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Methyltransferase-like 3 (METTL3), an RNA N6-methyladenosine (m6A) methyltransferase, is essential for the m6A mRNA modification. As a key enzyme of m6A methylation modification, METTL3 has been implicated in immune and inflammation regulation. However, little is known of the role and underlying mechanism of METTL3 in rheumatoid arthritis (RA). The aim of the present study is to elucidate the function and potential mechanism of METTL3 in RA pathogenesis. We used quantitative real-time polymerase chain reaction to detect the expression of METTL3 in RA patients and controls as well as the macrophage cell line. CCK-8 was used for cell proliferation assay. Enzyme-linked immunosorbent assay (ELISA) was adopted to estimate the generation of IL-6 and TNF-α in macrophages. Western blot and immunofluorescence were applied to evaluate the activation of NF-κB in macrophages. The expression of METTL3 was significantly elevated in patients with RA. It was positively associated with CRP and ESR, two common markers for RA disease activity. Besides, LPS could enhance the expression and biological activity of METTL3 in macrophages, while overexpression of METTL3 significantly attenuated the inflammatory response induced by LPS in macrophages. Moreover, the effect of METTL3 on LPS-induced inflammation in macrophages was dependent on NF-κB. This study firstly demonstrates the critical role of METTL3 in RA, which provides novel insights into recognizing the pathogenesis of RA and a promising biomarker for RA.

Rheumatoid arthritis is a common systemic and autoimmune disease characterized by symmetrical and inflammatory destruction of distal joints. Its primary pathological characters are synovitis and vasculitis. Accumulating studies have implicated the critical role of non-coding RNAs (ncRNAs) in inflammation and autoimmune regulation, primarily including microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). NcRNAs are significant regulators in distinct physiological and pathophysiological processes. Many validated non-coding RNAs have been identified as promising biomarkers for the diagnosis and treatment of RA. This review will shed some light on RA pathogenesis and be helpful for identifying potential ncRNA biomarkers for RA.

Background Takayasu arteritis (TAK) is characterized by pro-inflammatory M1 macrophage infiltration and increased interferon (IFN)-γ expression in vascular lesions. IFN-γ is a key cytokine involved in M1 polarization. Macrophage polarization is accompanied by metabolic changes. However, the metabolic regulation mechanism of IFN-γ in M1 macrophage polarization in TAK remains unclear. Methods Immunohistochemistry and immunofluorescence were employed to observe the expression of IFN-γ, PFKFB3 (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3, the rate-limiting enzyme in glycolysis), and macrophage surface markers in the vascular tissue. Monocyte-derived macrophages from patients with TAK were cultured to examine the role of PFKFB3 in IFN-γ-induced M1 macrophage polarization. Seahorse analysis was used to detect the alterations in glucose metabolism during this process. Quantitative reverse transcription PCR, flow cytometry, and western blot were used to confirm the phenotypes of macrophages and related signaling pathways. Results In the vascular adventitia of patients with TAK, an increase in PFKFB3 accompanied by IFN-γ expression was observed in M1 macrophages. In vitro, IFN-γ successfully induced macrophage differentiation into the M1 phenotype, which was manifested as an increase in CD80 and HLA-DR markers and the pro-inflammatory cytokines IL-6 and TNF-α. During this process, PFKFB3 expression and glycolysis levels were significantly increased. However, glycolysis and M1 polarization induced by IFN-γ were suppressed by a PFKFB3 inhibitor. In addition, JAK2/STAT1 phosphorylation was also enhanced in macrophages stimulated by IFN-γ. The effects of IFN-γ on macrophages, including the expression of PFKFB3, glycolysis, and M1 polarization, were also inhibited by the JAK inhibitor tofacitinib or STAT1 inhibitor fludarabine. Conclusion PFKFB3-mediated glycolysis promotes IFN-γ-induced M1 polarization through the JAK2/STAT1 signaling pathway, indicating that PFKFB3 plays an important role in M1 polarization mediated by IFN-γ; thus, PFKFB3 is a potential intervention target in TAK.

