BRCA1 and BRCA2 deficiencies are classically defined by impaired homologous recombination–mediated DNA repair; however, their pathological consequences extend far beyond cell-autonomous genomic instability. Accumulating evidence indicates that BRCA deficiency is accompanied by iron dysregulation and persistent lipid peroxidation, placing cells under chronic ferroptotic pressure. Studies using BRCA1/2 rat models demonstrate that ferroptosis functions as a decisive biological checkpoint with gene-specific outcomes. Under BRCA1 haploinsufficiency, iron-driven oxidative stress accelerates carcinogenesis by selecting for ferroptosis-resistant clones, whereas BRCA2 haploinsufficiency enhances ferroptotic execution, thereby preventing iron-induced cancer promotion. In contrast, reproductive tissues lacking adaptive escape capacity manifest BRCA deficiency as a direct ferroptosis-driven cellular loss, resulting in male and female infertility. Importantly, ferroptosis is not a silent, cell-confined event. Experimental evidence from asbestos-induced carcinogenesis demonstrates that macrophages undergoing ferroptosis after asbestos phagocytosis release CD63-positive, ferritin-containing extracellular vesicles (EVs) that induce oxidative stress in recipient mesothelial cells, establishing EVs as active mediators of ferroptotic stress propagation. We propose that BRCA deficiency generates a state of ferroptotic priming in which oxidized lipids, iron-related factors, and nucleic acids are disseminated via EVs, thereby shaping tissue- and organ-level pathology. From an evolutionary perspective, the persistence of pathogenic BRCA variants may reflect adaptive advantages conferred by haploinsufficiency in iron-limited, short-lived ancestral environments; under modern conditions of iron abundance and extended lifespan, this once-adaptive state becomes maladaptive, predisposing carriers to cancer and degenerative disorders beyond the cell.

Aims: Medical image translation is widely used for data augmentation and cross-domain adaptation in clinical image analysis. However, the nature of medical imaging makes it challenging to collect high-quality samples for the training of translation models. Because of the limited access to and costly expense of medical images, distribution bias is commonly observed between the source and target samples, and this finally leads the models to mismatch the target domain.

Methods: To promote medical image translation, a bias correction method, named content adaptation, has been proposed in this study to align the training samples in the data space. Based on the invariant medical topological structure, paired samples are constructed from weakly paired and unpaired data using content adaptation to correct the distribution bias and promote the image-to-image translation.

Results: Experiments on retinal fundus image translation and COVID-19 CT synthesis demonstrate that the proposed method effectively suppresses structural hallucination and improves both visual quality and quantitative performance. Consistent gains are observed across multiple backbone models under different supervision settings. The results suggest that explicit anatomical alignment provides an effective and model-agnostic way to mitigate distribution bias in medical image translation. By bridging weakly paired data with paired translation paradigms, the proposed approach enhances structural fidelity without requiring strong supervision.

Conclusion: This work presents a topology-guided content adaptation strategy that improves robustness and reduces hallucination in medical image translation. The proposed framework is general and can be readily integrated into existing translation models, offering a practical solution for data-scarce medical imaging scenarios.

Oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and cellular antioxidant defenses, is a critical driver of various pathological states. The transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) serves as the master regulator of redox homeostasis, counteracting oxidative stress by orchestrating the expression of genes involved in glutathione biosynthesis, iron metabolism, and lipid peroxidation detoxification. Recently, the specific induction of lipid peroxidation has been identified as the hallmark of ferroptosis, a form of regulated cell death that represents a promising vulnerability for cancer therapy. However, cancer cells frequently hijack the NRF2 pathway to suppress ferroptosis, thereby promoting tumor survival, metastasis, and therapy resistance. Here, we comprehensively summarize the multi-layered regulatory mechanisms of NRF2, spanning transcriptional, post-transcriptional, and post-translational modifications, within the specific context of the ferroptosis-cancer axis. We further discuss how aberrant NRF2 signaling confers ferroptosis resistance in malignancy and highlight emerging therapeutic strategies that target the NRF2 pathway to reignite ferroptotic cell death in tumors. A deeper understanding of these regulatory networks is essential for the development of precision ferroptosis-based cancer therapies.

