Angiopoietin-like protein (ANGPTL), a group of secreted glycoproteins, is widely expressed in vivo and is involved in many pathophysiological processes such as glycolipid metabolism, stem cell growth, local inflammation, vascular leakage and angiogenesis. Many kinds of ANGPTL are closely related to the occurrence and development of diabetic retinopathy (DR), especially ANGPTL4, which has gradually become a new hotspot in the field of DR Research. ANGPTL is involved in glucose metabolism and lipid metabolism, promotes increased vascular permeability, pathological angiogenesis, and participates in intraocular inflammation. ANGPTL is a promising molecular target. It can not only be used as a biomarker to predict the occurrence and progression of DR, but also provide new ideas for the treatment of DR by making antibody drugs to interfere with this molecule.
Renal cancer is a common malignant tumor and the deadliest cancer of the urinary and reproductive system. Given the increasing incidence rate of kidney cancer, timely intervention of its controllable risk factors is crucial. Antimicrobial agent is widely used worldwide, and in recent years, some studies have found that long-term use of antimicrobial agent is associated with an increased risk of kidney cancer. The mechanism may involve multiple factors such as nephrotoxicity of antimicrobial agent and intestinal flora imbalance. This article reviews the relationship between long-term use of antimicrobial agent and risk of kidney cancer, and explores possible mechanisms, to understand the impact of long-term use of antimicrobial agent on the risk of kidney cancer, and to provide more references for early prevention of kidney cancer and rational use of antimicrobial agent.
Deep learning-based automatic classification of diabetic retinopathy (DR) helps to enhance the accuracy and efficiency of auxiliary diagnosis. This paper presents an improved residual network model for classifying DR into five different severity levels. First, the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network. Second, to address the issue of inaccurate classification due to minimal differences between different severity levels, a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions. Finally, to better extract the morphological features of the lesions in DR images, cross-layer fusion convolutions were used instead of the conventional residual structure. To validate the effectiveness of the improved model, it was applied to the Kaggle Blindness Detection competition dataset APTOS2019. The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75% and a Kappa value of 0.971 7 for the five DR severity levels. Compared to some existing models, this approach shows significant advantages in classification accuracy and performance.
Amanitin-containing mushroom poisoning is one of the most harmful and lethal types of mushroom poisoning events. Its basic medical and clinical medical knowledge has not been fully understood and mastered, so the basic and clinical diagnosis and treatment of amanitin-containing mushroom poisoning has always been a hot research field of acute mushroom poisoning. This article focuses on the new progress in the epidemiology, toxicological properties, poisoning mechanism, clinical diagnosis and treatment of amanitin-containing mushroom poisoning, in order to provide the basis for further study, diagnosis and treatment of amanitin-containing mushroom poisoning for basic researchers and clinical medical staff.
ObjectivesTo summarize and compare the operative mechanisms of the most representative comprehensive clinical practice guideline (CPG) databases worldwide, so as to provide references for establishing and managing Chinese CPG database.MethodsCPG databases were collected worldwide by discussing with experts in the guideline and database fields. Studies on guideline databases were searched in PubMed and CNKI to further collect CPG databases mentioned in these studies. Representative comprehensive guideline databases were finally selected by consulting relevant guideline experts. The institutions’ names of establishing and managing CPG databases, funding sources, human resources, aims, quality control measures (including CPG inclusion and updating criteria) were extracted and summarized. Databases were divided into government-led, society-led, and enterprise-led models. A descriptive analysis was conducted.ResultsThere were four government-led databases, four society-led databases and merely one enterprise-led database. The institutions of establishing CPG databases were same as the institutions of managing databases in the seven databases. All CPG databases had set up offices, seven of which were located in the capital. Most databases’ funds came from the government. Four databases implemented board management. According to the division of functions, members involved in establishing and managing CPG databases mainly included leaders, expert teams, managerial personnel, secretaries, web developers, and patient representatives. Criteria for inclusion of CPG were relevant to the purpose of establishing databases. Most databases required guidelines that had be updated within three to five years.ConclusionsThis study provides comprehensive information on operative mechanism of different CPG databases which can assist guideline database builders to optimize their operative mechanism.
To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.
ObjectiveTo investigate the association between colorectal adenoma (CRA) and colorectal cancer (CRC), and to analyze the main pathogenesis of CRA, in order to identify and control the key factors of CRA and reduce the incidence of CRC. MethodThe studies on the mechanism of CRA in recent years were searched and summarized, focusing on the interaction of inflammation, genetic and epigenetic changes, gut microbiota and lipid metabolism, and their effects on the development of CRA. ResultsInflammation, genetic and epigenetic changes, intestinal flora and lipid metabolism play an important roles in the occurrence and development of CRA. These factors had a significant impact on the formation and progress of CRA at different stages through complex interaction, and had potential application value in preventing CRC. ConclusionsMany factors participate in the occurrence and development of CRA and plays an important role, which provide reference for future research and clinical intervention.
Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.
As a novel soluble guanylate cyclase stimulator, vericiguat can improve myocardial and vascular function, reduce ventricular remodeling, myocardial hypertrophy, inflammation and fibrosis, and delay the progression of heart failure by interfering with cell signaling pathways. Vericiguat not only can significantly reduce the risk of heart failure-related hospitalization or cardiovascular death, but also is well tolerated and compliant by patients, which can increase the additional benefit and improve prognosis of patients with heart failure with reduced ejection fraction. This article will review the mechanism and research progress of vericiguat in heart failure with reduced ejectionfraction.
Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.