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.

Axially chiral biaryls exhibit broad applications across diverse disciplines, and thus the development of conceptually new methods for accessing this structural motif remains highly desirable. Herein, we report the successful implementation of atroposelective de novo benzene formation via asymmetric [2+4] annulation in the enantioselective synthesis of axially chiral 1,2'-binaphthyls using β-substituted α-naphthylalkynes and o-iodostyrenes as C-4 and C-2 synthons, respectively. This nickel/PyrOX-catalyzed annulation reaction features a broad substrate scope (43 examples), excellent regiocontrol, exclusive 6-endo cyclization, and good to high enantioselectivity (up to 92% ee). Furthermore, the synthetic utility of the products is demonstrated through their efficient derivatizations into a range of potential bidentate ligands and bifunctional organocatalysts.

Friedreich’s Ataxia (FRDA) is a rare neurodegenerative condition driven by a severe deficiency of the mitochondrial protein frataxin. 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 frataxin 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.

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.

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.

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 cGAS-STING pathway may pave new ways for therapeutic strategies to mitigate cellular senescence-associated diseases.

Aims: Single-cell RNA sequencing 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 non-dominated sorting genetic algorithm II and technique for order preference by similarity to ideal solution methods. The proposed prediction and optimization framework for 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.

Predicting developmental trajectories and disease progression is a central goal of virtual biology. While conventional biological network models struggle with noise and computational complexity, black-box deep learning obscures mechanistic interpretability. In this Perspective, we introduce “gene programs” (GPs) as the essential intermediate layer to close this gap. Extracted from massive single-cell datasets, GPs provide relatively robust and interpretable modules that capture coherent biological functions. Supported by recent multi-modal and large-scale perturbation advances, GPs may reflect underlying cross-layer biophysical structures. We propose a framework where next-generation virtual cells encode GPs as latent variables in neural networks. By incorporating spatial multicellular contexts and multimodal GP extraction, this GP-centric framework will reduce data noise and allow direct perturbation-to-phenotype mapping, enabling high-fidelity simulations of complex tissue dynamics. Anchoring virtual models on GPs paves the way for highly personalized “Patient Digital Twins”, empowering predictive in silico clinical simulations and advancing mechanism-driven precision medicine.

Operating as a physically and physiologically integrated unit, skeletal muscle and bone are fundamental to human mobility and metabolism. Their reciprocal crosstalk endures throughout life. In early embryonic development, with a primary focus on morphogenesis, skeletal muscle and bone actively drive the functional maturation of both tissues during development. When the interplay reaches relative homeostasis during adulthood, they reciprocally sustain the functional integrity and physiological homeostasis of one another. With aging, however, this intimate connection exacerbates reciprocal decline, initiating a pathological feed-forward loop that precipitates osteosarcopenia. As this crosstalk is orchestrated by a shifting matrix of mediators, from biomechanical loads to neuronal, immunological, and secretory signals, understanding their age-associated alterations may help provide a point of intervention for treatment. This review outlines dynamic changes in muscle-bone crosstalk throughout the lifespan, discusses longitudinal changes in aging, and provides stage-specific perspectives for intervention.

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.

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 acid, protein and reactive oxygen specie. 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.

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 incidences 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.

Frailty, a geriatric syndrome marked by multisystem functional decline and heightened vulnerability, threatens older adults’ health span. Immunosenescence and chronic low-grade inflammation are increasingly recognized as core drivers, with age-related impairments in cellular, humoral, and innate immunity disrupting immune homeostasis. Tertiary lymphoid structures (TLS), ectopic immune aggregates that orchestrate local immune responses, have emerged as candidate regulators in chronic disease, yet their role in frailty remains largely unexplored. Here, we propose a conceptual framework for frailty pathogenesis in which immunosenescence and chronic low-grade inflammation (inflammaging) may act as upstream biological drivers associated with systemic lymphatic dysfunction, potentially contributing to the structural and functional dysregulation of TLS, a candidate intermediate tissue-level histological link. TLS dysfunction may subsequently contribute to multi-organ functional decline and the progression of frailty. We synthesize current evidence on the dynamic characteristics of TLS in aging, discuss TLS as candidate biomarkers and putative therapeutic targets for frailty, and highlight intervention strategies including molecular modulation, lymphatic function enhancement, immunotherapy, and lifestyle modifications. This tissue-level perspective highlights TLS as a potential histological link connecting immunosenescence, lymphatic dysfunction, and frailty, offering novel avenues for geriatric precision medicine and the development of immune-targeted interventions to delay frailty progression.

Heavy metal ions (HMIs) pose persistent risks to ecosystems and human health owing to their toxicity, environmental persistence, and bioaccumulation. Conventional laboratory techniques provide high sensitivity and accuracy, but their dependence on bulky instruments, skilled operators, and complex pretreatment restricts rapid on-site screening. Portable electrochemical systems offer a complementary strategy by integrating sensing electrodes, potentiostatic control, weak-current readout, and software-based signal processing to convert interfacial redox reactions into measurable electrical signals. This review examines portable electrochemical HMI detection from three perspectives: detection principles, hardware architectures, and field applications. It summarizes redox and stripping mechanisms, baseline correction, limit-of-detection (LOD) estimation, potentiostat evolution, and single- and multi-metal detection in complex matrices. Key bottlenecks include matrix-induced peak drift and fouling, coexisting-ion interference, limitations in weak-current readout, and insufficient field standardization. Future progress will require co-optimized sensing interfaces, low-noise electronics, multichannel and flow-cell formats, field calibration, and data-driven peak analysis.