The development of surface-enhanced Raman scattering (SERS) technology is critically reliant on the effective modulation of the electromagnetic mechanism and chemical mechanism. In this paper, we conduct a systematic review of the recent advancements in the modulation of SERS by multi-physical fields (light field, magnetic field, and electric field) and their cooperative modulation. Light field modulation enhances the intensity and area of hotspots by optimizing the matching between the local surface plasmon resonance of nanostructures and the parameters of incident light (wavelength, polarization, and pulse). Moreover, the photo-induced plasmonic thermal effect dynamically regulates the phase transition between the nanogap and the material, achieving the synergistic enhancement of SERS. Magnetic field modulation capitalizes on the magnetic induction of magnetic materials and the magnetic resonance behavior of non-magnetic structures. It enables an external magnetic field to control the aggregation and spatial organization of nanoparticles, thereby generating high-density hotspots and enhancing the detection sensitivity and selectivity. Electric field modulation can adjust the band structure, carrier density, and molecular orientation of the substrate through an external electric field or the spontaneous electric field of functional materials (such as piezoelectric, triboelectric, thermoelectric, and pyroelectric materials), thus enhancing the charge transfer efficiency and the local electromagnetic field strength. The multi-field cooperative modulation strategy overcomes the static limitations of traditional SERS substrates and further provides a crucial theoretical and technical approach for realizing a high-performance, intelligent, and reconfigurable SERS sensing platform.

This perspective reframes human–robot interaction (HRI) through extended reality (XR), arguing that virtual robots powered by large foundation models (FMs) can serve as cognitively grounded, empathic agents. Unlike physical robots, XR-native agents are unbound by hardware constraints and can be instantiated, adapted, and scaled on demand, while still affording embodiment and co-presence. We synthesize work across XR, HRI, and cognitive AI to show how such agents can support safety-critical scenarios, socially and cognitively empathic interaction across domains, and extend physical capabilities with XR and AI integration. We then discuss how multimodal large FMs (e.g., large language models, large vision models, and vision-language models) enable context-aware reasoning, affect-sensitive situations, and long-term adaptation, positioning virtual robots as cognitive and empathic mediators rather than mere simulation assets. At the same time, we highlight challenges and potential risks, including overtrust, cultural and representational bias, privacy concerns around biometric sensing, and data governance and transparency. The paper concludes by outlining a research agenda for human-centered, ethically grounded XR agents, emphasizing multi-layered evaluation frameworks, multi-user ecosystems, mixed virtual–physical embodiment, and societal and ethical design practices to envision XR-based virtual agents powered by FMs as reshaping future HRI into a more efficient and adaptive paradigm.

Major depressive disorder (MDD) is a risk factor for many aging-related medical conditions as well as cognitive decline and mortality. These and other types of observations indicate a premature aging phenotype associated with the conditions mentioned above. Recent studies have started to elucidate the mechanisms underlying the link between MDD and premature aging, pointing towards novel treatments of this phenotype. In this review, we first present evidence linking MDD to a premature aging phenotype and its association with abnormalities in multiple hallmarks of biological aging. Next, we discuss implications for treatment in MDD, including the potential geroprotective effects of antidepressant treatment as well as the conceptualization of biological aging processes as targets for novel gerotherapeutic interventions. Finally, we highlight the importance of integrating mental health assessment into both research and clinical settings to fulfill the promises of the new medical discipline of Geromedicine in preventing age-related decline and extending healthspan in the aging population.

Modification with UFM1 (UFMylation) has recently emerged as a versatile signaling system regulating diverse cellular processes, from endoplasmic reticulum homeostasis to genome stability. Recent studies have expanded this landscape by revealing a direct role of UFMylation in the core machinery of DNA replication. Specifically, UFMylation of MCM5 is required for optimal activation of the CDC45–MCM–GINS (CMG) helicase, the molecular engine that governs origin firing and drives replication fork progression. This discovery introduces a previously unrecognized regulatory layer into the DNA replication program and positions UFMylation as an important coordinator of genome duplication, whose disruption provides a mechanistic explanation for developmental disorders such as microcephalic primordial dwarfism (MPD), while more subtle or progressive dysregulation may have important implications for genome stability across ageing and cancer.