The e-commerce platform’s function-oriented classification basis will cause items with the same (different) raw materials to be incorrectly classified into different (same) functional categories, posing a challenge to marketing staff who create item sales statistics based on raw materials. Furthermore, it is challenging to promote the present item classification method in engineering applications since it necessitates a high number of manual markings to add labels. As a result, this paper created an item conceptual model to specify the categories and attributes of item raw materials, allowing it to screen item specification samples and automatically add category labels, generate domain-specific lexicon to extract item raw material features, and finally use a machine learning classifier to complete the classification. This research presents a verification of the suggested classification model using flour data from the Chinese e-commerce platform. The experimental results show that the self-supervised learning-based classification method proposed in this article for classifying raw materials of e-commerce items can achieve an accuracy of 91%.

Non-alcoholic fatty liver disease (NAFLD) is an important health issue worldwide. We aimed to develop a simple model to determine the presence of NAFLD in a Chinese population. A cross-sectional study with 9602 subjects was conducted. Potential predictors were entered into a stepwise logistic regression analysis to obtain the model. We used 148 patients with liver biopsy to validate this model. The model, named the ZJU index, was developed based on body mass index (BMI), fasting plasma glucose (FPG), triglycerides (TG) and the serum alanine aminotransferase (ALT) to serum aspartate transaminase (AST) ratio. The area under the receiver operating characteristic curve (AUROC) of the ZJU index to detect NAFLD was 0.822. At a value of <32.0, the ZJU index could rule out NAFLD with a sensitivity of 92.2% and at a value of >38.0, the ZJU index could detect NAFLD with a specificity of 93.4%. In patients with liver biopsy, the ZJU index could detect steatosis with good accuracy, with an AUROC of 0.896. This study revealed that the ZJU index is a helpful model to detect NAFLD for community physicians in China. It was validated not only by a validation cohort but also by pathological data.

Ischemic stroke (IS) poses a significant global health burden, with treatment efficacy often limited by the blood-brain barrier (BBB) and narrow therapeutic windows. Cell membrane-camouflaged biomimetic nanoparticles (CMC@NPs) represent an advanced drug delivery platform that integrates the versatility of synthetic nanocarriers with the biological functionality of natural cell membranes, thereby enhancing targeted delivery and immune evasion. However, a systematic assessment of their biosafety remains incomplete. This review critically evaluates both the safety profile and therapeutic efficacy of CMC@NPs in the context of IS, with a specific focus on the structure-activity relationships between their physicochemical properties and toxicological outcomes. We further explore their biosafety within the unique pathological microenvironment of IS. Key findings demonstrate that optimal particle size and surface functionalization critically determine biodistribution, enabling superior tissue penetration and prolonged circulation. Furthermore, naturally derived or engineered membrane proteins facilitate precise targeting to ischemic lesions, thereby enhancing drug accumulation and therapeutic efficacy. Concurrently, a mildly negative surface charge mitigates the risk of cerebral microvascular embolism, and targeted delivery significantly reduces systemic toxicity. The pivotal role of cell-specific uptake and clearance mechanisms in governing neurotoxicity and long-term accumulation is also emphasized. This review provides a foundational framework for the development of safer and more effective biomimetic nanomedicines for IS.

