Objective To investigate the load distribution on the more painful and less painful limbs in patients with mild-to-moderate and severe bilateral knee osteoarthritis (KOA) and explore the compensatory mechanisms in both limbs among bilateral KOA patients with different severity levels. Methods A total of 113 participants were enrolled between July 2022 and September 2023. This cohort comprised 43 patients with mild-to-moderate bilateral KOA (Kellgren-Lawrence grade 2-3), 43 patients with severe bilateral KOA (Kellgren-Lawrence grade 4), and 27 healthy volunteers (healthy control group). The visual analogue scale (VAS) score for pain, the Hospital for Special Surgery (HSS) score, passive knee range of motion (ROM), and hip-knee-ankle angle (HKA) were used to assess walking pain intensity, joint function, and lower limb alignment in KOA patients, respectively. Motion trajectories of reflective markers and ground reaction force data during walking were captured using a gait analysis system. Musculoskeletal modeling was then employed to calculate biomechanical parameters, including the peak knee adduction moment (KAM), KAM impulse, peak joint contact force (JCF), and peak medial/lateral contact forces (MCF/LCF). Statistical analyses were performed to compare differences in clinical and gait parameters between bilateral limbs. Additionally, one-dimensional statistical parametric mapping was utilized to analyze temporal gait data. Results Mild-to-moderate KOA patients showed the significantly higher HSS score (67.7±7.9) than severe KOA patients (51.9±8.9; t=8.747, P<0.001). The more painful limb in all KOA patients exhibited significantly greater HKA and higher VAS scores compared to the less painful limb (P<0.05). While bilateral knee ROM did not differ significantly in mild-to-moderate KOA patients (P>0.05), the severe KOA patients had significantly reduced ROM in the more painful limb versus the less painful limb (P<0.05). Healthy controls showed no significant bilateral difference in any biomechanical parameters (P>0.05). All KOA patients demonstrated longer stance time on the less painful limb (P<0.05). Critically, severe KOA patients exhibited significantly higher peak KAM, KAM impulse, and peak MCF in the more painful limb (P<0.05), while mild-to-moderate KOA patients showed the opposite pattern with lower peak KAM and KAM impulse in the more painful limb (P<0.05) and a similar trend for peak MCF. Conclusion Patients with mild-to-moderate KOA effectively reduce load on the more painful limb through compensatory mechanisms in the less painful limb. Conversely, severe bilateral varus deformities in advanced KOA patients nullify compensatory capacity in the less painful limb, paradoxically increasing load on the more painful limb. This dichotomy necessitates personalized management strategies tailored to disease severity.
Diabetes mellitus patients have the characteristics of higher morbidity of ischemic stroke, severe symptoms, more recurrent stroke and higher mortality. Current studies have shown that stroke patients with or without diabetes mellitus have different pathophysiological mechanisms during stroke progress. Accordingly, treatment that is beneficial to non-diabetes mellitus patients may not be beneficial to diabetes mellitus stroke patients. This article reviews the current research status of pathophysiological mechanism of diabetes mellitus complicated with ischemic stroke, and provides reference for the relevant research of drug intervention in diabetes mellitus patients complicated with stroke.
Myasthenia gravis is an autoimmune neuromuscular junction disorder primarily mediated by autoantibodies against the acetylcholine receptor (AChR). It is now widely recognized that the total titer of anti-AChR antibodies does not correlate directly with clinical severity and shows significant interindividual variability. This review focuses on the structure of the AChR, the three major pathogenic mechanisms mediated by anti-AChR antibodies, the pathogenic differences associated with distinct antigenic epitopes, the characteristics of various immunoglobulin subclasses, and the limitations of current antibody detection methods. It further explores future directions in antibody profiling and functional assessment. By systematically analyzing the complexity and heterogeneity of anti-AChR antibodies, this article underscores the critical role of precision medicine in the management of myasthenia gravis.
At present, the potential hazards of infrasound on heart health have been identified in previous studies, but a comprehensive review of its mechanisms is still lacking. Therefore, this paper reviews the direct and indirect effects of infrasound on cardiac function and explores the mechanisms by which it may induce cardiac abnormalities. Additionally, in order to further study infrasound waves and take effective preventive measures, this paper reviews the mechanisms of cardiac cell damage caused by infrasound exposure, including alterations in cell membrane structure, modulation of electrophysiological properties, and the biological effects triggered by neuroendocrine pathways, and assesses the impact of infrasound exposure on public health.
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.
Infodemic describes a special state during a public health emergency in which biased health information shows an explosive spread. This study systematically comprehends the definition, spreading mechanism, and coping strategies of infodemics. The study shows that the proliferation of false information relies on social media platforms and is driven by group psychological factors, social networks and algorithmic recommendation mechanisms, presenting complex dynamic characteristics. This article focuses on analyzing the commonly used tools in the field of communication science (including three types of models, ABM, SIR and MAP) and exploring their application value in simulating the diffusion path of disinformation. In terms of management strategies, the article proposes the strategies of constructing an intelligent information monitoring system, enhancing public health literacy, strengthening information filtering and quality control, and realizing the precise transformation of scientific knowledge. Despite the progress of the research, it still faces challenges such as fast dissemination speed and difficult supervision. In the future, interdisciplinary cooperation should be strengthened, a data-based information intervention system should be constructed, and the public's ability to immunize false information should be enhanced to protect public health safety.
Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.
Currently, all the conventional antibiotics have developed corresponding drug-resistant pathogenic strains, which have increasingly become a serious threat to people's health. Development of completely new types of antibiotics is one of effective ways to solve the drug resistance issue. Antimicrobial peptides with broad-spectrum antibacterial and antimicrobial activity and wild variety become the ideal alternative to traditional antibiotics. Antimicrobial peptides are derived from wide range of sources, such as plants, animals, and microorganisms. Mechanism of function of the antimicrobial peptides and the investigation approaches of different antimicrobial peptides also vary dramatically. In this paper, we give an overview of preparation, antibacterial mechanisms, and research methodology of antimicrobial peptides.
PNAS-4 is a novel pro-apoptosis gene identified latetly. In recent years, there has been a large number of research reports on the basic studies about PNAS-4 in cancer gene therapy and gene therapy of PNAS-4 alone or combined with chemotherapy or radiotherapy manifested a good application prospect, but its molecular mechanisms to promote apoptosis is not clear yet. In this paper, recent research about PNAS-4 in cancer gene therapy is briefly reviewed, and recent hypotheses on its molecular mechanisms to promote apoptosis are especially elucidated. Based on its newly identified characteristics of structural domain, we made a point that PNAS-4 might regulate functions of some target protein related to apoptosis by deSumoylation as a new deSumoylating isopeptidase, and consequently promote apoptosis.
Obesity is a prevalent metabolic disorder,which seriously affects human health and has become the world's public health problem. Kinase S6K1, an important downstream effector of mammalian target of rapamycin (mTOR), influences specific pathological responses, including obesity, type 2 diabetes and cancer. Presently, S6K1 has become an attractive therapeutic target in the treatment of these disorders. Here, the functions of kinase S6K1, its molecular regulation mechanisms, related pathogenesis of disease and relevant small molecular inhibitors are reviewed. Finally, the prospect of research toward S6K1 is expected as well.