Radiative thermal management (RTM) smart windows represent an emerging class of adaptive building-envelope technologies that combine dynamic spectral regulation with passive heat dissipation through the atmospheric window. By simultaneously modulating visible light (VIS), near-infrared solar radiation (NIR), and mid-infrared thermal emission (MIR), these systems enable year-round thermal regulation with reduced building energy consumption. This review systematically summarizes recent progress and mechanisms of RTM smart windows. Compared with existing reviews that mainly focus on static radiative cooling materials or single-mode smart windows, this review emphasizes integrated RTM smart windows featuring tri-band (VIS/NIR/MIR) spectral regulation and dual-responsive mechanisms. Firstly, the fundamental principles of radiative thermal management and intelligent response mechanisms are introduced, followed by an overview of key performance. Secondly, the latest progress in electrochromic, thermochromic, and photochromic RTM smart windows is comprehensively reviewed. Particular attention is devoted to dual-responsive mechanism RTM smart windows, which integrate passive and active control to achieve synergistic performance. In summary, this review presents an overview of recent advances and underlying mechanisms in intelligent windows for radiative heat management.

The application of sustainable manufactured sand (MS) in 3D printed concrete is currently severely restricted by the “pumpability-buildability” conflict, primarily characterized by the high internal friction and angularity of MS particles. To overcome this bottleneck, this study proposes a novel process-oriented strategy: a secondary mixing protocol featuring a static resting period and delayed superplasticizer addition. This approach is explicitly designed to induce a “rheological rejuvenation” effect in stiff MS mixtures. Quantitative results demonstrate that this protocol reduces the static yield stress of high-volume MS ink (T-3) by approximately 39% (from 1,516 Pa to 924 Pa) and significantly diminishes thixotropic hysteresis, thereby successfully reopening the effective printing window to approximately 65 min. Although increasing MS content accelerates the structural build-up rate, the optimized 3D printed manufactured sand concrete (T-3) achieves a 28-day compressive strength of 61 MPa, representing a 15% enhancement over the river sand reference group (T-0). These findings confirm that the secondary mixing protocol is a robust solution capable of unlocking the potential of high-volume MS in sustainable digital construction.

Sulfur, like oxygen, belongs to group 16 of the periodic table and exhibits remarkable versatility in both electron donation and acceptance, as well as in its wide range of oxidation states. These properties enable sulfur to participate in diverse redox reactions, while also serving as a critical component of enzyme active sites and redox sensors. Moreover, sulfur is the only element capable of forming stable linear homonuclear chains, a property known as catenation, which gives rise to a rich array of naturally occurring allotropes with complex chemical architectures. Recent advances in analytical technologies have uncovered the in vivo existence of supersulfides, molecules containing catenated sulfur atoms, whose physiological functions and pathological relevance are now beginning to be elucidated. In this review, we highlight the unique chemical features and biological functions of supersulfur species, with a particular focus on their roles in cancer. Furthermore, we discuss the therapeutic implications of supersulfur-driven “reductive stress,” a distinct imbalance in redox homeostasis that may be exploited for cancer treatment.

Twisted photonic lattices have recently emerged as a promising platform for opto-twistronics, enabling the exploration of moiré-induced photonic phenomena. Despite significant progress, the implications of non-Hermitian effects within these systems remain largely unexplored. In this work, we theoretically propose and experimentally demonstrate a non-Hermitian twisted photonic lattice with dynamically tunable gain-loss modulation, realized in a four-level atomic medium through the twisted superposition of two stripe fields. By adjusting the frequency detuning, a local flat band is introduced into the photonic band structure, leading to the directional localization of light in momentum space. The degree of localization can be further controlled by varying the laser power, while the direction of localization is reconfigured in real time by tuning the twist angle. Our work uncovers an intriguing interplay between non-Hermitian band reconstruction and geometric twisting by means of reconfigurable photonic lattice, and it provides a versatile platform for studying light manipulation in twisted configurations.

