Efficacy and safety of direct oral anticoagulants in patients with atrial fibrillation combined with chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: Currently published studies have not observed consistent results on the efficacy and safety of direct oral anticoagulants (DOACs) use in patients with chronic kidney disease (CKD) combined with atrial fibrillation (AF). Therefore, this study conducted a meta-analysis of the efficacy and safety of DOACs for patients with AF complicated with CKD. METHODS: Database literature was searched up to May 30, 2023, to include randomized controlled trials (RCT) involving patients with AF complicated with CKD DOACs and vitamin K antagonists (VKAs). Stroke, systemic embolism (SE), and all-cause mortality were used as effectiveness indicators, and major bleeding, intracranial hemorrhage (ICH), fatal bleeding, gastrointestinal bleeding (GIB), and clinically relevant non-major bleeding (CRNMB) were used as safety outcomes. RESULTS: Nine RCT studies were included for analysis according to the inclusion criteria. Results of the efficacy analysis showed that compared with VKAs, DOACs reduced the incidence of stroke/SE (OR = 0.75, 95% CI 0.67-0.84) and all-cause deaths (OR = 0.84, 95% CI 0.75-0.93) in patients with AF who had comorbid CKD. Safety analyses showed that compared with VKAs, DOACs improved safety by reducing the risk of major bleeding (OR = 0.76, 95%CI 0.65-0.90), ICH (OR = 0.46, 95%CI 0.38-0.56), and fatal bleeding (OR = 0.75, 95%CI 0.65-0.87), but did not reduce the incidence of GIB and CRNMB. CONCLUSION: Compared with VKAs, DOACs may increase efficacy and improve safety in AF patients with CKD (90 ml/min> Crcl≥15 ml/min), and shows at least similar efficacy and safety in AF patients with Kidney failure (Crcl<15 ml/min).

Non-destructive detection and recognition of pesticide residue levels on cauliflowers using visible/near-infrared spectroscopy combined with chemometrics.

In this study, two prediction models were developed using visible/near-infrared (Vis/NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) for the detection of pesticide residues of avermectin, dichlorvos, and chlorothalonil at different concentration levels on the surface of cauliflowers. Five samples of each of the three different types of pesticide were prepared at different concentrations and sprayed in groups on the surface of the corresponding cauliflower samples. Utilizing the spectral data collected in the Vis/NIR as input values, comparison and analysis of preprocessed spectral data, and regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) were used in turn to downscale the data to select the main feature wavelengths, and PLS-DA and LS-SVM models were built for comparison. The results showed that the RC-LS-SVM was the best discriminant model for detecting avermectin residues concentration on the surface of cauliflowers, with a prediction set discriminant accuracy of 98.33%. For detecting different concentrations of dichlorvos, the SPA-LS-SVM had the best predictive accuracy of 95%. The accuracy of the model based on CARS-PLS-DA to identify chlorothalonil at different concentration levels on cauliflower surfaces reached 93.33%. The results demonstrated that the Vis/NIR spectroscopy combined with chemometrics could quickly and effectively identify pesticide residues on cauliflower surfaces, affording a certain reference for the rapid recognition of different pesticide residue concentrations on cauliflower surfaces. PRACTICAL APPLICATION: Vis/NIR spectroscopy can detect the concentration levels of pesticide residues on the surface of cauliflowers and help food regulators quickly and non-destructively detect traces of pesticides in food, providing a guarantee for food safety. The technique also provides a basis for determining pesticide residue concentrations on the surface of other vegetables.

Surface Defect Detection of Fresh-Cut Cauliflowers Based on Convolutional Neural Network with Transfer Learning.

A fresh-cut cauliflower surface defect detection and classification model based on a convolutional neural network with transfer learning is proposed to address the low efficiency of the traditional manual detection of fresh-cut cauliflower surface defects. Four thousand, seven hundred and ninety images of fresh-cut cauliflower were collected in four categories including healthy, diseased, browning, and mildewed. In this study, the pre-trained MobileNet model was fine-tuned to improve training speed and accuracy. The model optimization was achieved by selecting the optimal combination of training hyper-parameters and adjusting the different number of frozen layers; the parameters downloaded from ImageNet were optimally integrated with the parameters trained on our own model. A comparison of test results was presented by combining VGG19, InceptionV3, and NASNetMobile. Experimental results showed that the MobileNet model's loss value was 0.033, its accuracy was 99.27%, and the F1 score was 99.24% on the test set when the learning rate was set as 0.001, dropout was set as 0.5, and the frozen layer was set as 80. This model had better capability and stronger robustness and was more suitable for the surface defect detection of fresh-cut cauliflower when compared with other models, and the experiment's results demonstrated the method's feasibility.