Homologous recombination (HR) is a high-fidelity DNA double-strand break repair pathway that plays a central role in preserving genome stability and suppressing tumorigenesis. Core HR proteins, including CtIP, BRCA1, and RAD51, function in a tightly coordinated manner to mediate DNA end resection, pathway choice, and homology-directed strand exchange. Increasing evidence indicates that the abundance, activity, and chromatin engagement of these HR regulators are not constitutive but are dynamically controlled across multiple regulatory layers, including transcriptional programs, post-transcriptional RNA regulation, and post-translational proteostasis mechanisms. These regulatory systems integrate cell-cycle, stress signaling, and metabolic context to regulate HR capacity. In this review, we present an integrated framework describing how CtIP, BRCA1, and RAD51 are regulated across transcriptional, post-transcriptional, and post-translational levels, with emphasis on mechanisms that jointly control multiple HR proteins as well as gene-specific regulatory modules.

Regular aerobic exercise is associated with increased survival for patients suffering from solid tumor cancers. In the last decade, pre-clinical exercise oncology studies have begun to explore the mechanisms governing the protective effects of exercise, leading to translation of exercise-based regimens into the clinic. However, many patients with intractable solid tumors or those diagnosed at late stage may be physically unable to partake in exercise-based regimens. In this perspective piece, authors argue that the value of pre-clinical exercise oncology work is not limited to direct translation, but should instead be reframed as a means of discovery for novel anti-tumor mechanisms. Exercise-based pre-clinical work should be considered as a discovery engine, wherein mechanisms identified at the intersection of exercise physiology and tumor biology should be independently evaluated for their clinical potential, independent of the need for exercise.

Carbon fibers (CF)/epoxy composites are widely utilized in aerospace and transportation due to their light weight and high specific strength/modulus. However, poor interfacial binding between CF and the epoxy matrix leads to phonon scattering and inefficient load transfer, causing heat accumulation and reduced service life in high-power electronic systems. In this study, CF was coated with a styrene, benzocyclobutene, and methyl methacrylate units contained polymer layer mixed with carbon nanotubes (CNT) through impregnation and drying. The polymer layer was then thermally crosslinked to obtain the polymer and CNT coated CF (CF@(CNT/P)). CF@(CNT/P) was then applied as reinforced fibers and epoxy resin containing liquid crystal structure as matrix to prepare CF@(CNT/P)/epoxy composites. The π-π interactions and hydrogen bonds between CF and epoxy resin were enhanced by the benzene ring and ester groups in the polymer, thereby improving the interfacial binding between epoxy resin and CF. CF@(CNT/P)/epoxy composite showed enhanced load-bearing and thermal conduction performance. When the mass fractions of CNT and copolymer in CNT/P/dichloromethane (DCM) solution were 0.1 wt% and 0.03 wt%, respectively, the CF@(CNT/P) had the best interfacial binding to the epoxy resin. The interlaminar shear strength and flexural strength of the CF@(CNT/P)/epoxy composite increased from 23.7 and 252.5 MPa of CF/epoxy composite to 31.4 and 369.1 MPa, respectively. Meanwhile, the in-plane (λ_∥) and through-plane (λ_⊥) thermal conductivity were improved from 7.15 and 0.31 W/(m·K) of CF/epoxy composite to 10.08 and 0.58 W/(m·K), respectively. The CF@(CNT/P)/epoxy composite also demonstrated an electromagnetic interference shielding effectiveness of 38.6 dB which has broad application in high-power electronic information systems.

Conventional complementary metal-oxide-semiconductor (CMOS) image sensors with Bayer color filter arrays face limitations in low-illumination color imaging. Color router (CR) aims to achieve absorption-loss-free spectral separation, providing an efficient way to sort and guide different color lights to the corresponding pixels. However, applying CR to imaging chips requires consideration of different incidence conditions and processing difficulties. In this study, we propose and fabricate a polarization-insensitive “red, green and blue + near-infrared (NIR)” CR based on metasurface, operating within the wavelength range of 400-1,100 nm and exhibiting high light energy utilization efficiency. The structural parameters are optimized through an inverse design method. The device is fabricated by silicon-compatible processes, offering the advantage of high integration. Experimental results demonstrate that the measured energy utilization efficiency reaches over 90%, with the measured average correlation coefficient of spectral curves under different polarizations reaches 0.98. This device can efficiently route visible to NIR light, providing a novel approach for integrating metasurface into CMOS image sensors to enhance high-performance color imaging systems.