Hazard-adjacent sites such as seawalls, bridge edges, quaysides, and fenced industrial perimeters routinely require metric positioning without in-zone fiducials, survey control, or repeated traversal, yet still demand decision-ready uncertainty on a site-meaningful datum. We present a datum-constrained forward intersection (DFI) pipeline that completes metric anchoring in a reachable safe subregion and transfers the uncertainty of scale, pose, and datum plane across the boundary to a targetless zone. In the safe-zone calibration stage, standard ChArUco imagery drives a prior-constrained bundle adjustment that jointly estimates multi-camera extrinsics and the site datum plane under weak but auditable tape-measure priors (inter-camera baseline and optical-center heights). In the target-zone measurement stage, undistorted pixel rays intersect the datum in closed form and are fused by incidence weighting; first-order propagation yields a 95% confidence region on the datum plane for each reported coordinate and distance. To tolerate consumer-grade instabilities (modest refocus/zoom and mixed resolutions), the pipeline supports BA-anchored refinement and a minimalist one-parameter in-situ metric correction (DFI–MScale) based on a few taped distances. In the test pair, DFI–MScale attains a median/p90 distance error of 62/106 mm, reducing p90 by 88.5% versus ORB, 87.6% versus LoFTR, and 78.4% versus RoMa under identical intrinsics and target points.

This review summarizes our current knowledge and understanding of the biosynthesis and assembly of lipoprotein(a) [Lp(a)] with a focus on diagnostic and therapeutic implications. Lp(a) is composed of a low-density lipoprotein (LDL)-like particle and the covalently bound apolipoprotein(a) [apo(a)]. Our understanding of the physiology and pathophysiology of the atherogenic Lp(a) has considerably increased over the past decades. The precise mechanisms regulating the biosynthesis, secretion, and assembly of Lp(a) have been extensively investigated but are, nevertheless, still incompletely understood or, at least, controversially discussed. Lp(a) plasma concentrations are mainly determined by synthesis and, to a minor extent, also by catabolism. Assembly of Lp(a) occurs in a complex 2-step procedure, starting with an intracellular non-covalent association between lysine-binding sites on apo(a) and lysine residues on apoB-100, the two major protein components of Lp(a). The final assembly of Lp(a) occurs extracellularly and/or transiently associated with the cell membrane by covalent disulfide bonding from newly synthesized apolipoprotein(a) and circulating LDL. Lp(a)-lowering therapies have been developed that specifically inhibit apo(a) biosynthesis and Lp(a) particle assembly. The inhibition of Lp(a) assembly raises interesting questions regarding the quantification of Lp(a) since it leads to the release of substantial amounts of LDL-unbound apo(a), which is co-measured by routine laboratory assays. The complex biosynthesis and assembly pathway of Lp(a) has been intensively investigated since its discovery more than 60 years ago. It is not only of basic-scientific interest but also concerns actual issues of diagnosis and therapy of elevated plasma concentrations of this enigmatic lipoprotein.

Electrocatalytic water splitting is a key technology for sustainable hydrogen production, but its efficiency is limited by the slow kinetics of the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER). Spinel oxides (AB₂O₄) have drawn significant interest due to their electrochemical stability, tunable electronic properties, and low cost. Their catalytic performance is highly influenced by the crystalline phase, which governs charge transport, active site density, and reaction energetics. However, the effects of phase transitions and structural variations on electrocatalysis remain insufficiently explored. This review systematically evaluates the performance of spinel oxides across six primary crystal phases: cubic, hexagonal, tetragonal, orthorhombic, rhombohedral, and monoclinic, discussing how these structural features affect charge transport, intermediate adsorption, and reaction energetics. The review emphasizes the structure-electronic-catalytic performance relationships and covers phase variants, including coordination environment modulation, defect regulation, metastable phase transitions, and strain engineering. It further examines how phase symmetry, oxygen coordination, orbital rearrangement, and interfacial characteristics influence OER and HER kinetics. Challenges in phase engineering, such as controlling phase transitions and stabilizing metastable phases, are also highlighted. This review provides a framework for understanding the electrocatalytic behavior of spinel oxides and offers guidance for designing high-performance catalysts for water splitting.

