Human breast milk serves as a vital source of nutrition for infants, and it also plays a critical role in shaping the infant gut microbiota and establishing intestinal homeostasis. This process substantially impacts immune function, neurodevelopment, and overall health. The noninvasive nature of breast milk collection makes it an ethical and accessible area for research, positioning it as a key focus for future studies. These future directions include the identification of novel bacteria combination, the establishment of comprehensive databases on infant microbiota, and the use of computational models to predict interactions between breast milk components and the gut microbiome. Additionally, the creation of diverse biological models and the establishment of infant stool banks will further enhance understanding of host–microbiome interactions and support disease prevention strategies.

The brittleness of traditional ceramics severely limits their application progress in engineering. The multiscale structural design of organisms can solve this problem, but it still lacks sufficient research and attention. The underlined main feature is the multiscale hierarchical structures composed of basic nano–microstructure units arranged in order, which is currently impossible to achieve through artificial synthesis driven by high temperatures. This perspective aims to bridge the gap between biostructural materials and biomimetic ceramics, highlighting the relationship between bioinspired structures and interfacial interaction of structure densification in biomimetic ceramics. Therefore, we could accomplish densification and ceramic development at room temperature, consequently correlating the structure, properties, and functions of materials and accelerating the development of the next generation of advanced functional ceramics.

Digital light processing (DLP) is a high-speed, high-precision 3-dimensional (3D) printing technique gaining traction in the fabrication of ceramic composites. However, when printing 0-3 composites containing lead zirconate titanate (PZT) particles, a widely used piezoelectric ceramic, severe density and refractive index mismatches between the 2 phases pose challenges for ink synthesis and the printing process. Here, we systematically and quantitatively optimized DLP printing of PZT composites, streamlining process development and providing a solid theoretical and experimental foundation for broader applications of DLP technology. PZT particles were pretreated with air plasma to improve slurry uniformity and enhance stress transfer at the composite interface, leading to improved chemical modification, mechanical strength, and piezoelectric properties. We investigated the effects of key process parameters on printability and accuracy by analyzing the curing behavior of PZT–polymer composites. A quantitative model of the DLP curing process was introduced. Unlike stereolithography (SLA), DLP curing depth was found to depend on energy dose and light intensity, with higher intensities proving more favorable for printing 0-3 PZT composites. From depth/width–energy curves, optimal process parameters were determined. We designed and fabricated a soft piezoelectric metamaterial-based touch sensor using these parameters, achieving a customized output profile. This work offers critical insights into optimizing DLP for functional materials and expands the potential of 3D-printed piezoelectric composites.

Developing advanced tissue-engineered membranes with biocompatibility, suitable mechanical qualities, and anti-fibrotic and anti-inflammatory actions is important for tympanic membrane (TM) repair. Here, we present a novel acoustically transmitted decellularized fish swim bladder (DFB) loaded with mesenchymal stem cells (DFB@MSCs) for TM perforation (TMP) repair. The DFB scaffolds are obtained by removing the cellular components from the original FB, which retains the collagen composition that favors cell proliferation. Benefitting from their spatially porous structures and excellent mechanical properties, the DFB scaffolds can provide a suitable microenvironment and mechanical support for cell growth and tissue regeneration. In addition, by loading mesenchymal stem cells on the DFB scaffolds, the resultant DFB@MSCs system exhibits remarkable anti-fibrotic and anti-inflammatory effects, together with the ability to promote cell migration and angiogenesis. In vivo experiments confirm that the prepared DFB@MSCs scaffolds can not only alleviate inflammatory response caused by TMP but also promote new vessel formation, TM repair, and hearing improvement. These features indicate that our proposed DFB@MSCs stent is a prospective tool for the clinical repair of TM.