Multiple clinical and translational studies have shown that infiltration of tumour-associated neutrophils is associated with advanced disease stages, increased metastatic spread, therapeutic resistance, and poorer overall survival across various cancer types. Beyond their traditional role as first responders to tissue damage and pathogen infection, neutrophils within the tumour microenvironment (TME) display remarkable functional plasticity, adopting phenotypes that can promote angiogenesis, extracellular matrix remodelling, immune suppression, and tumour cell invasion. Importantly, it is now recognised that the prognostic impact of neutrophils is highly context-dependent, shaped by tumour type, stage, and the evolving inflammatory milieu. Intravital imaging studies have revealed dynamic neutrophil behaviours, including distinct migratory patterns between intra-tumoural and peri-tumoural regions, interactions with tumour and immune cells, and contributions to processes such as metastasis and immune suppression. However, there is a lack of understanding of how transcriptionally defined neutrophil subsets translate into specific functional and behavioural states in the TME in vivo. This mini-review spotlights intravital imaging approaches that illuminate neutrophil dynamics in tumours. We also discuss how extrinsic regulators, including cancer-associated fibroblasts and neural inputs direct neutrophil dynamics, further contributing to TME complexity.

Deep eutectic solvent (DES) based nanofluids have gained ample attention owing to their extraordinary thermophysical properties such as wide temperature range and thermal stability. While poor static stability of DES based nanofluids heavily hinders their practical application due to the incompatibility with dispersant. Herin, a novel ZnO nanofluids using ethylene glycol and potassium acetate DES for solar thermal utilization was developed. With the aim at addressing the poor stability, a one-step in situ synthesis involving microwave-induced dehydration was employed to prepare self-dispersing ZnO nanoparticles without external dispersants. Thermophysical properties and photothermal performance of nanofluids with varying mass fractions (0.5-5 wt.%) were systematically investigated. Results indicate that ZnO inclusion significantly improves thermal conductivity and photothermal conversion. Specifically, the 5 wt.% sample exhibited a 12% increase in thermal conductivity at 65℃ compared to the base fluid, while the 0.5 wt.% sample demonstrated optimal photothermal response under low light intensity. Additionally, the fluids displayed anomalously enhanced specific heat capacity (up to 14.6%), attributed to the formation of ordered interfacial liquid layers on the high-surface-area ZnO nanoparticles through electrostatic interactions and hydrogen bond rearrangement, offering dual advantages in heat transfer and storage, while maintaining dispersion stability for approximately two weeks, which thus presents a low-cost, stable, and environmentally friendly strategy for developing heat transfer fluids suitable for medium-to-high temperature solar collection systems.

Cardiovascular aging is characterized by progressive loss of regulatory capacity, reduced physiological reserve, and increased vulnerability to stress and disease. Resting heart rate (RHR) and heart rate variability (HRV) provide complementary measures of cardiac automaticity and autonomic regulation, capturing important dimensions of cardiovascular aging. Comparative and epidemiological evidence demonstrates that elevated RHR and reduced HRV predict morbidity and mortality across species and human populations, reflecting cumulative physiological stress and declining regulatory function. Biological pathways associated with these autonomic phenotypes include sympathetic overactivation, parasympathetic withdrawal, neuroendocrine dysregulation, impaired baroreflex function, chronic inflammation, oxidative stress, and mitochondrial dysfunction. Clinical and mechanistic studies indicate that behavioral interventions (e.g., exercise and dietary modulation), pharmacological therapies, and neuromodulatory approaches can favorably influence RHR and HRV, although their causal effects on aging trajectories remain uncertain. Recent advances in wearable technologies and machine-learning-based phenotyping enable continuous assessment of autonomic function in both research and real-world settings. Integrating RHR and HRV into geroscience frameworks may help link autonomic regulation with fundamental aging mechanisms and cardiovascular risk. As accessible, noninvasive measures of physiological resilience and adaptability, RHR and HRV have potential value for advancing precision approaches to healthy cardiovascular aging and longevity.

YWHAG is a subtype of the 14-3-3 protein family that regulates the transduction of multiple signaling pathways in cells, such as the PI3K-AKT and MAPK pathways, by recognizing and binding to specific phosphorylated target protein motifs. These signaling pathways are widely found to be abnormally activated or shut down in human diseases, such as cancer and neurodegenerative diseases. Different evidence has shown that the expression levels and functions of YWHAG in different tumors are not the same. Meanwhile, the potential functions of YWHAG in these diseases and its role in regulating abnormal signaling pathways are still unknown. Therefore, further exploration and research are needed to determine YWHAG’s core role in regulating signaling pathways and whether YWHAG can be a therapeutic target for diseases to develop corresponding drugs. This review will revisit the structure and function of YWHAG, elaborate on the newly identified YWHAG-interacting proteins in recent years, summarize the functions of YWHAG in cancer while comparing it with the homologous family member YWHAZ, explore the functions of YWHAG in neurodegenerative diseases, and present our perspectives on drug-screening strategies targeting YWHAG.