Aging is characterized by systemic dysregulation of intercellular communication, with extracellular vesicles (EVs) serving as key mediators that transport bioactive molecules, including long non-coding RNAs (lncRNAs), to modulate tissue homeostasis and aging processes. In this review, we summarize how EVs selectively package and deliver lncRNAs between cells, thereby transmitting senescence-related signals. These EV-associated lncRNAs regulate core aging-related signaling pathways such as p53, mammalian target of rapamycin (mTOR), and sirtuins, influencing cellular senescence, stress adaptation, and systemic aging. In age-related diseases, including neurodegenerative, cardiovascular, and metabolic disorders, as well as osteoarthritis, EV-lncRNAs play significant pathophysiological roles by mediating inflammation, metabolic dysregulation, and tissue repair. Owing to the protection provided by the vesicular membrane and the selectivity of their loading, EV-lncRNAs exhibit exceptional stability and cell-type specificity in biofluids, positioning them as candidate biomarkers for aging clocks and disease risk stratification. Furthermore, they also hold potential as vehicles for therapeutic delivery. However, progress in this field is constrained by methodological heterogeneity in EV isolation and characterization, a lack of causal in vivo validation, and an incomplete understanding of lncRNA sorting mechanisms. Emerging advances in experimental and analytical technologies are expected to overcome these bottlenecks and facilitate the translation of EV-lncRNA regulatory networks into precision diagnostics and aging-targeted therapeutic strategies.

Aims: Neural networks capable of capturing temporal dependencies in electroencephalogram (EEG) signals hold considerable potential for seizure prediction by modeling the progressive evolution of preictal EEG changes. However, redundant or less informative temporal features may obscure critical preictal patterns, limiting seizure prediction performance. To address this, we developed a neural architecture that effectively leverages informative temporal features to enhance seizure prediction capability.

Methods: We designed a bidirectional long short-term memory (BiLSTM) network enhanced with a channel attention mechanism, termed Attention-BiLSTM, which adaptively emphasizes informative temporal features while reducing information redundancy. We further analyze the model’s attention weights and feature distributions to provide interpretable insights into its decision-making process.

Results: Evaluation on the CHB-MIT scalp EEG dataset demonstrates that Attention-BiLSTM achieves significant performance improvements over the baseline BiLSTM, with an average accuracy of 94.77%, sensitivity of 94.58%, specificity of 94.97%, and an area under the curve of 98.38%. Furthermore, visualization results indicate that the proposed model progressively enhances feature discriminability and directs attention to the most relevant temporal features for seizure prediction.

Conclusion: The proposed Attention-BiLSTM achieves improved performance and interpretability, offering valuable insights to support future development of scalable and generalizable seizure prediction systems.

Bone tissue engineering (BTE) is pivotal for addressing bone defects. It integrates biomaterials, cells, and bioactive factors to mimic the natural bone microenvironment, thereby promoting bone regeneration and repair. In this system, scaffolds provide physical support and nutrient transport for cells. Three-dimensional (3D) printing revolutionizes BTE by fabricating customized scaffolds tailored to individual patients. As the cornerstone of 3D bioprinting, bioinks must meet strict biocompatibility and printability requirements to drive BTE advancement. In this review, we first explore the principles, advantages, and limitations of various bioprinting techniques. Furthermore, we also summarized recent breakthroughs and merits of three key photo-crosslinking reactions, including photoinitiated free radical crosslinking, photoclick crosslinking, and photo-conjugation crosslinking. Moreover, this review focused on the properties of natural polymers, synthetic polymers, and calcium phosphate-based inorganic materials in bioink formulation, as well as their BTE applications. Finally, a concise outlook on the future advancement of photocurable bioinks was provided.

Aims: Accurately identifying diagnostic biomarkers for Autism Spectrum Disorder (ASD) is crucial for enabling early diagnosis and timely intervention. Brain causal networks, which outline causal relationships and information transmission pathways among different brain regions, hold significant diagnostic value for ASD. Identifying ASD-relevant causal relationships between brain regions is critical for ASD diagnosis and for elucidating its pathophysiology. This study proposes a new model called the Peter and Clark Momentary Conditional Independence (PCMCI)-Support Vector Machine (SVM) model, which can identify diagnostic biomarkers for ASD.

Methods: The brain blood oxygen level-dependent signals of 116 brain regions from 167 participants, consisting of 72 participants with ASD and 95 Typical Controls (TCs), were used to derive brain causal networks by detecting inter-regional causal relationships using five causal discovery algorithms; the PCMCI algorithm, spectral dynamic causal modeling, Granger causal analysis, Transfer Entropy, and Liang-Kleeman information flow causal analysis. Then, the brain causal relationships were fed into the SVM, Random Forest (RF), and K-Nearest Neighbors (KNN) classifiers, respectively to classify individuals with ASD and TCs, and thereby obtain a model suitable for identifying diagnostic biomarkers for ASD through causal network analysis.

