RESUMO
Olive oil is an important and popularly used plant oil in the daily diet or chemical industry. Due to its biological benefits on human health and higher selling prices, adulteration of olive oil for commercial fraud by other plant oils is becoming a serious issue. In this study, a specific, sensitive and rapid loop-mediated isothermal amplification (LAMP) was first developed for the detection of Olea europaea DNA for olive oil authentication. The oleosin gene was used for the primer design of the LAMP assay. After primer validation, the results showed that the LAMP primers were specific and rapid to isothermally authenticate the oleosin gene of Olea europaea within 1 h at 62 °C and had no cross-reaction with other DNA of plant oils. The sensitivity of LAMP was 1 ng of genomic DNA in olive oil, and only 1% olive oil in the sample was requisite during DNA amplification. Additionally, positive detection by LAMP in all the collected commercial olive oil products was practically performed but not in PCR assays. In conclusion, herein, the established LAMP assay with specificity could not only be capable for rapid identification but also applicable for olive oil authentication for precluding adulteration in plant oil products. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05726-y.
RESUMO
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