Aims: Mental illnesses such as anxiety disorder, obsessive-compulsive disorder and intrusive thoughts affect millions worldwide and have become increasingly visible in recent decades, particularly in young adults. Despite greater public awareness and discussion, significant stigma remains, undermining the self-confidence and well-being of those affected. Anti-stigmatization interventions can help reduce prejudice by promoting education and meaningful contact between people with and without mental illness. Technology-based interventions may further support these efforts by simulating such contact through approaches such as virtual perspective-taking or storytelling. In this context, social robots, as physically embodied agents with social presence and multimodal communication capabilities, may offer additional advantages for storytelling-based interventions. Especially the integration of music and sound effects may benefit the outcomes. To this end, we evaluated a robotic storyteller as an intervention method focusing on the influence of additional sound integration.

Methods: In multimodal robotic storytelling, modalities such as voice modulation and bodily expressions have been extensively studied, while non-speech sounds, namely sound effects and background music, remain largely overlooked, despite their importance in related media such as audio books and films. To address this gap, we compared a robotic storyteller narrating a story about a person experiencing a panic attack and intrusive thoughts using only voice and bodily expression with versions integrating sound effects, background music, or both. A laboratory study examined how these modalities affected prejudice, empathy, and narrative transportation, using questionnaires and behavioral observation.

Results: The comparison yielded mixed results. While the addition of diverse combinations of non-speech sounds did not affect story recipients’ prejudice differently than a robotic storytelling without additional sounds, adding both sound effects and background music led to increased transportation into the story as well as improved associative empathy. In contrast, the addition of music only decreased associative empathy. Although mediation was not indicated, they revealed transportation as a predictor for both empathy and prejudice, while being only minimally influenced by sound integration itself.

Conclusion: The effects of music and/or sound effect integration to a robotic storytelling intervention on recipients’ prejudice were mixed, recommending either the combination of both sound types or complete omission. Furthermore, transportation was indicated as an important key lever for increasing empathy and decreasing prejudice in a robotic storytelling intervention that warrants further investigation. Thus, future work is needed to gain deeper insights into robotic storytelling as an intervention tool for reducing prejudice and stigmata, including work on increasing transportation as a modifiable factor as well as the integration of pre-post-measurements.

Silver nanowire (AgNW) networks are promising electrode candidates for flexible organic solar cells (FOSCs) due to their outstanding optoelectronic properties and mechanical flexibility. However, their practical deployment remains hindered by high junction resistance and inherent surface irregularities, which lead to non-uniform current distribution and increased energy dissipation. Here, we report a hyaluronic acid (HA)-assisted Joule-heating strategy that enables spatially uniform yet junction-selective sintering within AgNWs. The HA treatment increases the density of effective inter-nanowire contacts and improves interfacial adhesion, thereby preconditioning the network for a more homogeneous current distribution. As a result, Joule heating is preferentially localized at electrically active junctions, leading to efficient welding without damaging the overall network. This synergistic regulation produces AgNW electrodes with reduced sheet resistance, improved surface smoothness, and enhanced mechanical robustness, while preserving high optical transparency. Based on the transition from localized current crowding to a homogenized transport regime, which contributes to reduced resistive losses and suppressed recombination, the FOSCs achieve a power conversion efficiency increased from 16.85% to 18.09%, which is the highest reported value of inverted FOSCs. This work establishes a general strategy for coupling network densification with electrically driven selective sintering, offering a scalable route toward high-performance transparent electrodes for next-generation flexible optoelectronics.

Aims: Conventional two-dimensional (2D) culture systems fail to recapitulate the structural and functional complexity of the brain parenchyma, driving microglia toward an activated, non-homeostatic state. Here, we therefore developed a simple, robust, and scalable three-dimensional (3D) culture system using a Matrigel-collagen composite matrix to promote a more in vivo-like microglial phenotype.

Methods: Primary mouse microglia and human induced pluripotent stem cell-derived microglia (iMGLs) from multiple donor lines were cultured either in conventional 2D conditions or embedded in a Matrigel-collagen 3D matrix. Phenotypes were characterized using confocal microscopy and morphometric analysis, live-cell imaging, transcriptomic profiling, electrophysiology, cytokine secretion assays upon inflammatory stimulation, and flow cytometry.