Results: Experimental results demonstrate that the PCMCI-SVM model achieves an accuracy of 91.29% in ASD identification and outperforms the other four causal discovery algorithms, as well as the RF and KNN classifiers. Moreover, our analysis indicates that the right thalamus and right middle temporal gyrus are potential diagnostic biomarkers for ASD. Additionally, the causal relationship between [left inferior parietal→right insula] was found to be associated with the dorsal attention network, while [right cuneus→left cuneus] was associated with the visual network, suggesting that disruptions in these causal relationships may impair the functional integrity of their respective subnetworks.

Conclusion: Our findings show that cognitive processes and brain connectivity are largely influenced by causal interactions between different brain regions. These potential diagnostic biomarkers not only offer insights into the neurofunctional mechanisms of ASD but also hold promise for improving diagnostic accuracy for ASD.

Pathological calcification is typically regarded as a pathological endpoint, representing the abnormal deposition of calcium salts in tissues. With the continuous advancement of research, scientists have explored novel therapeutic strategies involving the induced calcification of tumor cells, demonstrating its potential therapeutic capabilities. Recently, a pioneering study on the use of induced calcification to treat bacterial infections has been reported. This commentary first introduces the classification, etiology, and treatment of pathological calcification. Then, the antibacterial effects of induced calcification therapy through the delicately designed antibacterial agent against methicillin-resistant Staphylococcus aureus (MRSA) are generally demonstrated and discussed. The underlying mechanism of induced calcification for the treatment of chronic lung infections and chronic osteomyelitis caused by MRSA is revealed from the perspectives of bacterial energy metabolism and immune modulation. At the end of this commentary, challenges and future directions for induced calcification therapy are also briefly presented.

Ferroptosis, a non-apoptotic regulated cell death culminating in iron-dependent lipid peroxidation, has rapidly emerged as an additional mechanism of neuronal death after stroke. Yet, despite converging molecular features, such as the collapse of antioxidant systems and oxidation of lipids, the upstream biochemical landscapes that trigger ferroptosis differ across ischemic and hemorrhagic injury. In this review, we examine ferroptosis as an emergent property of redox network failure, where the transcriptional, metabolic, and oxidative circuits that normally sustain neuronal resilience are interrupted. Cells continuously operate within a spectrum of oxidative eustress and distress, balancing reactive species that serve as physiological signals and limiting their ability to inflict irreversible damage. Within this continuum, eustress supports adaptive redox signaling, metabolic coupling, and repair, whereas distress reflects exhaustion of reducing power and a shift toward self-propagative oxidation. Drawing on evidence from our lab and others, we explore how distinct stress inputs drive neurons and glia along this continuum toward ferroptotic collapse following each stroke subtype; much of this work originated within reductionist in vitro ferroptotic models and has since been extended to in vivo models. Understanding ferroptosis after stroke not only as a graded failure of redox homeostasis but also an evolutionarily adapted mechanism to couple neuronal injury to the reparative immune response is essential. Effective neuroprotective strategies will therefore depend on identifying and targeting the context-specific triggers and temporal windows that define each stroke subtype.

Aims: Driver mutations are crucial factors in the occurrence and development of cancer. Identifying cancer-related driver genes is of great significance for understanding the mechanisms of cancer initiation, prevention, and treatment. With the continuous accumulation of cancer data, how to effectively utilize these data for the identification of cancer driver genes has become a major challenge in the field of cancer biology.

Methods: We propose a novel computational model called iCDG-MOHGAT. This model integrates multi-omics pan-cancer data (such as mutations, DNA methylation, etc.), multi-dimensional gene networks, and disease semantic similarity networks to identify cancer driver genes. We first construct multi-dimensional gene networks using various types of gene correlation information (protein-protein interaction, gene sequence similarity, etc.) and establish disease semantic similarity networks for relevant cancers. Due to the complexity of node and edge types, we utilize a heterogeneous graph attention network to learn and extract features from the multi-dimensional gene networks and disease semantic similarity networks. We also incorporate a fusion learning module to effectively integrate features from different dimensions. Finally, we optimize the random forest classifier using the sparrow algorithm for the task of predicting cancer driver genes.