The study of many phenomena in the terahertz (THz) frequency spectral range has emerged as a promising playground in modern science and technology, with extensive applications in high-speed communication, imaging, sensing, and biosensing. Many THz metamaterial designs explore quantum physics phenomena embedded into a classical framework and exhibiting various unexpected behaviors. For spatial THz waves, the effects inspired by quantum phenomena include electromagnetically induced transparency (EIT), Fano resonance, bound states in the continuum (BICs), and exceptional points (EPs) in non-Hermitian systems. They facilitate the realization of extensive functional metadevices and applications. For on-chip THz waves, quantum physics-inspired topological metamaterials, as photonic analogs of topological insulators, can ensure robust, low-loss propagation with suppressed backscattering. These trends open new pathways for high-speed on-chip data transmission and THz photonic integrated circuits, being crucial for the upcoming 6G and 7G wireless communication technologies. Here, we summarize the underlying principles of quantum physics-inspired metamaterials and highlight the latest advances in their application in the THz frequency band, encompassing both spatial and on-chip metadevice realizations.

Simulated microgravity (SMG) poses substantial challenges to astronaut health, particularly impacting osteoblast function and leading to disuse osteoporosis. This study investigates the adverse effects of SMG on osteoblasts, focusing on changes in mitochondrial dynamics and their consequent effects on cellular energy metabolism and mechanotransduction pathways. We discovered that SMG markedly reduced the expression of osteoblast differentiation markers and promoted mitochondrial fission, as indicated by an increase in punctate mitochondria, a decrease in mitochondrial length, and a reduction in cristae density. These mitochondrial alterations are linked to elevated reactive oxygen species levels, a decrease in ΔΨm, and a metabolic shift from oxidative phosphorylation to glycolysis, resulting in decreased adenosine triphosphate production, which are all indicative of mitochondrial dysfunction. Our results showed that treatment with mitochondrial division inhibitor-1 (mdivi-1), a mitochondrial fission inhibitor, effectively inhibited these SMG-induced effects, thereby maintaining mitochondrial structure and function and promoting osteoblast differentiation. Furthermore, SMG disrupted critical mechanotransduction processes, by affecting paxillin expression, the RhoA–ROCK–Myosin II pathway, and actin dynamics, which subsequently altered nuclear morphology and disrupted Yes-associated protein signaling. Notably, treatment with mdivi-1 prevented these disruptions in mechanotransduction pathways. Moreover, our study showed that SMG-induced chromatin remodeling and histone methylation, which are epigenetic barriers to osteogenic differentiation, can be reversed by targeting mitochondrial fission, further highlighting the significance of mitochondrial dynamics in osteoblast function in an SMG environment. Therefore, targeting mitochondrial fission emerges as a promising therapeutic strategy to alleviate osteoblast dysfunction under SMG conditions, providing novel approaches to maintain bone health during prolonged space missions and safeguard the astronaut well-being.

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.

Atherosclerosis (AS) is a chronic inflammatory condition influenced by glucose and lipid disorders, oxidative stress, and thrombosis, reflecting the complexity of its pathological process. The development of accurate experimental models that simulate human AS is essential for understanding its initiation and progression. This review summarizes the current AS research models and analyzes their specific application scenarios. We discuss tissue-engineered blood vessels (TEBVs) and vessels-on-a-chip (VoCs), which leverage tissue engineering and precise microenvironmental control to construct in vitro models that closely resemble the structure and function of human AS. Isolated vessel segments from live animals provide a valuable tool for investigating human AS due to their physiological similarity, controllability, and reproducibility. The review further outlines the construction of AS animal models through high-fat diets and gene-editing techniques, highlighting how immune-inflammatory responses, mechanical arterial injury, and hemodynamic changes accelerate model development. This comprehensive analysis highlights the potential of AS models to revolutionize theranostic applications in clinical translational research, paving the way for more personalized and effective treatments for AS in the near future.

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.

Excessive gonadotropin-releasing hormone (GnRH) is considered to be an initiating factor in the etiology of polycystic ovary syndrome (PCOS). GnRH neuronal axons terminate at the hypothalamic arcuate nucleus and median eminence, where tanycytes, specialized glial cells, have been proposed to modulate GnRH secretion through plasticity. However, the precise role of the “GnRH-tanycyte unit” during the pathological state of PCOS has not been thoroughly explored. In this study, we demonstrated the architecture and distribution of GnRH neurons and tanycytes. In PCOS-like mice, retracted tanycyte processes and dysregulated GnRH-tanycyte unit may create an environment conducive to the excessive secretion of GnRH and subsequent reproductive endocrine dysfunction. Mechanistically, excessive androgens impair hypothalamic neuroglial homeostasis by acting through the androgen receptor (AR) and its downstream target integrin β1 (Itgb1), thereby suppressing the FAK/TGF-βR1/Smad2 signaling pathway. Both selective deletion of AR and overexpression of Itgb1 in tanycytes counteracted the detrimental effects of androgens, alleviating endocrine dysfunction. Collectively, this study highlights the alterations in the GnRH-tanycyte unit mediated by androgen/AR/Itgb1 signaling and provides a novel perspective for developing therapies for hypothalamic hormone secretion disorders by maintaining solid neuroglial structures in the brain.

