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.
Mental disorders are a type of behavioral, emotional, cognitive, or thinking disorder that cause pain and social dysfunction, and are one of the top ten global disease burdens. Cannabidiol (CBD) is one of the main components of cannabis, with high safety and tolerability, and is a hot topic in drug research. CBD has a wide range of therapeutic effects, and research has found that CBD has neuropsychiatric effects such as anti-addiction, anti-depression, anti-anxiety, and anti-stress, making it one of the candidate drugs for mental disorders. This article summarizes the mechanism and research progress of CBD for major mental disorders, in order to provide useful references for CBD-related compounds in the treatment of mental disorders.
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.
ObjectiveTo investigate the neural mechanisms of subjects with anisometropic amblyopia using event-related potential (ERP) technology.MethodsNineteen subjects diagnosed with anisometropic amblyopia were consecutively recruited from the outpatients of the West China Hospital of Sichuan University from June 2013 to February 2014, and twenty normal subjects were recruited as control subjects. All subjects were given different orientation Gabor stimuli with three spatial frequencies (1, 2, 8 cpd) and their ERP was recorded. The differences of P3b, including amplitude and latency, were compared between amblyopic eye and fellow or control eye.ResultsAmong the three groups of amblyopic eye and fellow or control eye, there was no statistically significant difference in accuracy or response time at three spatial frequencies (P>0.05), in latency or amplitude of P3b at low spatial frequencies (1, 2 cpd) ((P>0.05), or in amplitude of P3b at high spatial frequency (8 cpd) (P>0.05). P3b latency of amblyopic eye extended at high spatial frequency (8 cpd), compared to fellow or control eye. Amblyopic eye was most significantly weakened in Brodmann area 17 at the P3b wave of each spatial frequency (1, 2, 8 cpd), and there was most significantly weakened in high spatial frequency (8 cpd).ConclusionThe P3b latency of amblyopic eye extended at high spatial frequency suggests that the cognitive function of amblyopia is impaired, at an extent, in the late visual processing stage.
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.
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.
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.
Neutrophil extracellular traps (NETs), composed of deoxyribonucleic acid (DNA), histones, and granule enzymes, exhibit a “double-edged sword” effect in retinal neovascularization (RNV) diseases. The formation of NETs involves classical lytic and non-lytic pathways, as well as peptidylarginine deiminase 4 (PAD4)-dependent and reactive oxygen species-dependent molecular mechanisms. Across different types of RNV, the roles of NETs vary markedly. In diabetic retinopathy, NETs accelerate disease progression by amplifying inflammatory responses, disrupting the blood-retinal barrier, and increasing vascular permeability. During specific stages of retinopathy of prematurity, NETs participate in the clearance of senescent vessels and promote beneficial vascular remodeling. In retinal vein occlusion, their pro-coagulant and pro-inflammatory properties may exacerbate thrombosis and ischemia. The functions of NETs are dynamically regulated by disease stage, microenvironmental factors, and interactions with immune cells. Therefore, therapeutic strategies aimed at clearing NETs (e.g., deoxyribonuclease I) or inhibiting NET formation (e.g., PAD4 inhibitors) may serve as complementary approaches to current anti-vascular endothelial growth factor therapies, although further studies are needed to elucidate optimal intervention timing, safety, and combination treatment strategies for clinical translation.
Endogenous adult neural stem cells are closely related to the normal physiological functions of the brain and many neurodegenerative diseases. Neurons are affected by factors such as extracellular microenvironment and intracellular signaling. In recent years, some specific signaling pathways have been found that affect the occurrence of neural stem cells in adult neural networks, including proliferation, differentiation, maturation, migration, and integration with host functions. In this paper, we summarize the signals and their molecular mechanisms, including the related signaling pathways, neurotrophic factors, neurotransmitters, intracellular transcription factors and epigenetic regulation of neuronal differentiation from both the extracellular and intracellular aspects, providing basic theoretical support for the treatment of central nervous system diseases through neural stem cells approach.
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.