Results: Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted new driver genes have at least one supporting evidence of being cancer genes. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Conclusion: We have introduced a novel computational model named iCDG-MOHGAT, which precisely identifies cancer driver genes by integrating multi-omics pan-cancer data and intricate multidimensional gene networks, coupled with disease semantic similarity networks. Experimental results demonstrate that iCDG-MOHGAT outperforms many state-of-the-art models in terms of AUPR and AUROC. In the final prediction results, 91% of the predicted genes have supporting evidence. In the laboratory, this model can serve as an effective tool for identifying cancer driver genes.

Osteoarthritis (OA) is a common degenerative joint disease driven by synovial inflammation and immune dysregulation, especially in the knee joint. Macrophages play a key role in innate immunity and can differentiate into pro-inflammatory M1 or anti-inflammatory M2 phenotypes, which have a significant impact on the progression of OA. Electrospun nanofiber membranes, as a promising biomaterial, can regulate the polarization of macrophages towards the M2 phenotype, thereby reducing inflammation and promoting tissue repair. This article systematically reviews the immune microenvironment of OA, the mechanism of macrophage polarization, and the role of nanofiber membranes in the treatment of OA. In conclusion, the continuous development of nanofiber membrane technology will greatly change the future of OA treatment and provide more effective and efficient solutions for patients.

Artificial intelligence (AI) has become a central driver in healthy longevity medicine (HLM), offering new tools to characterize biological aging trajectories, identify preclinical physiological decline, and optimize interventions aimed at preserving function throughout the lifespan by targeting age-related processes. HLM is increasingly recognized as a specialty focusing on the multidimensional process of aging, encompassing molecular, physiological, cognitive, and behavioral components, all of which generate complex, high-dimensional datasets that exceed the analytical capacity of traditional clinical approaches. AI methodologies, including machine learning and deep learning models capable of integrating large, multimodal data streams, provide the computational infrastructure required to produce actionable insights. In the clinical practice of HLM, AI further facilitates integration of converging domains, including continuous digital phenotyping enabled by wearables and sensors, advanced biomarker modeling, predictive modeling capable of forecasting risk trajectories and personalized intervention optimization through life models and digital twins. These models support anticipatory clinical management, shifting care from reactive disease treatment toward continuous preservation of physiological resilience. Despite rapid progress, the integration of AI into routine healthy longevity care requires careful consideration of data quality, algorithmic transparency, regulatory frameworks, population diversity, and clinical interpretability. Nonetheless, AI-driven healthy longevity management is beginning to allow biological aging to be quantified, targeted, and longitudinally monitored in clinical practice.

Aims: Non-enzymatic autoxidation of polyunsaturated fatty acids (PUFAs), generating numerous toxic by-products implicated in neurodegeneration, aging, and other pathologies, is a key process in ferroptosis. Lipid peroxidation (LPO) can be inhibited by deuterated polyunsaturated fatty acids (D-PUFA), as the rate-limiting step of abstraction of bis-allylic hydrogen atoms is slowed down by replacing the bis-allylic hydrogens with deuteriums. Here, we aimed to assess the protective effect of monounsaturated fatty acids (MUFA), which do not undergo LPO, as compared to that of various D-PUFAs, in a liposomal model of LPO.

Methods: To detect LPO induced by ferrous ions in liposomes, we used the LPO fluorescent probe C11-Bodipy (581/591), in addition to measuring conjugated diene and malondialdehyde accumulation.

Results: By applying the C11-Bodipy (581/591) probe, we found that both 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) and 1-stearoyl-2-(11,11-d₂-linoleoyl)-phosphatidylcholine (D2-Lin-PC) protect non-deuterated 1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine (H-Lin-PC) liposomes from LPO. Similarly, both POPC and 1-stearoyl-2-(11,11,14,14-D4-linolenyl)-phosphatidylcholine (D4-Lnn-PC) protect 1-stearoyl-2-linolenyl-phosphatidylcholine (H-Lnn-PC), and so does 1-stearoyl-2-(6,6,9,9,12,12,15,15,18,18-d10-docosahexaenoyl)-sn-glycero-3-phosphatidylcholine (D10-DHA-PC). The conjugated diene and malondialdehyde probes also showed similar protective effects of POPC and D-PUFA on LPO in H-Lnn-PC.

