Objective To explore the difference between bone marrow edema syndrome (BMES) and avascular necrosis of femoral head (ANFH). Methods Recent original articles about BMES and ANFH were extensively reviewed, and were comprehensively analysed. Results The pathology, pathogenesis, clinical features, treatment selection, and prognosis are different between these two diseases. Conclusion BMES and ANFH are two different diseases. Micro-fracture may be the cause of bone marrow edema.
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.
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.
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.
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.
Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.
Objective To provide some theoretical reference and practical guidance for the medical risk management and early warning of private medical institutions, and to improve the service level and social reputation of private medical institutions. Methods China National Knowledge Infrastructure, Wanfang, VIP, and Web of Science database were searched for literature on medical risk management of private medical institutions published from the dates of establishment of databases to July 31, 2023. CiteSpace software was used for analysis. The aspects of literature number, literature source, author-institution cooperation, keyword co-occurrence, keyword clustering and burst were analyzed. Results A total of 2 635 literature were detected. Among them, there were 1446 articles in Chinese and 1189 articles in English. Although domestic research started late, it covered a wide range of disciplines and research fields. The Chinese literature showed a growth trend in the medium term, but the growth trend was slower than that of foreign literature. At the same time, the cooperation network of foreign authors and institutions was closer than that of domestic ones, and the overall development was relatively insufficient. There were differences between domestic and foreign research hotspots in terms of disciplines and research contents. Conclusions It is necessary to strengthen the theoretical and practical research on medical risk management of private medical institutions, and accelerate the construction of risk management and early warning models suitable for the characteristics of private medical institutions in China. In the future, the emerging research fields such as moral hazard, emergency and internal control need to be deepened and expanded.