Osteoarthritis (OA) is the most prevalent joint disease, yet effective disease-modifying OA drugs (DMOADs) remain elusive. Targeting macrophage polarization has emerged as a promising avenue for OA treatment. This study identified skatole through high-throughput screening as an efficient modulator of macrophage polarization. In vivo experiments demonstrated that skatole administration markedly reduced synovitis and cartilage damage in both destabilization of medial meniscus (DMM)-induced OA mice and monosodium iodoacetate (MIA)-induced OA rats. Mechanistically, skatole activated signal transducer and activator of transcription 6 (Stat6) signaling, promoting M2 macrophage polarization, while inhibiting nuclear factor-κB (NFκB) and mitogen-activated protein kinase (MAPK) signaling pathways to suppress M1 polarization. RNA-sequencing analysis, targeted metabolomics, and mitochondrial stress tests further revealed that skatole treatment shifted macrophages toward oxidative phosphorylation for energy production. Additionally, it up-regulated genes associated with glutathione metabolism and reactive oxygen species (ROS) pathways, reducing intracellular ROS production. The CUT&Tag assay results indicated that the downstream transcription factor p65 of NFκB can directly bind to gene loci related to inflammation, oxidative phosphorylation, and glutathione metabolism, thereby modulating gene expression. This regulatory process is inhibited by skatole. At the chondrocyte level, conditional medium from skatole-treated M1 macrophages balanced anabolism and catabolism in mouse chondrocytes and inhibited apoptosis. In IL1β-treated chondrocytes, skatole suppressed inflammation and catabolism without affecting apoptosis or anabolism. Overall, skatole maintains immune microenvironment homeostasis by modulating macrophage polarization in joints and preserves cartilage function by balancing chondrocyte anabolism and catabolism, effectively alleviating OA. These findings suggest skatole's potential as a DMOAD.

We apply the recently developed concept of the nucleon energy–energy correlator (NEEC) for the gluon sector to investigate the long-range azimuthal angular correlations in proton–proton collisions at the Large Hadron Collider. The spinning gluon in these collisions will introduce substantial nonzero \text{cos} \left( 2\varphi \right) asymmetries in both Higgs boson and top quark pair productions, where \varphi is the azimuthal angle between the forward and backward energy correlators in the NEEC observables. The genesis of the \text{cos} \left( 2\varphi \right) correlation lies in the intricate quantum entanglement. Owing to the substantial \text{cos} \left( 2\varphi \right) effect, the NEEC observable in Higgs boson and t \overline{t} production emerges as a pivotal avenue for delving into quantum entanglement and scrutinizing the Bell inequality at high-energy colliders.

Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. However, existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets, sparsity of valid data, and lack of classification in datasets, resulting in poor generalization capability and accuracy. In this study, a dataset for 10,905 compounds was constructed including their retention times. Next, an innovative classification system was implemented, classifying 10,905 compounds into a 3-tier hierarchy across 141 classes, based on functional group weighting. Then, data augmentation was performed within each category using simplified molecular input line entry system (SMILES) enumeration combined with structural similarity expansion. Finally, by training the optimal quantitative structure–retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. The results demonstrate that this model achieves an R² of 0.98 and an average prediction error of 23 s, outperforming currently published models. This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc.

Background: Cardiovascular diseases (CVD) are a major global health issue strongly associated with altered lipid metabolism. However, lipid metabolism-related pharmacological targets remain limited, leaving the therapeutic challenge of residual lipid-associated cardiovascular risk. The purpose of this study is to identify potentially novel lipid metabolism-related genes by systematic genomic and phenomics analysis, with an aim to discovering potentially new therapeutic targets and diagnosis biomarkers for CVD. Methods: In this study, we conducted a comprehensive and multidimensional evaluation of 881 lipid metabolism-related genes. Using genome-wide association study (GWAS)-based mendelian randomization (MR) causal inference methods, we screened for genes causally linked to the occurrence and development of CVD. Further validation was performed through colocalization analysis in 2 independent cohorts. Then, we employed reverse screening using phenonome-wide association studies (PheWAS) and a drug target–drug association analysis. Finally, we integrated serum proteomic data to develop a machine learning model comprising 5 proteins for disease prediction. Results: Our initial screening yielded 54 genes causally linked to CVD. Colocalization analysis in validation cohorts prioritized this to 29 genes marked correlated with CVD. Comparison and interaction analysis identified 13 therapeutic targets with potential for treating CVD and its complications. A machine learning model incorporating 5 proteins for CVD prediction achieved a high accuracy of 96.1%, suggesting its potential as a diagnostic tool in clinical practice. Conclusion: This study comprehensively reveals the complex relationship between lipid metabolism regulatory targets and CVD. Our findings provide new insights into the pathogenesis of CVD and identify potential therapeutic targets and drugs for its treatment. Additionally, the machine learning model developed in this study offers a promising tool for the diagnosis and prediction of CVD, paving the way for future research and clinical applications.

The rapid evolution of deep learning has markedly enhanced protein–biomolecule binding site prediction, offering insights essential for drug discovery, mutation analysis, and molecular biology. Advancements in both sequence-based and structure-based methods demonstrate their distinct strengths and limitations. Sequence-based approaches offer efficiency and adaptability, while structure-based techniques provide spatial precision but require high-quality structural data. Emerging trends in hybrid models that combine multimodal data, such as integrating sequence and structural information, along with innovations in geometric deep learning, present promising directions for improving prediction accuracy. This perspective summarizes challenges such as computational demands and dynamic modeling and proposes strategies for future research. The ultimate goal is the development of computationally efficient and flexible models capable of capturing the complexity of real-world biomolecular interactions, thereby broadening the scope and applicability of binding site predictions across a wide range of biomedical contexts.