Conclusion: Obviously, the presence of non-oxidizable lipids, such as POPC, similar to the deuterated lipids D2-Lin-PC, D4-Lnn-PC, and D10-DHA-PC, leads to a sharp decrease in the length of lateral propagation of chain reactions in lipid membranes, but they do not participate in LPO themselves.

Seawater-sea sand concrete has emerged as an innovative construction material due to its effective utilization of marine resources. At the same time, geopolymer concrete has shown rapid hardening capabilities, low energy consumption, and eco-friendly properties, making it highly promising for structural reinforcement. This study evaluated the effects of alkali modulus, alkali concentration, and fly ash (FA) content on the performance of geopolymer FA-slag seawater-sea sand mortar (SWSSGM). Performance characteristics such as consistency, flowability, compressive strength, and flexural strength were tested. The findings indicate that FA enhances workability, whereas slag powder has the opposite effect. Increasing alkali concentration reduces consistency but increases slump. Optimal compressive and flexural strength is achieved when the alkali modulus is 1.2, alkali content is 15%, and FA content is 30%. Generally, higher alkali concentrations promote strength development, while increased FA content raises porosity and reduces strength. However, strength gains over time are more pronounced, and the effects of alkali modulus require further investigation. Multiple linear regression analysis of the three factors affecting SWSSGM properties demonstrates that a quadratic polynomial regression model yields high coefficients of determination (R² ≥ 0.92), enabling accurate predictions of SWSSGM performance parameters. From a lifecycle (cradle-to-gate) perspective, SWSSGM outperforms traditional cement-based materials in terms of carbon emissions, energy consumption, and resource efficiency, showcasing its significant low-carbon, energy-saving, and environmentally friendly attributes. Through systematic optimization of SWSSGM’s design parameters, performance prediction modeling, and preliminary environmental assessment, this study provides guidance for its sustainable application in coastal engineering projects.

Optical vortices, characterized by their central singularities and spiral phase wavefront, have gained widespread attention in light manipulation and diverse applications. With their topological order being extended from integer to fraction, more unique properties have sprung out, such as continuous topological charges, fractional spiral-phase, expanded orbital angular momentum spectrum, and slit openings. These features have facilitated applications in areas such as optical tweezers, direction-selective edge enhancement imaging, robust rotational Doppler metrology, free-space communication, displacement sensing, and high-dimensional quantum entanglement. In recent years, advances in metasurface-based optical control, spatiotemporal pulse shaping, holographic imaging, and deep learning have spurred rapid innovation in the modeling, generation, and measurement of fractional vortex beams. This review begins with the fundamental theory of fractional vortex beams and surveys the latest developments in this rapidly evolving field. The scope of research has also expanded beyond optical vortices to include acoustic vortices. The growing interest in fractional vortex beams, coupled with ongoing technological innovations, is expected to pave the way for further advancements in this promising area of research.

The increasing digitalization of the construction industry is reshaping how professionals are trained and educated. As a key component of construction education, traditional site visits provide students with valuable first-hand exposure to real-world construction environments. However, despite their educational value, such visits present multiple challenges, including scheduling difficulties, limited site capacity, health and safety restrictions, induction requirements, and high logistical costs. In recent years, Virtual Reality (VR) has offered new opportunities to deliver remote, realistic, and interactive site experiences that replicate the authenticity of on-site learning without requiring physical presence. Previous studies have primarily focused on fully computer-generated VR environments developed using platforms such as Unity or Unreal Engine. While these models offer flexibility and interactivity, they often lack the contextual realism of actual construction sites. Addressing this gap, the present study designed and developed a VR immersive construction site experience tailored to the Australian construction industry, built from 111 high-resolution 360° panoramas captured at a real construction site. The system architecture was developed using an incremental development approach to manage complexity, refine functions, and ensure continuous feedback. Drawing on insights from a systematic literature review, the system architecture specifically targeted barriers to VR adoption in construction education. The resulting prototype was evaluated by 31 students, revealing positive perceptions across all key constructs, with mean scores of 4.31 for usefulness, 4.30 for ease of use, and 4.28 for learning impact (five-point Likert scale). This study represents a pioneer exploration in establishing a scalable and educationally aligned framework for VR-based construction learning. Future development stages will focus on expanding the system to cover diverse project types, integrating adaptive learning features, and linking it with learning management systems to enhance engagement and learning analytics.