Tetramethylpyrazine and paeoniflorin combination(TMP-PF)alleviates atherosclerosis progress by reducing hyperlipemia and inhibiting plaque angiogenesis via the NR4A1/VEGFR2 pathway

Atherosclerosis remains a great threat to human health worldwide.Previous studies found that tetramethylpyrazine(TMP)and paeonifl orin(PF)combination(TMP-PF)exerts anti-atherosclerotic effects in vitro.However,whether TMP-PF improves atherosclerosis in vivo needs further exploration.The present study aims to assess the anti-atherosclerotic properties of TMP-PF in ApoE^(-/-)mice and explore the related molecule mechanisms.Results showed that TMP and high-dose TMP-PF decreased serum triglyceride and low-density lipoprotein cholesterol levels,suppressed vascular endothelial growth factor receptor 2(VEGFR2)and nuclear receptor subfamily 4 group A member 1(NR4A1)expression in aortic tissues,inhibited plaque angiogenesis,reduced plaque areas,and alleviated atherosclerosis in ApoE^(-/-)mice.Also,TMP-PF exhibited a better modulation effect than TMP or PF alone.However,NR4A1 agonist abolished the anti-atherosclerotic effects of TMP-PF.In conclusion,TMP-PF was first found to alleviate atherosclerosis progression by reducing hyperlipemia and inhibiting plaque angiogenesis via the NR4A1/VEGFR2 pathway,indicating that TMP-PF had a positive effect on reducing hyperlipemia and attenuating atherosclerosis development.

Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care system.Timely diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the disease.This study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with segmentation.First,the data were preprocessed.An optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance problem.Second,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning network.Third,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease severity.In this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

Cryo-self-assembled silk fibroin sponge as a biodegradable platform for enzyme-responsive delivery of exosomes

Although advances in protein assembly preparation have provided a new platform for drug delivery during tissue engineering,achieving long-term controlled exosome delivery remains a significant challenge.Diffusion-dominated exosome release using protein hydrogels results in burst release of exosomes.Here,a fibroin-based cryo-sponge was developed to provide controlled exosome release.Fibroin chains can self-assemble into silk I structures under ice-cold conditions when annealed above the glass transition temperature.Exosome release is enzyme-responsive,with rates primarily determined by enzymatic degradation of the scaffolds.In vivo experiments have demonstrated that exosomes remain in undigested sponge material for two months,superior to their retention in fibrin glue,a commonly used biomaterial in clinical practice.Fibroin cryo-sponges were implanted subcutaneously in nude mice.The exosome-containing sponge group exhibited better neovascularization and tissue ingrowth effects,demonstrating the efficacy of this exosome-encapsulating strategy by realizing sustained release and maintaining exosome bioactivity.These silk fibroin cryo-sponges containing exosomes provide a new platform for future studies of exosome therapy.

The p.(Pro170Leu) variant in NOG impairs noggin secretion and causes autosomal dominant congenital conductive hearing loss due to stapes ankylosis

Conductive hearing loss is the impairment in the mechanical transduction of sound wave through the external ear and the middle ear.Although most cases are sporadic due to acquired causes such as infections(otitis media and otitis externa),cerumen obstruction,and injuries,congenital structural defects are uncommon for significant etiologies to recognize.Stapes ankylosis is characterized by conductive hearing loss.It may be difficult to differentiate from otosclerosis,the most common cause of progressive conductive hearing loss,by audiologic evaluation,when the diagnosis is delayed.Skeletal anomalies may be subtle,such that the syndrome may not be recognized(Brown et al.,2002).

A Bridge Neural Network-Based Optical-SAR Image Joint Intelligent Interpretation Framework

The current interpretation technology of remote sensing images is mainly focused on single-modal data,which cannot fully utilize the complementary and correlated information of multimodal data with heterogeneous characteristics,especially for synthetic aperture radar(SAR)data and optical imagery.To solve this problem,we propose a bridge neural network-(BNN-)based optical-SAR image joint intelligent interpretation framework,optimizing the feature correlation between optical and SAR images through optical-SAR matching tasks.It adopts BNN to effectively improve the capability of common feature extraction of optical and SAR images and thus improving the accuracy and application scenarios of specific intelligent interpretation tasks for optical-SAR/SAR/optical images.Specifically,BNN projects optical and SAR images into a common feature space and mines their correlation through pair matching.Further,to deeply exploit the correlation between optical and SAR images and ensure the great representation learning ability of BNN,we build the QXS-SAROPT dataset containing 20,000 pairs of perfectly aligned optical-SAR image patches with diverse scenes of high resolutions.Experimental results on optical-to-SAR crossmodal object detection demonstrate the effectiveness and superiority of our framework.In particular,based on the QXSSAROPT dataset,our framework can achieve up to 96%high accuracy on four benchmark SAR ship detection datasets.