N6-methyladenosine RNA methylation, an essential post-transcriptional modification, dynamically regulates RNA metabolism and plays a crucial role in neuronal function. Growing evidence suggests that dysregulated N6-methyladenosine modification contributes to the pathogenesis of neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis. However, the precise mechanisms by which N6-methyladenosine modification influences these conditions remain unclear. This review summarizes the role of m6A modification and its associated regulators in neurodegeneration, focusing on their involvement in key pathological processes. In Alzheimer's disease, m6A modification contributes to synaptic dysfunction, mitochondrial damage, and neuronal apoptosis. Evidence from APP/PS1, 5XFAD, tau transgenic, and Drosophila models demonstrates that regulators such as METTL3 and FTO influence Alzheimer's disease progression through neuroinflammation, circRNA dysregulation, and autophagy-related mechanisms. In Parkinson's disease, altered N6-methyladenosine regulator expression affects dopaminergic neuron survival and stress responses by modulating mRNA stability and autophagy-related lncRNAs. In multiple sclerosis and amyotrophic lateral sclerosis, N6-methyladenosine affects immune activation, myelin repair, and the regulation of disease-associated genes such as TDP- 43. Beyond N6-methyladenosine, other RNA methylation modifications-such as m1A, m5C, m7G, uracil, and pseudouridine-are implicated in neurodegenerative diseases through their regulation of mitochondrial function, RNA metabolism, and neuronal stress responses. Additionally, N6- methyladenosine exhibits cell type-specific functions: in microglia, it regulates inflammatory activation and phagocytic function; in astrocytes, it modulates metabolic homeostasis and glutamate-associated neurotoxicity; in neurons, it affects synaptic function and neurodegeneration-related gene expression; and in adult neural stem cells, it controls differentiation, neurogenesis, and cognitive plasticity. Recently, several small-molecule inhibitors targeting METTL3 or FTO have been developed to modulate N6-methyladenosine modification, providing new opportunities for disease intervention, with the targeting of N6-methyladenosine-related pathways emerging as a promising therapeutic strategy. However, challenges persist in optimizing the specificity and delivery of these therapeutic approaches.

Reverse osmosis (RO) membrane separation is vital for the advanced removal of contaminants, playing a key role in safe water supply. However, existing RO membranes fall remarkably short in adequate removal of small neutral organic contaminants (SNOCs ≤150 Da), due to the structural heterogeneity and nanosized defects of polyamide (PA) rejection layers. To address these challenges, we propose creating a continuous solid-phase interface using amphiphilic CdII/L-cysteine nanowires that spontaneously self-assemble at the water/n-hexane interface, thereby enabling precise control of the PA structure and suppressed formation of nanosized defects. The self-assembled CdII/L-cysteine interface facilitates the shuttling of m-phenylenediamine (MPD) monomers, achieving MPD pre-enrichment in the organic phase and leading to the formation of an ultraselective PA layer for the RO membrane, with an outstanding SNOC removal rate of up to 97.9%. Furthermore, the gutter effect and enhanced surface area ratio of the PA layer, induced by the CdII/L-cysteine interface, contribute to a remarkable increase in water permeance—upgraded by a factor of 4.5, reaching 3.6 ± 0.1 L m−2 h−1 bar−1. This effectively breaks the trade-off between SNOC removal and water permeance. This work opens an appealing avenue for developing highly permeable and selective RO membranes for efficient water reuse.

