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Background Porphyromonas gingivalis (Pg.) is the main pathogen of periodontitis. Studies have found that Pg. can affect systemic diseases including type 2 diabetes mellitus (T2DM) through oral-intestinal pathway, but its specific mechanism remains unclear. Objective To explore whether Pg. has an effect on T2DM by changing intestinal mechanical and immune barriers. Methods A total of 24 among 40 SPF mice were randomly selected to construct T2DM models, among the successful models, 16 mice were selected to be divided into the model group (DM group, n=8) and model+Pg. group (PD group, n=8), and the other 16 mice were divided into the control group (N group, n=8) and Pg. group (n=8). After modeling, body mass and fasting plasma glucose (FPG) of mice were observed, and oral glucose tolerance test (OGTT) was performed at the 5th week, OGTT curve was plotted and the area under the curve (AUC) was calculated. Pg. group and PD group were gavaged with Pg. bacterial solution from the 7th week for 5 consecutive weeks. Lipopolysaccharide (LPS) was determined by enzyme-related immunosorbent assay (ELISA), colonic tight-junction protein and inflammatory factors were detected by real-time fluorescence quantitative PCR, and colonic tissue lesions were observed by hematoxylin-eosin (HE) staining. Pearson correlation or Spearman correlation analysis was used to investigate the relationship of FPG with colonic tight junction protein mRNA expression and serum LPS levels in mice. Results Body weight of DM group was higher than that of N group and Pg. group at 2nd to 6th weeks before irrigation (P

ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

Deep learning based on labeled data has brought massive success in computer vision, speech recognition, and natural language processing. Nevertheless, labeled data is just a drop in the ocean compared with unlabeled data. How can people utilize the unlabeled data effectively? Research has focused on unsupervised and semi-supervised learning to solve such a problem. Some theoretical and empirical studies have proved that unlabeled data can help boost the generalization ability and robustness under adversarial attacks. However, current theoretical research on the relationship between robustness and unlabeled data limits its scope to toy datasets. Meanwhile, the visual models in autonomous driving need a significant improvement in robustness to guarantee security and safety. This paper proposes a semi-supervised learning framework for object detection in autonomous vehicles, improving the robustness with unlabeled data. Firstly, we build a baseline with the transfer learning of an unsupervised contrastive learning method—Momentum Contrast (MoCo). Secondly, we propose a semi-supervised co-training method to label the unlabeled data for retraining, which improves generalization on the autonomous driving dataset. Thirdly, we apply the unsupervised Bounding Box data augmentation (BBAug) method based on a search algorithm, which uses reinforcement learning to improve the robustness of object detection for autonomous driving. We present an empirical study on the KITTI dataset with diverse adversarial attack methods. Our proposed method realizes the state-of-the-art generalization and robustness under white-box attacks (DPatch and Contextual Patch) and black-box attacks (Gaussian noise, Rain, Fog, and so on). Our proposed method and empirical study show that using more unlabeled data benefits the robustness of perception systems in autonomous driving.

Through vicinal difunctionalization of nitriles, a successful, innovative strategy for the synthesis of N-alkyl amidines is developed. This methodology capitalizes on commercially available nitriles, alcohols, and sulfonamides. The activation of alcohols by trifluoromethanesulfonic anhydride (Tf2O) plays a pivotal role throughout the entire reaction process. In comparison to existing amidine synthesis methods, this strategy showcases notable advantages, including the avoidance of toxic and hazardous azides, the exclusion of transition metal catalysts, and the use of mild reaction conditions. Importantly, this methodology provides an effective approach for late-stage modification of complex molecules and drugs, holding promise for the construction of molecular libraries in drug discovery. [ABSTRACT FROM AUTHOR]