The high frequency oscillatory ventilation (HFOV) is characterized with low tidal volume and low mean airway pressure, and can well support the breathing of the patients with respiratory diseases. Since the HFOV was proposed, it has been widely concerned by medical and scientific researchers. About the HFOV, this paper discussed its current research status and prospected its future development in technologies. The research status of ventilation model, mechanisms and ventilation mode were introduced in detail. In the next years, the technologies in developing HFOV will be focused on: to develop the branched high-order nonlinear or volume-depended resistance-inertance-compliance (RIC) ventilation model, to fully understand the mechanisms of HFOV and to achieve the noninvasive HFOV. The development in technologies of HFOV will be beneficial to the patients with respiratory diseases who failed with conventional mechanical ventilation as one of considerable ventilation methods.
Autoimmune uveitis (AU) and mood disorders, such as anxiety and depression, share a close bidirectional association. Visual impairment caused by AU and the side effects of glucocorticoid therapy significantly increase the incidence of anxiety and depression. Conversely, mood disorders disrupt immune homeostasis through neuro-endocrine-immune mechanisms, exacerbating inflammatory responses and elevating the risk of AU recurrence. The primary reasons for AU-induced mood disorders include visual impairment, unpredictable fluctuations in vision, long-term treatment, and glucocorticoid-related psychiatric reactions. Meanwhile, mood disorders not only trigger the onset and recurrence of AU but also interfere with treatment efficacy by reducing patient adherence. The underlying mechanisms involve psychological stress leading to hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis, inflammatory factor-mediated “brain-eye axis” regulation, synergistic effects of the gut microbiota-brain-immune axis, and stage-specific immune regulatory characteristics of acute and chronic stress. Therefore, clinical management should emphasize the synergistic integration of psychological interventions and anti-inflammatory therapy to enable early detection and treatment of extramedullary lesions, optimize diagnostic and therapeutic protocols, and improve the prognosis of AU patients. Future research should further elucidate the molecular mechanisms underlying the interaction between mood and inflammation, establish multidisciplinary collaborative diagnosis and treatment systems, validate the efficacy of psychological interventions through large-scale clinical studies, and explore the development of neuroprotective anti-inflammatory drugs.
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.
ObjectiveAlthough evidence links idiopathic pulmonary fibrosis (IPF) and diabetes mellitus (DM), the exact underlying common mechanism of its occurrence is unclear. This study aims to explore further the molecular mechanism between these two diseases. MethodsThe microarray data of idiopathic pulmonary fibrosis and diabetes mellitus in the Gene Expression Omnibus (GEO) database were downloaded. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify co-expression genes related to idiopathic pulmonary fibrosis and diabetes mellitus. Subsequently, differentially expressed genes (DEGs) analysis and three public databases were employed to analyze and screen the gene targets related to idiopathic pulmonary fibrosis and diabetes mellitus. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape. In addition, common microRNAs (miRNAs), common in idiopathic pulmonary fibrosis and diabetes mellitus, were obtained from the Human microRNA Disease Database (HMDD), and their target genes were predicted by miRTarbase. Finally, we constructed a common miRNAs-mRNAs network by using the overlapping genes of the target gene and the shared gene. ResultsThe results of common gene analysis suggested that remodeling of the extracellular matrix might be a key factor in the interconnection of DM and IPF. Finally, hub genes (MMP1, IL1R1, SPP1) were further screened. miRNA-gene network suggested that has-let-19a-3p may play a key role in the common molecular mechanism between IPF and DM. ConclusionsThis study provides new insights into the potential pathogenic mechanisms between idiopathic pulmonary fibrosis and diabetes mellitus. These common pathways and hub genes may provide new ideas for further experimental studies.
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.
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.
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.
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.
Iron death is an alternative to normal cell death and is regulated by a variety of cellular metabolic pathways. Iron death has become a hot topic of research because it can cause damage to various organs and degenerative diseases in the body. Metabolism, signalling pathways, endoplasmic reticulum stress, and immune cells can all affect the occurrence of iron death, and the blood-retina destruction induced by iron death plays an important role in autoimmune uveitis. Exploring the components of the blood-retina regulatory mechanism of iron death in autoimmune uveitis can lead to the search for targeted drug targets, which can provide a new research idea for the subsequent study of the diagnosis and treatment of autoimmune uveitis.
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.