Mental health issues have become a significant global public health challenge. Traditional assessments rely on subjective methods with limited ecological validity. Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. This study aimed to provide a comprehensive review of the current state of passive sensing-based and ML technologies for mental health monitoring. We summarized the technical approaches, revealed the association patterns between behavioral features and mental disorders, and explored potential directions for future advancements. This scoping review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and was prospectively registered on the Open Science Framework. We systematically searched 7 databases (Web of Science, PubMed, IEEE Xplore, Embase, PsycINFO, Scopus, and ACM Digital Library) for studies published between January 2015 and February 2025. We included 42 peer-reviewed studies that used passive sensing from wearables or smartphones with ML to monitor clinically diagnosed mental disorders, such as depression and anxiety. Data were synthesized across technical dimensions (data collection, preprocessing, feature engineering, and ML models) and clinical associations, with behavioral features categorized into 8 domains. The 42 included studies were predominantly cohort designs (23/42, 55%), with a median sample size of 60.5 (IQR 54-99). Most studies focused on depression (23/42, 55%) and anxiety (9/42, 21%) using primarily wrist-worn devices (32/42, 76%) collecting heart rate (28/42, 67%), movement index (25/42, 60%), and step count (17/42, 40%) as key biomarkers. Deep learning models (eg, convolutional neural networks and long short-term memory) showed high accuracy, while traditional ML (eg, random forest) remained prevalent due to better interpretability. We identified critical limitations, including small samples (32/42, 76% with N<100), short monitoring periods (19/42, 45% <7 days), scarce external validation (1/42, 2%), and limited reporting on data anonymization (6/42, 14%). While passive sensing and ML demonstrate promising accuracy (eg, convolutional neural network-long short-term memory achieving 92.16% in anxiety detection), the evidence remains constrained by three key limitations: (1) methodological heterogeneity (32/42, 76% single-device studies; 19/42, 45% with <7-day monitoring), (2) high risk of bias from small samples (median 60.5, IQR 54-99 participants) and scarce external validation (1/42, 2%), and (3) ethical gaps (only 6/42, 14% addressing anonymization). These findings underscore the technology's potential to transform mental health care through objective, continuous monitoring-particularly for depression (heart rate and step count biomarkers) and anxiety (sleep and social interaction patterns). However, clinical translation requires standardized protocols, larger longitudinal studies (≥3 months), and ethical frameworks for data privacy. Future work should prioritize multimodal sensor fusion and explainable artificial intelligence to bridge the gap between technical performance and clinical deployability.

Accumulating studies have suggested that long non-coding RNAs (lncRNAs) have drawn more and more attention in rheumatoid arthritis (RA), which can function as competitive endogenous RNAs (ceRNAs) in inflammation and immune disorders. Previously, we have found that lncRNA HIX003209 is differentially expressed in RA. However, the precise mechanism of lncRNA HIX003209 in RA is still vague. We aim to elucidate the role and its targeted microRNA of lncRNA HIX003209 in RA as ceRNA. Significantly increased expression of lncRNA HIX003209 was observed in the peripheral blood mononuclear cells (PBMCs) from RA cases. It was positively associated with TLR2 and TLR4 in RA. Besides, peptidoglycan (PGN) and lipopolysaccharide (LPS) could enhance the expression of lncRNA HIX003209, which reversely promoted the proliferation and activation of macrophages through IκBα/NF-κB signaling pathway. Moreover, HIX003209 was involved in TLR4-mediated inflammation via targeting miR-6089 in macrophages. LncRNA HIX003209 functions as a ceRNA and exaggerates inflammation by sponging miR-6089 through TLR4/NF-κB pathway in macrophages, which offers promising therapeutic strategies for RA.

In this study, 356 MYB transcription factors were identified from the genome of honeysuckle, and combined with transcriptome data analysis, 104 of them were found to respond to drought stress. Through phylogenetic analysis, qRT-PCR analysis and correlation analysis of chemical components, three target genes LjMYB3 , LjMYB8 and LjMYB63 were screened from these 104 MYB transcription factors and overexpressed in Arabidopsis thaliana . The results showed that the drought resistance, total flavonoid content and flavonoid biosynthesis-related gene expression levels of transgenic Arabidopsis were higher than those of wild-type Arabidopsis .Under drought stress, MYB transcription factors can activate the expression of drought resistance-related genes, regulate key genes in the flavonoid synthesis pathway, and promote the accumulation of flavonoids. This study promotes the research on transcriptional regulation of honeysuckle MYB transcription factors and flavonoid biosynthesis and lays a foundation for unraveling the drought resistance mechanism in honeysuckle.

•Based on GMV deviations, we identified two distinct subtypes of structural changes in MDD: one characterized by GMV reduction and the other by GMV increase.•Our study found that the GMV increase subtype does not exhibit signs of accelerated brain aging.•We discovered that different structural subtypes of MDD share common epicenters within the DMN, while also possessing their own specific epicenters.•We revealed that the heterogeneity of structural changes in MDD involves distinct microscopic molecular characteristics. Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD. [Display omitted]