Results: In 3D culture, mouse microglia developed a markedly more ramified and structurally complex morphology, supported by filamentous actin-rich terminal processes. Live-cell imaging demonstrated enhanced dynamics of cell processes and a more sessile cell body, consistent with physiological microglia surveillance behavior. Transcriptomic profiling further revealed that microglia cultured in 3D downregulated genes associated with activation and proliferation while upregulating homeostatic markers, yet retained a robust cytokine response to inflammatory stimulation. Electrophysiological profiling similarly indicated a shift toward a more resting, in vivo-like state. Human iMGLs showed comparable morphological adaptations in 3D, with protein expression and functional readouts reflecting both shared homeostatic features and cell line- and context-dependent characteristics.

Conclusion: Together, our results demonstrate that a simple and accessible 3D culture system shapes microglial morphology, behavior, and molecular identity, establishing a versatile platform for investigating microglial physiology in a controlled yet physiologically more relevant environment.

Macrophage efferocytosis is the phagocytic clearance of apoptotic cells. This fundamental process governs tissue homeostasis, immune tolerance, and the resolution of inflammation. Immune and metabolic reprogramming are essential to sustain efferocytic capacity. Maladaptive efferocytosis is mostly considered “incomplete or nonfunctional” in chronic diseases, but can also be characterized by misinterpreted, mistimed, or mischaracterized efferocytic events. A central barrier to progress has been the inability to define efferocytic macrophages as functional entities, rather than transient, poorly resolved states obscured by technical and conceptual limitations. Here, we review efferocytosis across health and disease by linking apoptotic cargo identity, macrophage processing capacity, and inflammatory and metabolic constraints to efferocytic throughput. We further highlight the technical challenges that cause bona fide efferocytic states to be misclassified or excluded in high‑dimensional datasets. Applying an updated framework acknowledging complexity of macrophage efferocytosis will ultimately enable the development of novel therapeutic strategies.

Ferroptosis, an iron-dependent form of regulated cell death characterised by lipid peroxidation, has emerged as a critical pathway in cancer, neurodegeneration, and ischaemia-reperfusion injury. While reverse genetic approaches have dominated ferroptosis research, forward genetic strategies offer unique advantages for discovering novel regulatory mechanisms without prior knowledge of pathway components. This perspective explores how classical forward genetic methodologies, including chemical mutagenesis screens, genetic modifier studies, and quantitative trait locus mapping, can be adapted to systematically identify ferroptosis regulators. We focus on employing the nematode model, Caenorhabditis elegans, and discuss the inherent advantages and disadvantages of this system. Technical considerations for designing phenotype-based screens are discussed, highlighting successful examples from related cell death pathways. Experimental frameworks for leveraging alternate model organisms to uncover conserved ferroptosis mechanisms are also explored. Forward genetics promises to reveal unexpected connections between ferroptosis and cellular processes, potentially identifying new therapeutic targets and biomarkers for ferroptosis-related diseases.

Extracellular vesicles (EVs) are membrane-enclosed particles released by cells carrying proteins, lipids, metabolites and nucleic acids that can alter the behavior of recipient cells. In mammalian systems, EVs have been studied extensively as important mediators of intercellular and interorgan communication in development, tissue homeostasis, immunity, regeneration, metabolism, cancer and neurobiology. In parallel, Drosophila has emerged as a powerful in vivo model for EV research owing to its genetic tractability and the availability of well-established tools for studying interorgan communication. Work in Drosophila has shown that EVs participate in synaptic cargo transfer, selected developmental signaling contexts and neuronal homeostasis. Importantly, most of the pathways that regulate endosomal sorting, multivesicular body dynamics, membrane budding and vesicle secretion are conserved between flies and mammals. This review summarizes current understanding of EV nomenclature, biogenesis, cargo selection and biological function, with emphasis on points of convergence and divergence between mammalian and Drosophila systems. It further discusses the strengths and limitations of Drosophila as a model for mammalian EV biology and highlights how comparative approaches can sharpen mechanistic insight and translational EV studies.

Aims: Circular RNAs (circRNAs) have been shown to be closely associated with the occurrence and progression of various diseases. However, most existing circRNA-disease association prediction methods are limited to homogeneous networks and are unable to effectively capture deep semantic associations through high-order meta-paths. This study aims to develop an efficient computational method for accurately predicting potential circRNA-disease associations.

