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation
- Sildenafil as a Candidate Drug for Alzheimer’s Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons
- Ozone-modified properties of pumpkin seed oil as anti-H. pylori, anticancer, anti-diabetic and anti-obesity agent
- Effectiveness of Low-Intensity Extracorporeal Shock Wave Therapy in Erectile Dysfunction: An Analysis of Sexual Function and Penile Hardness at Erection: An Umbrella Review
- Molecular Docking of Silymarin as a Ligand for Aromatase Enzyme and HIV-1 Reverse Transcriptase Using Molegro Virtual Docker
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.