Smart contact lenses (SCLs), an innovative evolution of conventional contact lenses, have recently attracted increasing attention for their substantial potential for use in the healthcare field. With advancements in materials science and medical technology, SCLs have integrated electronic information technology with biomedical engineering to enable the incorporation of various medical functionalities. Recent developments have focused on applying SCLs to provide intelligent, efficient, and personalized healthcare solutions in the surveillance, diagnosis, and treatment of chronic ocular surface inflammation, glaucoma, and diabetes complications.

Emerging evidence highlights the central role of gut microbiota in maintaining physiological homeostasis within the host. Disruptions in gut microbiota can destabilize systemic metabolism and inflammation, driving the onset and progression of cardiometabolic diseases. In heart failure (HF), intestinal dysfunction may induce the release of endotoxins and metabolites, leading to dysbiosis and exacerbating HF through the gut–heart axis. Understanding the relationship between gut microbiota and HF offers critical insights into disease mechanisms and therapeutic opportunities. Current research highlights promising potential to improve patient outcomes by restoring microbiota balance. In this review, we summarize the current studies in understanding the gut microbiota–HF connection and discuss avenues for future investigation.

Understanding long-term historical changes in cloudiness is essential for elucidating Earth's climate dynamics and variability and its extremes. In this study, we present the first millennial-length reconstruction of the annual total cloud cover (TCC) in the western Mediterranean, covering the period from 969 to 2022 CE. Based on a comprehensive set of hydrological and atmospheric variables, our reconstruction reveals a nuanced pattern of cloudiness evolution over the past millennium. We observe an initial increase in cloudiness until 1600 CE, followed by a substantial decrease in TCC. This shift was driven by a confluence of factors, including the eruption of Mount Tambora in Indonesia in 1815, increased solar forcing, and a positive phase of the Atlantic Multidecadal Oscillation. These complex dynamics have brought modern warming cloud patterns closer to those observed during the medieval period before c. 1250, exceeding the background variability of the Little Ice Age (c. 1250 to 1849). In particular, recent decades have witnessed an unprecedented coupling of intense solar activity, high temperatures, and the lowest cloud cover on record. Our results highlight the importance of inter-oceanic-scale relationships between Atlantic forcing mechanisms and the TCC in shaping future trends in western Mediterranean cloudiness. This study provides valuable insights into the long-term dynamics of cloudiness and its implications for regional climate trends in the western Mediterranean and beyond.

Biomimetic dry adhesive structures, inspired by geckos' climbing abilities, have attracted research attention in recent years. However, achieving superior adhesion on a rough surface remains an important challenge, which limits practical applications. Conventional bionic adhesion methods perform well on smooth surfaces, but adhesion strength drastically decreases on rough surfaces due to the reduced contact area. Generally, various adhesive structures have been proposed to increase the contact area without assessing adhesion states, against obtaining good performance on rough surfaces. If an intelligent adhesive approach could be introduced on rough surfaces, it would be beneficial for promoting the development of gecko-inspired adhesives. However, existing adhesive structures with the sensing function usually utilize the adhesive function to support the sensing function, i.e., a sensor with an adhesive function; for other few structures, the sensing function supports adhesion, but they do not focus on improving adhesion performance on rough surfaces. Inspired by the synergistic effect of a kinematic system during the crawling process of geckos, this study proposes an intelligent adhesive structure for rough surfaces. The proposed structure combines a hierarchical bionic dry adhesive structure based on gecko paw microhairs with a flexible capacitive sensor unit. Experimental observations and analytical modeling demonstrate that incorporating mushroom-shaped bionic dry adhesive structures with inclined support micropillars can reduce interface contact stiffness, notably enhancing adhesion on rough surfaces while allowing real-time monitoring of contact states. Moreover, this innovative smart adhesive structure facilitates morphology sensing of contact interfaces, presenting potential advancements in bionic adhesion for morphology sensing applications.

Human bone marrow stem cells (hBMSCs) play an important role during the fracture healing phase. Previous clinical studies by our research group found that fracture healing time was obviously delayed in patients who underwent splenectomy, for combined traumatic fractures and splenic rupture, which is most likely related to the dysregulation of immune inflammatory function of the body after splenectomy. A large number of studies have reported that the inflammatory factor interleukin-1β plays an important role in the multi-directional differentiation ability and immune regulation of BMSC, but its specific regulatory mechanism needs to be further studied. Recently, long noncoding RNAs (lncRNAs) have attracted remarkable attention owing to their close relationship with stem cell osteogenesis and potential role in various bone diseases. In this study, we explored the molecular mechanism of a novel lncRNA, LncMSTRG.11341.25 (LncMSTRG25), in terms of its effects on osteogenic differentiation of hBMSCs. Our results reveal significant up-regulation of LncMSTRG25, osteogenic differentiation markers during the osteogenic differentiation of hBMSCs, and decreased expression of miR-939-5p with an increase in differentiation time. LncMSTRG25 knockdown significantly inhibited the osteogenic ability of hBMSCs. When we knocked down PAX8 alone, we found that the osteogenic ability of hBMSCs was also significantly reduced. The interaction between LncMSTRG25 and PAX8 was verified using the RNA immunoprecipitation assay, RNA pull-down assays, silver staining, and the dual-luciferase reporter. The results show that LncMSTRG25 can function as a sponge to adsorb miR-939-5p, inducing the osteogenic differentiation of hBMSCs by activating PAX8. These findings deepen our understanding of the regulatory role of lncRNA–miRNA–mRNA networks in the immune microenvironment of bone marrow, and highlight the important role played by the spleen as an immune organ in fracture healing.