Methods: We propose a meta-relation-aware heterogeneous graph learning framework for circRNA-disease association prediction. Specifically, known circRNA-disease associations are first used to compute Gaussian interaction profile kernel similarity and extract node attribute features, based on which a heterogeneous graph network is constructed. A graph neural network is then employed to perform multi-layer message passing on the heterogeneous graph, aggregating neighborhood information to achieve deep fusion of multi-source features and generate node embeddings that encode both local and global structural information. Finally, the learned embeddings are fed into a gradient boosting decision tree classifier, and an ensemble strategy is adopted to improve prediction accuracy. Five-fold cross-validation is used for performance evaluation.

Results: Experimental results on three benchmark datasets, CircR2Disease V2.0, circAtlas 3.0, and circRNADisease V2.0, show that the proposed model achieves area under the receiver operating characteristic curve (AUC) values of 92.17%, 91.83%, and 91.73%, respectively. The model outperforms traditional methods in terms of accuracy, precision, and recall. Furthermore, ablation studies validate the effectiveness of the meta-relation-aware strategy.

Conclusions: Overall, this work provides an efficient and reliable computational framework for molecular association prediction and biomarker discovery in the biomedical domain.

Aims: Brown adipose tissue (BAT) relies heavily on mitochondrial activity and reactive oxygen species homeostasis to regulate thermogenesis and metabolic balance. However, the specific role of glutathione peroxidase 4 (GPX4), a critical antioxidant enzyme and central regulator of ferroptosis, in BAT remains unclear. This study aims to investigate the necessity of GPX4 for the functional integrity and thermogenic capacity of BAT.

Methods: Initially, we employed pharmacological inhibition of GPX4 in vitro using differentiated brown adipocytes. To investigate its role in vivo, we generated a BAT-specific Gpx4 knockout mouse model. The physiological and metabolic impacts of GPX4 deficiency were evaluated across three different conditions: cold exposure, high-fat diet, and vitamin E-deficient diet. Comprehensive evaluations were conducted using metabolic, histological, ultrastructural, and transcriptomic (RNA-seq) analyses.

Results: In vitro, pharmacological inhibition of GPX4 induced ferroptosis in differentiated brown adipocytes, suggesting its potential regulatory role. Strikingly, in vivo histological, ultrastructural, and metabolic analyses indicated that the genetic deletion of GPX4 does not impair BAT morphology or thermogenic function under any of the tested conditions. Consistent with these physiological findings, RNA-seq revealed that GPX4 deficiency did not significantly alter the expression of genes associated with ferroptosis or thermogenic pathways.

Conclusion: Although pharmacological inhibition of GPX4 triggers ferroptosis in brown adipocytes in vitro, GPX4 is not essential for maintaining the morphological integrity and thermogenic capacity of BAT in vivo under the specific experimental conditions tested.

Skin photoaging is a progressive, ultraviolet (UV)-driven form of extrinsic skin aging that compromises epidermal barrier integrity, dermal extracellular matrix organization, pigmentary homeostasis, and subcutaneous tissue support. Current findings indicate that photoaging is driven by interconnected molecular mechanisms, including ultraviolet A-(UVA)- and ultraviolet B (UVB)-induced reactive oxygen species (ROS) generation, DNA photolesions, mitogen-activated protein kinase/activator protein-1 (MAPK/AP-1-) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-mediated inflammatory signaling, matrix metalloproteinase activation, collagen and elastin degradation, mitochondrial dysfunction, autophagy impairment, and senescence-associated secretory phenotypes (SASP). Plant-derived polyphenols, carotenoids, and terpenoids primarily attenuate oxidative stress and inflammatory signaling; marine-derived mycosporine-like amino acids, sulfated polysaccharides, xanthophylls, and collagen peptides provide UV absorption, matrix protection, and structural support; and microbiome-derived metabolites and probiotics modulate redox balance, immune signaling, and barrier homeostasis via the gut-skin axis. By integrating layer-specific pathogenesis with multi-source natural interventions, this review highlights translationally relevant strategies for developing safer, mechanism-guided anti-photoaging therapies, informing both preclinical research and clinical applications.

