Abstract Chronic fatigue syndrome (CFS) is a multisystem disease of unknown aetiology. It disrupts multiple physiological systems, and its pathogenesis, reliable diagnostic markers and targeted therapies remain poorly understood. Ferroptosis is a newly characterised form of programmed cell death. This cell death is associated with the pathogenesis of a variety of neurodegenerative diseases, making it a potential therapeutic target for such diseases. In this review, we focus on the interplay between CFS and ferroptosis. Relevant domestic and international literature is reviewed to summarise available findings, elucidate molecular regulatory mechanisms, and identify promising directions for future research. Our analysis reveals a bidirectional interaction between CFS and ferroptosis. Ferroptosis may contribute to CFS through oxidative stress and neuroinflammation, whereas pathological changes associated with CFS could further exacerbate ferroptosis. This review first describes the function of ferroptosis in CFS pathogenesis. Targeting ferroptosis‐related pathways may offer a potential therapeutic strategy for CFS. Nevertheless, further clinical evidence is still needed to support relevant research, and the specific regulatory network and interactions with other pathological mechanisms remain to be clarified.
Abstract Understanding brain function requires models that characterize neural activity as evolving latent brain states rather than as isolated signal segments. Existing artificial intelligence methods have advanced neural decoding, disease classification, functional connectivity analysis, and brain–computer interfaces. However, brain‐state prediction, temporal organization, state transitions, dynamical stability, and intervention responses are often studied as separate problems, limiting a unified understanding of how brain states are represented, organized, transformed, disrupted, and regulated. In this perspective, we examine liquid neural networks (LNNs) as a computational framework for adaptive latent brain‐state modeling. We do not argue that LNNs are the only approach to continuous‐time modeling. Instead, we focus on how their continuous dynamics and input‐ and state‐dependent temporal adaptation can help link latent trajectories, temporal‐scale regulation, state stability, and perturbation responses. We position LNNs in relation to spiking neural networks, recurrent neural networks, transformers, neural ordinary differential equations, and state‐space models, and discuss their relevance to multimodal neural signal fusion, cortical hierarchical dynamics, brain‐state transitions, dynamic disease prediction, and closed‐loop neuromodulation. Overall, LNNs show promise for adaptive latent brain‐state modeling, but their scientific and translational value depends on rigorous benchmarking, biological validation, computational feasibility, generalization, and real‐world safety.
Abstract Backgroud The human visual system can effectively perceive and understand complex visual scenes through the synergistic effect of bottom‐up primary visual cortex feature extraction and top‐down prefrontal cortex semantic guidance. This biological mechanism achieves an optimal balance between capturing fine‐grained detail and comprehending global semantics, providing valuable inspiration for multimodal generation research. Methods Inspired by this neural mechanism, this study proposes a method for text‐to‐image multimodal tasks and presents a new diffusion model framework that integrates bottom‐up and top‐down attention mechanisms. The bottom‐up mechanism employs global‐shape convolution, local key aggregation, and hyperbolic space mapping to hierarchically extract multi‐scale visual features, preserving both global structural integrity and local fine‐grained details by adaptively highlighting salient image regions. The top‐down mechanism dynamically modulates cross‐attention weights using mutual information, which quantifies text–image semantic correlation to enhance task‐relevant features, and information entropy, which measures feature uncertainty to suppress irrelevant noise. Results Extensive experiments conducted on the Common Objects in Context (COCO), Fill50K, and Large Scale Scene Understanding (LSUN) datasets show that our model compared favorably with the Stable Diffusion model in generation quality and diversity, achieving an inception score of 21.64 and a Fréchet inception distance (FID) of 23.65 on the COCO dataset. The model also achieved FID scores of 3.06 on LSUN Bedrooms and 4.98 on LSUN Churches. Ablation studies further validated the synergistic effect of the bidirectional framework and the independent contribution of each component, with notable improvements observed in text‐image alignment and fine‐grained detail handling. Conclusions This study demonstrates that cognitive‐inspired bidirectional attention mechanisms can effectively enhance text‐to‐image generation by balancing structural fidelity and semantic consistency. The integration of mutual information and information entropy provides a novel theoretical approach for multimodal fusion tasks and offers new directions for advancing the quality and applicability of generative models.
Abstract Multiple sclerosis (MS) is a chronic inflammatory and autoimmune disorder of the central nervous system, characterized by demyelination and axonal damage. The epidemiology of MS reflects a complex interplay between genetic susceptibility, including specific HLA alleles, and environmental factors such as geographic location, vitamin D status, and Epstein‐Barr virus infection. MS is more common in women, who more frequently develop relapsing‐remitting disease, whereas primary progressive MS is relatively more common in men. Disruption of the blood‐brain barrier facilitates immune‐cell infiltration and promotes neuroinflammation. The innate immune response involves macrophages, microglia, and other cell types that contribute to inflammation and neuronal damage. In contrast, the adaptive immune response is driven by autoreactive T and B cells, which damage myelin and neurons through cytokine production and antigen presentation. Glial cells play context‐dependent roles in both promoting neuroinflammation and supporting tissue repair. Key molecular mechanisms include MHC‐mediated antigen presentation, complex networks of pro‐inflammatory and anti‐inflammatory cytokines and chemokines that regulate cell migration, colony‐stimulating factors, and complement system activation. Current therapeutic approaches primarily emphasize immunomodulation and immunosuppression using agents such as interferon‐β, glatiramer acetate, monoclonal antibodies, and inhibitors of cell migration. Recent therapeutic developments, including cellular therapies and strategies that modulate the gut microbiota, offer new avenues for improving disease control and promoting repair in MS. Despite these advances, the interactions among immune, glial, and molecular mechanisms remain incompletely understood. Defining these interactions more precisely will be essential for developing more targeted and effective therapies.
Abstract Backgroud Major depressive disorder (MDD) remains a leading contributor to the global health burden. However, its burden exhibits substantial variation across populations and over time. Methods Using data from the global burden of disease study 2021, we analyzed MDD metrics across 204 countries and territories in this study. Joinpoint regression was used to calculate annual percentage changes in MDD trends, and age–period–cohort analysis examined age, period, and cohort effects and local drift in disability‐adjusted life years (DALYs). Bayesian age–period–cohort models projected age‐standardized prevalence, incidence, and DALY rates through 2035 for China and worldwide. Results From 1990 to 2021, the number of MDD cases in China increased by 38.3%, whereas age‐standardized rates declined, reaching levels approximately 49% lower than global averages in 2021. The global burden peaked at ages 20–39 years, whereas in China it peaked at 50–59 years, with widening sex disparities. Joinpoint analyses indicated a decline in the MDD burden in China, with a rebound during 2010–2015. Global trends showed a stable decline until 2010, followed by sustained growth and a sharp rise during 2019–2021. Age–period–cohort analysis showed an age‐related increase and a period effect in China after 2010. The global pattern displayed a plateau in midlife, a decline at older ages, and stronger period effects after 2015. Projections indicated that the MDD burden would continue to decline in China but remain stable or increase worldwide through 2035. Conclusions Intensified efforts are needed to address the growing caregiving burden associated with aging populations and strengthen mental health service systems.
