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.

Nanozymes are a class of nanomaterials that exhibit catalytic functions analogous to those of natural enzymes. They demonstrate considerable promise in the biomedical field, particularly in the treatment of bone infections, due to their distinctive physicochemical properties and adjustable catalytic activities. Bone infections (e.g., periprosthetic infections and osteomyelitis) are infections that are challenging to treat clinically. Traditional treatments often encounter issues related to drug resistance and suboptimal anti-infection outcomes. The advent of nanozymes has brought with it a new avenue of hope for the treatment of bone infections.

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.

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.

Given the important role of iodine resources in chemical industry application and the scarcity of geogenic iodine resources, sustainable access to iodine resources has become increasingly crucial. Seawater is the largest iodine reservoir on Earth, but efficient chemical methods for recovering iodine from seawater are still lacking. Concurrently, the remediation of radioactive iodine pollution in seawater, caused by nuclear accident, remains a great challenge. Supramolecular organic frameworks (SOFs) are considered promising candidates for the recovery of aqueous iodine. However, currently available SOF adsorbents lack sufficient iodine storage space, resulting in low iodine adsorption capacity. Herein, we developed a 3-dimensional (3D) SOF, named SOF-HTNI, via the self-assembly of 2 adjustable compounds, including the internal amine bond-rotatable 5-(bis(4-carboxybenzyl)amino)isophthalic acid (HT) and the configuration-transformable 4,4′-[1,4-phenylenedi-(1E)-2,1-ethenediyl]bis-pyridine (NI), for highly efficient iodine recovery from seawater. Due to the rigid support and the formation of hydrogen bonds and π–π stacking interactions between the compounds, interconnected 1D channels and 2D interlayer nanospaces are constructed within SOF-HTNI, providing abundant flexible spaces for iodine storage. By combining the charge interaction of the amine and pyridyl groups from the compounds with the binding ability of aromatic rings, SOF-HTNI achieves impressive iodine adsorption capacities of 436.56 mg g⁻¹ to iodide and 5.03 g g⁻¹ to triiodide. Notably, SOF-HTNI realizes a high iodine capture capacity of 46 mg g⁻¹ in natural seawater, 40 times greater than that of seaweed. These findings make SOF-HTNI a highly promising material for iodine pollution treatment and iodine resource recovery in seawater.

The prevention of air pollution-related cardiopulmonary disorders has been largely overlooked despite its important burden. Extracellular vesicles (EVs) have shown great potential as carriers for drug delivery. However, the efficiency and effect of EVs derived from different sources on ambient fine particulate matter (PM2.5)-induced cardiopulmonary injury remain unknown. Using PM2.5-exposed cellular and mouse models, we investigated the prevention of air pollution-related cardiopulmonary injury via an innovative strategy based on EV delivery. By using a “2-step” method that combines bibliometric and bioinformatic analysis, we identified superoxide dismutase 2 (Sod2) as a potential target for PM2.5-induced injury. Sod2-overexpressing plasmid was constructed and loaded into human plasma-, bovine milk-, and fresh grape-derived EVs, ultimately obtaining modified nanoparticles including PEV^Sod2, MEV^Sod2, and GEV^Sod2, respectively. GEV^Sod2, especially its lyophilized GEV^Sod2 powder, exhibited superior protection against PM2.5-induced cardiopulmonary injury as compared to PEV^Sod2 and MEV^Sod2. High-sensitivity structured illumination microscopy imaging and immunoblotting showed that GEV^Sod2 powder treatment altered lysosome positioning by reducing Rab-7 expression. Our findings support the use of fruit-derived EVs as a preferred candidate for nucleic acid delivery and disease treatment, which may facilitate the translation of treatments for cardiopulmonary injuries.

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.

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.

Thrombus is a blood clot that forms in a blood vessel at the point of flaking. Thrombosis is closely associated with cardiovascular diseases caused by different sources and factors. However, the current clinical methods of thrombus diagnosis and treatment still have problems with targeting, permeability, stability, and biosafety. Therefore, in recent years, based on the development of micro/nano technology, researchers have tried to develop some new strategies for the diagnosis and treatment of thrombosis. Due to the unique structural characteristics, the micro-nano materials in physiological environments show excellent transport and delivery properties such as better in vivo circulation, longer life span, better targeting ability, and controllable cellular internalization. Especially, elasticity and stiffness are inherent mechanical properties of some well-designed micro-nano materials, which can make them better adapted to the needs of thrombosis diagnosis and treatment. Herein, this review first introduces the thrombotic microenvironment to characterize the thrombus development process. Then, to fine-tune the pathological occurrence and development of thrombosis, the role of elastic micro-nano materials for thrombus diagnosis and treatment is summarized. The properties, preparation methods, and biological fate of these materials have been discussed in detail. Following, the applications of elastic micro-nano materials in biomedical imaging, drug delivery, and therapy of thrombosis are highlighted. Last, the shortcomings and future design strategies of elastic micro-nano materials in diagnosis and treatment of clinical thrombosis are discussed. This review will provide new ideas for the use of nanotechnology in clinical diagnosis and treatment of thrombus in the future.

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.