Ferroptosis, a distinct form of cell death driven by lipid peroxidation, holds considerable potential as a therapeutic strategy for cancer. Its unique mechanisms, centered on the disruption of cellular systems that protect against phospholipid peroxidation, distinguish ferroptosis from apoptosis and other well-characterized forms of cell death. This creates a novel therapeutic opportunity; however, it also presents challenges, as non-cancerous cells likewise depend to some extent on ferroptosis-regulating pathways. Consequently, extensive research efforts have focused on identifying suitable molecular targets, developing targeted drug delivery strategies, defining cancer types that are particularly dependent on ferroptosis-regulatory components, and establishing effective patient stratification approaches. Furthermore, exploring combination therapies may further enhance therapeutic efficacy through additive or synergistic effects. This review highlights the potential synergistic effects of combining ferroptosis induction with conventional cancer therapies, including chemotherapy, immunotherapy, and radiation therapy. Preclinical studies indicate that promoting ferroptosis may help overcome drug resistance, a major barrier that often limits the efficacy of existing treatments. Nevertheless, the successful development of ferroptosis-based therapies will require overcoming several challenges through innovative therapeutic strategies.

Friedreich’s ataxia (FRDA) is a rare neurodegenerative condition driven by a severe deficiency of the mitochondrial protein frataxin (FXN). This depletion impairs mitochondrial iron-sulfur cluster biogenesis and disrupts intracellular iron homeostasis, ultimately promoting oxidative stress. Driven by localized iron overload and the continuous generation of reactive oxygen species, the resulting metabolic dysfunction renders vulnerable tissues highly susceptible to ferroptosis. This iron-dependent form of regulated cell death, executed through excessive lipid peroxidation, is now widely acknowledged as an important contributor to the neurodegeneration and hypertrophic cardiomyopathy that characterize FRDA. In the present review, we explore how FXN loss undermines cellular defenses against oxidative damage, placing a specific focus on the regulation of the lipid redox landscape. We detail the breakdown of glutathione (GSH)-dependent mechanisms, specifically highlighting the blunted Nrf2 antioxidant response and the subsequent reduced capacity of glutathione peroxidase 4. Alongside these deficits, we investigate the compensatory roles of GSH-independent rescue networks, namely ferroptosis suppressor protein 1 and mitochondrial dihydroorotate dehydrogenase. Looking toward clinical translation, we critically assess emerging pharmacological interventions designed to target these ferroptotic nodes. The potential of mitochondria-targeted iron chelators, lipoxygenase inhibitors, lipophilic radical-trapping antioxidants, and novel Nrf2 activators is evaluated to determine whether inhibiting ferroptosis can serve as a viable disease-modifying strategy. Moving forward, combinatorial “protect and restore” approaches will likely prove essential for maximizing therapeutic efficacy in FRDA.

Metasurfaces, as a key platform for flat optics, enable advanced manipulation of light by harnessing rich local and nonlocal resonant modes at subwavelength-structured interfaces. Distinct from conventional local metasurfaces that primarily modulate optical wavefronts over broad spectral ranges, nonlocal metasurfaces based on collective resonances provide enhanced spectral and momentum selectivity together with strengthened light–matter interaction. This review surveys recent advances in optical-field information manipulation enabled by nonlocal metasurfaces, where “information manipulation” denotes the high-dimensional control of amplitude, phase, and polarization across spectral, temporal, spatial, and momentum domains, with a particular focus on integrating local and nonlocal resonances to enhance device performance and functionality. We first introduce the fundamental characteristics of nonlocal resonances and summarize representative progress in real- and momentum-space light control within passive nonlocal metasurfaces. We also discuss recent advances in active nonlocal metasurfaces, including applications in nonlinear harmonic generation, quantum-state control, and spatial information lasers. Finally, we highlight emerging trends in on-chip and tunable nonlocal metasurfaces and outline key challenges and future research directions for this rapidly evolving field.

Cellular senescence is a cell fate triggered by diverse endogenous and exogenous stresses, including DNA damage, telomere dysfunction, and metabolic dysregulation. It is characterized by irreversible cell cycle arrest and a hypersecretory state known as the senescence-associated secretory phenotype (SASP). As a hallmark of ageing, senescence contributes to various physiological processes and is closely associated with the pathogenesis of age-related diseases. Specifically, senescent cells secrete SASP factors, which promote chronic inflammation, and propagate senescence to neighboring normal cells. DNA damage, a crucial inducer of cellular senescence and inflammation, often leads to the accumulation of cytosolic DNA. The cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS-STING) pathway has emerged as a critical mechanism for detecting such intracellular DNA, and its excessive activation is strongly associated with senescence and age-related chronic inflammation (inflammaging). Accumulating evidence indicates that multiple factors can trigger cGAS-STING signaling, thereby stimulating inflammatory responses and accelerating cellular senescence. In this review, we summarize recent advances in understanding how cGAS-STING signaling orchestrates cellular senescence and inflammaging. We outline the key hallmarks and triggers of cellular senescence, with particular emphasis on the role of cGAS in age-related inflammatory diseases. Finally, we discuss that targeting the cGAS-STING pathway may pave new ways for therapeutic strategies to mitigate cellular senescence-associated diseases.