Microglial activation, driven by a metabolic shift towards aerobic glycolysis, is implicated in neuroinflammation and neurological disorders like depression. THSG (2,3,5,4′-Tetrahydroxystilbene-2-O-β-D-glucoside), derived from Polygonum multiflorum, exhibits anti-inflammatory and neuroprotective properties, but its mechanisms, particularly its impact on microglial metabolism, are largely unexplored. Using a LPS-induced mouse model of neuroinflammation, we observed that THSG significantly ameliorated depression-like behaviors. It suppressed pro-inflammatory cytokine production (TNF-α, IL-1β, IL-6, iNOS), inhibited microglial activation, and reduced key necroptosis markers (phosphorylated RIPK1, RIPK3, and MLKL) in the hippocampus. Importantly, THSG effectively suppressed LPS-induced activation of the glycolytic pathway in the hippocampus, as evidenced by increased ATP levels, decreased lactate levels, reduced activity of key glycolytic enzymes, and decreased expression of PKM2 and HIF-1α, critical players in microglial glycolysis. Further in vitro studies with BV2 microglial cells confirmed that THSG significantly suppressed glycolytic enhancement, promoting a metabolic shift towards oxidative phosphorylation, thus inhibiting inflammatory activation of microglia. Co-culture experiments of BV2 cells and SH-SY5Y cells further corroborated the in vivo findings, demonstrating that THSG mitigated inflammation-induced necroptosis in SH-SY5Y neurons by reducing phosphorylation of RIPK1, RIPK3, and MLKL, thus protecting neurons from damage. Our results highlight the potential of THSG as a therapeutic agent for neuroinflammatory disorders by modulating microglial metabolic reprogramming and inhibiting neuronal necroptosis.

Melanoma is a malignant tumor originating from the lesions of skin cells. Medical image segmentation tasks for skin lesion play a crucial role in quantitative analysis. Achieving precise and efficient segmentation remains a significant challenge for medical practitioners. Hence, a skin lesion segmentation model named MSDUNet, which incorporates multi-scale deformable block (MSD Block) and dual-input dynamic enhancement module(D2M), is proposed. Firstly, the model employs a hybrid architecture encoder that better integrates global and local features. Secondly, to better utilize macroscopic and microscopic multiscale information, improvements are made to skip connection and decoder block, introducing D2M and MSD Block. The D2M leverages large kernel dilated convolution to draw out attention bias matrix on the decoder features, supplementing and enhancing the semantic features of the decoder's lower layers transmitted through skip connection features, thereby compensating semantic gaps. The MSD Block uses channel-wise split and deformable convolutions with varying receptive fields to better extract and integrate multi-scale information while controlling the model's size, enabling the decoder to focus more on task-relevant regions and edge details. MSDUNet attains outstanding performance with Dice scores of 93.08% and 91.68% on the ISIC-2016 and ISIC-2018 datasets, respectively. Furthermore, experiments on the HAM10000 dataset demonstrate its superior performance with a Dice score of 95.40%. External validation experiments based on the ISIC-2016, ISIC-2018, and HAM10000 experimental weights on the PH2 dataset yield Dice scores of 92.67%, 92.31%, and 93.46%, respectively, showcasing the exceptional generalization capability of MSDUNet. Our code implementation is publicly available at the Github.

BackgroundThis study examines the relationship between public health insurance and inequality in nursing services for older adults and explores the different impacts of different public health insurance systems in China.MethodsUsing data from the China Health and Retirement Longitudinal Study, a logistic regression model was used to examine the use of nursing services by people aged 60 years and above.ResultsThe results show that participation in public health insurance can significantly improve the chances of older adults accessing nursing services, with participants being 53.57% more likely than nonparticipants to use nursing services. In addition, older adults enrolled in the Urban Employee Basic Medical Insurance program were 26.49% more likely than those enrolled in the Urban‒Rural Resident Basic Medical Insurance program to utilize nursing services, underscoring the substantial inequalities resulting from the structural differences within the public health insurance system.ConclusionsAlthough public health insurance plays a key role in improving the accessibility of nursing services for older adults, it also exacerbates existing inequalities. China’s health security reform should improve access to nursing services by increasing participation in public health insurance and eliminating discrimination based on status within the insurance framework.

Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for automated depression detection, leveraging bimodal data from voice and text. Wav2Vec 2.0 and BERT pre-trained models were utilized for feature extraction, while a multi-scale convolutional layer and Bi-LSTM network were employed for feature fusion and classification. Adaptive pooling was used to integrate features, enabling simultaneous depression classification and PHQ-8 severity estimation within a unified system.Experiments on the CMDC and DAIC datasets demonstrate the model’s effectiveness. On CMDC, the F1 score improved by 0.0103 and 0.2017 compared to voice-only and text-only models, respectively, while RMSE decreased by 0.5186. On DAIC, the F1 score increased by 0.0645 and 0.2589, with RMSE reduced by 1.9901. These results highlight the proposed method’s ability to capture and integrate multi-level information across modalities, significantly improving the accuracy and reliability of automated depression detection and severity prediction.