Atomic-scale polar topologies such as skyrmions offer important potential as technological paradigms for future electronic devices. Despite recent advances in the exploration of topological domains in complicated perovskite oxide superlattices, these exotic ferroic orders are unavoidably disrupted at the atomic scale due to intrinsic size effects. Here, based on first-principles calculations, we propose a new strategy to design robust ferroelectricity in atomically thin films by properly twisting 2 monolayers of centrosymmetric SrTiO₃. Surprisingly, the emerged polarization vectors curl in the plane, forming a polar skyrmion lattice with each skyrmion as small as 1 nm, representing the highest polar skyrmion density to date. The emergent ferroelectricity originates from strong interlayer coupling effects and the resulting unique strain fields with obvious ion displacements, contributing to electric polarization comparable to that of PbTiO₃. Moreover, we observe ultraflat bands (band width of less than 5 meV) at the valence band edge across a wide range of twist angles, which show widths that are smaller than those of common twisted bilayers of 2-dimensional materials. The present study not only overcomes the critical size limitation for ferroelectricity but also reveals a novel approach for achieving atomic-scale polar topologies, with important potential for applications in skyrmion-based ultrahigh-density memory technologies.

Macrophages are ubiquitous within the human body and serve pivotal roles in immune surveillance, inflammation, and tissue homeostasis. Phenotypic plasticity is a hallmark of macrophages, allowing their polarization into distinct phenotypes M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor) in response to local microenvironmental cues. In tumor tissues, the polarization of tumor-associated macrophages profoundly shapes the tumor microenvironment, influencing tumor progression, immune evasion, and metastasis. Therefore, the ability to image and monitor macrophage polarization is essential for comprehending tumor biology and optimizing therapeutic strategies. With the rapid advancement of nanomedicine, a diverse array of nanoprobes has been engineered to specifically target tumor-associated macrophages, offering new avenues for noninvasive in vivo imaging and real-time monitoring of macrophage dynamics within the tumor microenvironment. This perspective highlights recent advancements in macrophage-targeting nanoprobes for imaging macrophage polarization both in vitro and in vivo. It also addresses the current challenges in the field, such as enhancing probe sensitivity, specificity, and biocompatibility, while outlining the future directions for the development of next-generation nanoprobes aimed at precision oncology.

Nano-/microrobots have been demonstrated as an efficient solution for environmental remediation. Their strength lies in their propulsion abilities that allow active “on-the-fly” operation, such as pollutant detection, capture, transport, degradation, and disruption. Another advantage is their versatility, which allows the engineering of highly functional solutions for a specific application. However, the latter advantage can bring complexity to applications; versatility in dimensionality, morphology, materials, surface decorations, and other modifications has a crucial effect on the resulting propulsion abilities, compatibility with the environment, and overall functionality. Synergy between morphology, materials, and surface decorations and its projection to the overall functionality is the object of nanoarchitectonics. Here, we scrutinize the engineering of nano-/microrobots with the eyes of nanoarchitectonics: we list general concepts that help to assess the synergy and limitations of individual procedures in the fabrication processes and their projection to the operation at the macroscale. The nanoarchitectonics of nano-/microrobots is approached from microscopic level, focusing on the dimensionality and morphology, through the nanoscopic level, evaluating the influence of the decoration with nanoparticles and quantum dots, and moving to the decorations on molecular and single-atomic level to allow very fine tuning of the resulting functionality. The presented review aims to lay general concepts and provide an overview of the engineering of functional advanced nano-/microrobot for environmental remediation procedures and beyond.