Aims: Alternative splicing serves as a primary mechanism for diversifying the proteome, making the prediction of distinct isoform functions critical for understanding complex disease mechanisms. However, determining the specific functional roles of isoforms remains hindered by high sequence homology among variants and the sparsity of isoform-level annotations.

Methods: In this study, we propose SpliceEM, a deep learning framework for isoform function prediction at single-cell resolution. SpliceEM utilizes a splicing event-aware encoder with cross-modal attention to separate functional signals from global protein sequences. A Heterogeneous Graph Transformer captures the dependencies among isoforms, genes, and Gene Ontology terms. To bridge the annotation gap, we incorporate a self-distillation framework guided by an Exponential Moving Average teacher model and Multi-Instance Learning, optimized by an Asymmetric Loss and hierarchical constraints.

Results: Benchmarking on human datasets demonstrates that SpliceEM outperforms existing methods in isoform function prediction, particularly in identifying rare functional terms under data-sparse conditions. Furthermore, splicing-function analysis reveals that specific splicing events, such as skipped exons and alternative first exons, act as prominent drivers in oncogenic signaling cascades and context-specific functional switching.

Conclusion: SpliceEM provides a computational foundation for exploring transcriptomic functional diversity. By shifting the focus from global sequences to localized splicing events and utilizing hierarchical biological priors, it offers high-resolution insights into cell-type-specific molecular mechanisms and potential therapeutic targets.

Aims: Single-cell RNA sequencing (scRNA-seq) has emerged as a cornerstone technology in tumor microenvironment research. Accurate cell-type annotation is fundamental to downstream scRNA-seq analysis. However, automated tools are often highly sensitive to dataset noise and show limited adaptability in cross-patient scenarios. To address these challenges, we propose scAdaptAnno, a graph-based target domain adaptation framework for cross-patient single-cell annotation.

Methods: In the graph construction phase, scAdaptAnno integrates both gene expression similarity and biological prior knowledge to build a more biologically meaningful cell graph. By leveraging cell representations enriched with biological priors to mitigate noise in gene expression data and by implementing a bidirectional adaptation mechanism, the model achieves source-free target domain alignment.

Results: We performed comprehensive benchmarking against nine leading methods across multiple datasets spanning various cancer types. The results demonstrate that scAdaptAnno achieves state-of-the-art performance.

Conclusion: scAdaptAnno is a robust and accurate single-cell annotation tool that excels in cross-patient cell-type annotation transfer. By integrating biologically informed graph construction and bidirectional source-free domain adaptation, it delivers reliable, noise-resistant performance across diverse tumor microenvironments, providing an effective solution for automated cell-type annotation in multi-patient scRNA-seq studies.

Alkali-activated lunar regolith is considered one of the most promising lunar regolith-based construction materials for large-scale lunar construction. This study presents a comprehensive framework integrating machine learning (ML) algorithms to investigate the influence of various features on the mechanical strength of alkali-activated lunar regolith simulant (AALRS), aiming to achieve the strength prediction and optimization design of AALRS. The properties of lunar regolith simulant, mixture proportions, preparation parameters, environmental conditions, and enhancement methods were employed as input features for ML modeling. The compressive and flexural strength predictive models were constructed using eight ML algorithms and evaluated through statistical indicators. Among the models, Extreme Gradient Boosting demonstrated the best performance, yielding an R² of 0.8684, a root mean square error of 6.2007 MPa, and a mean absolute error of 4.0874 MPa on the testing dataset. Using the best prediction models, four design strategies with three objectives, namely, compressive strength, flexural strength, and transport payload (TP), were optimized and evaluated using the non-dominated sorting genetic algorithm II and the technique for order preference by similarity to ideal solution methods. The proposed prediction and optimization framework for the mechanical performance of AALRS, which integrates extreme environmental effects and TP-driven design, provides a robust data-driven approach for the design, prediction, and optimization of AALRS, advancing the development of extraterrestrial construction materials and technologies.

Aims: Single-cell RNA-sequencing (RNA-seq) enables high-resolution gene regulatory network (GRN) analysis in specific cell types, but data sparsity, noise, and complex regulatory relationships remain major challenges. Existing methods often focus on pairwise gene associations and insufficiently capture global network topology. This study aims to develop a deep graph framework for single-cell GRN inference by integrating global regulatory structure with biologically informed distributional regularization.

Methods: We propose ZINB-GRAN, a graph adversarial framework for single-cell GRN inference. It constructs a weighted gene co-expression matrix as a prior regulatory graph and reformulates GRN inference as a link prediction task. A graph convolutional encoder learns latent gene representations, while a decoder reconstructs network topology. To enhance biological consistency, a multilayer perceptron-based discriminator aligns encoder-derived representations with a continuous zero-inflated negative binomial (ZINB)-derived prior generated through ZINB sampling, logarithmic transformation, normalization, and Gaussian perturbation.

Results: ZINB-GRAN jointly optimizes network reconstruction and latent distribution alignment using mask-based supervised classification and adversarial losses. This strategy improves regulatory structure discrimination and robustness in sparse single-cell data. Benchmarking on simulated and real datasets shows that ZINB-GRAN outperforms most existing GRN inference methods and identifies cell type-specific GRNs and key regulatory factors in human peripheral blood mononuclear cells (PBMCs) and triple-negative breast cancer.

Conclusion: ZINB-GRAN integrates global network topology, graph convolutional representation learning, and continuous ZINB-derived prior regularization for single-cell GRN inference. By aligning latent representations with a biologically motivated prior, it improves the robustness and interpretability of GRN reconstruction and provides a useful tool for cell-specific network inference, key regulator identification, and biomarker discovery.

Lipoprotein(a) (Lp(a)) exhibits proinflammatory and proatherogenic properties. Evidence from prospective epidemiological studies, as well as Mendelian randomization studies, reveals an independent and causal association between elevated Lp(a) concentrations and atherosclerotic cardiovascular disease (ASCVD). Lipoprotein apheresis (LA) effectively lowers atherogenic lipoproteins when medication is insufficient. Since 2008, LA reimbursement for patients with high Lp(a) (> 60 mg/dL) and progressive ASCVD has been approved in Germany. To justify this policy, German authorities required prospective data, leading to the conduct of the Pro(a)LiFe study and establishment of the German Lipoprotein Apheresis Registry (GLAR). The Pro(a)LiFe study enrolled 170 patients with high Lp(a) and progressive ASCVD to evaluate LA’s long-term effect on cardiovascular event rates. Patients were investigated for 5 years before initiation of regular LA, then up to 12 years afterwards. Results showed a significant decline in mean annual cardiovascular events per patient from 0.27 (±0.25) in the 5 years before LA to 0.06 (±0.08) over the following 12 years (p < 0.001). Compared to a matched UK Biobank cohort, ASCVD event rates were higher before LA began and significantly lower afterwards. The results confirm that long-term treatment with LA is associated with low incidence of cardiovascular events in patients with high Lp(a) sustained over 12 years. Combining Lp(a) testing with LA has meaningfully reduced ASCVD events. Until Lp(a)-specific drugs receive regulatory approval, LA remains a viable treatment for selected high-risk patients. It will be important to assess whether results with novel pharmaceutical agents apply to the peculiar high-risk patients with high Lp(a) and progressive ASCVD.

Lanthanide-doped nanoparticles (LNPs) offer an emerging non-invasive theranostic platform for monitoring and treating neurodegenerative diseases (NDs) own to their high optical stability, deep tissue penetration, and excellent biocompatibility. Their key advantage lies in the ability to produce upconversion or downshifting luminescence under near-infrared excitation, enabling high-resolution deep-tissue imaging and in situ monitoring, particularly suitable for tracking pathological progression and studying molecular interactions in disorders such as Alzheimer’s disease and Parkinson’s disease. By integrating functional components such as organic dyes, noble metal nanoparticles, or therapeutic agents, LNPs can be engineered into multifunctional theranostic nanoplatforms capable of simultaneous diagnosis and targeted therapy. Moreover, their precisely tunable emission properties open new avenues for deep-brain imaging and optical modulation. This review systematically summarizes the luminescence mechanisms of LNPs and recent advances in their applications for biosensing and diagnosis in NDs. It covers the detection of key biomarkers, including metal ions, nucleic acids, proteins, and reactive oxygen species. The discussion further extends to the therapeutic strategies targeting intracellular and microenvironmental factors, as well as synergistic approaches, with a particular emphasis on the role of LNPs in targeted drug delivery and combined theranostics. Finally, the review discusses future prospects for leveraging this platform to improve clinical outcomes in NDs.