Circular RNAs (circRNAs) are differentially expressed in cardiac hypertrophy; however, the exact function and mechanisms during hypertrophy development are still unknown. Here, we explored the role of a newly discovered circRNA in the pathogenesis of myocardial hypertrophy. It was found that circ-0001283 promoted the progression of cardiac hypertrophy by interacting with myosin light chain 3 (MYL3) to inhibit the protein ubiquitination and enhance its protein expression, not by the competitive endogenous RNA mechanism. Further investigation demonstrated that the reduced hypertrophy induced by circ-0001283 knockdown was counteracted by overexpression of MYL3. Mechanistically, MYL3 facilitated myocardial hypertrophy by inducing autophagy in cells via PI3K/Akt/mTOR and ERK signaling pathways. In summary, circ-0001283 can bind directly to MYL3 and up-regulate its expression, thereby promoting autophagy to accelerate cardiac hypertrophy. Circ-0001283 may serve as a potential therapeutic target for cardiac hypertrophy.

Marine biofouling causes severe economical and environmental challenges to marine industries and maritime activities. Biofouling prevention has emerged as one of the most pressing issues in water-related industries. Recently, the slippery liquid-infused porous surfaces (SLIPSs) have shown great potential for biofouling prevention across a broad spectrum of fouling organisms. However, our understanding of the mechanisms by which SLIPSs prevent biofouling remains limited. In this study, we discovered that oil-infused polydimethylsiloxane elastomer (i-PDMS), a silicone-based SLIPS variant, significantly inhibited the sensory responses of the fouling mussel Mytilopsis sallei, particularly at its sensory organ, the foot. Using bioinformatics and molecular biology analyses, we demonstrated that i-PDMS disrupts larval settlement of M. sallei by interfering with the mechanosensitive transient receptor potential melastatin-subfamily member 7 (TRPM7) channel, which is highly expressed in the foot during the settlement process. Furthermore, adhesion assays and molecular dynamics simulations revealed that the secreted foot proteins of the mussel are unable to effectively interact with the i-PDMS surface due to nanoscale fluctuations at the material interface. These findings enhance our understanding of how fouling organisms sense and adhere to surfaces and provide deeper insights into the antifouling mechanisms of SLIPS.

Familial hypercholesterolemia (FH) is a lipoprotein disorder characterized by elevated plasma levels of low-density lipoprotein cholesterol (LDL-C) and an increased risk of premature atherosclerotic cardiovascular disease. Recent evidences have shown that several glycerophospholipid species were markedly altered in experimental FH animals and exhibited diverse bioactivities. Nevertheless, the glycerophospholipid profiles and their associated biological implications in human FH remain largely unknown. In this study, we sought to comprehensively delineate the glycerophospholipid phenotypes in human FH and to investigate the functional roles of key FH-altered glycerophospholipid molecules on cholesterol metabolism. Targeted analysis of 328 glycerophospholipid metabolites was used to profile the differentiated alterations in patients with homozygous FH (HoFH; n = 181), heterozygous FH (HeFH; n = 452), and non-FH hypercholesterolemia (n = 382). Our findings revealed that the glycerophospholipid phenotypes of FH and non-FH hypercholesterolemia were dominated by a spectrum of metabolites involved in the lysophosphatidic acid (LPA) metabolism. Among the LPA features, palmitoyl-LPA (16:0) showed significant association with the clinical levels of LDL-C and total cholesterol in HoFH and HeFH populations. Using functional metabolomic strategy and murine FH model, we demonstrated that supplementation with LPA 16:0 elevated the plasma levels of LDL and free/esterified cholesterol and exacerbated the atherosclerotic lesions. Conversely, inhibition of autotaxin-mediated LPA 16:0 production significantly ameliorated dyslipidemia. Mechanistically, we uncovered that LPA 16:0 could disrupt hepatic cholesterol homeostasis by impairing cholesterol excretion and inhibiting primary bile acid synthesis. In summary, our study offers novel insights into lipid metabolism in human FH and posits that targeting LPA metabolism may represent a promising therapeutic strategy for reducing cholesterol levels in the FH population.

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.