Wearable electrocardiography (ECG) is shifting cardiovascular monitoring from episodic in-hospital testing to continuous out-of-hospital assessment. Artificial intelligence-enabled ECG (AI-ECG) provides a new pathway for extracting clinically useful information from out-of-hospital wearable recordings. This review is organized around the wearable AI-ECG monitoring pipeline, summarizing key advances in model development, clinical application, and real-world validation, while emphasizing the special requirements that wearable scenarios impose on algorithm design and clinical translation. At present, arrhythmia screening remains the most mature applications of AI-ECG, with deep learning models achieving cardiologist-level or clinician-comparable performance in several well-defined tasks. With the growing availability of long-term continuous recordings, research is further extending from post-event recognition to pre-event warning, particularly for high-risk events such as acute atrial fibrillation and malignant ventricular arrhythmias. Related methods are evolving from traditional feature-based machine learning toward deep learning and foundation models that can exploit waveform morphology, rhythm dynamics, and long-range temporal information. Beyond rhythm disorders, AI-ECG is also being explored for structural cardiac abnormalities, metabolic disorders, and broader systemic risk prediction, suggesting a potential role for ECG as a digital biomarker platform. However, several barriers continue to limit clinical translation, including limited cross-device and cross-population generalizability, insufficient interpretability, and the lack of prospective real-world validation. Future progress will likely depend on standardized data systems, artifact-aware modeling, cross-device validation, foundation models, longitudinal risk modeling, and intelligent systems designed for clinical workflows. Overall, wearable AI-ECG is evolving from passive abnormality detection toward continuous, proactive, and personalized health risk assessment.

Ferroptosis, a lipid peroxidation-driven form of regulated cell death, has emerged as a central mechanism in neurological disease. While most studies have focused on neuronal vulnerability, non-neuronal cells, including oligodendrocytes, astrocytes, microglia, brain endothelial cells, and central nervous system (CNS) infiltrating T cells, play equally critical roles in shaping disease progression. These cell types regulate iron homeostasis, lipid metabolism, antioxidant defenses, and inflammatory signaling, thereby establishing the microenvironmental conditions that determine ferroptotic susceptibility within the CNS. Accumulating evidence demonstrates lipid peroxidation and ferroptosis-related signaling in demyelinating disorders, ischemic injury, small vessel disease, Alzheimer’s disease, Parkinson’s disease, and spinal cord injury. However, the contribution of non-neuronal cells to ferroptotic stress and execution remains comparatively underexplored. In this review, we synthesize emerging data highlighting cell type-specific dependencies on glutathione peroxidase 4 (GPX4), solute carrier family 7 member 11 (SLC7A11), ferroptosis suppressor protein 1 (FSP1), nuclear factor erythroid 2-related factor 2 (NRF2), peroxiredoxin (PRDX), thioredoxin (TRX), iron-handling proteins, and lipid remodeling pathways, and discuss how these regulatory networks differ across CNS-resident and CNS infiltrating T cells. We propose that ferroptosis in neurological disease is not solely a neuron-autonomous event, but a tissue-level process orchestrated by non-neuronal cells with distinct metabolic and immunological programs. Understanding these cell type-specific vulnerabilities and regulatory mechanisms will be essential for the development of targeted therapeutic strategies aimed at modulating ferroptotic stress in neuroinflammatory and neurodegenerative disorders.

Phase-shifting profilometry (PSP), as a high-precision and low-cost three-dimensional (3D) profile measurement technique, has been extensively applied in diverse fields. By introducing the compressive sensing paradigm, compressive phase-shifting fringe projection profilometry (CPSFPP) provides an effective solution for high-speed dynamic object acquisition. Nevertheless, achieving high-fidelity dynamic profile reconstruction from compressed measurements remains a considerable challenge, owing to the inherent information loss in compressive sampling and limitations in computational reconstruction, especially under high compression ratios. To address this issue, we propose a physics-informed neural network (PINN)-based computational imaging framework for high-fidelity CPSFPP, named PINN-CPSFPP. This method integrates the physical model constraints with neural network learning to guarantee high-fidelity reconstruction even at high compression ratios. We perform numerical simulations to verify the reconstruction accuracy of PINN-CPSFPP under different compression ratios and experimentally validate the method by measuring translational, rotational, and deformed objects. The results demonstrate that the measurement speed is increased by nine times compared with conventional PSP. Benefiting from its robust 3D imaging performance, PINN-CPSFPP serves as a high-fidelity metrological tool for high-speed 3D scenarios and exhibits promising application prospects in a wide range of basic and applied disciplines.

Aging is a key risk factor for cancer, with complex, context-dependent processes influencing both tumor initiation and progression. While certain aging-associated processes restrain cellular proliferation, others drive tumor initiation and progression, revealing a context-dependent duality in the role of aging in cancer. Increasing evidence indicates that many of these transitions occur not through genomic alterations, but through reprogramming of protein post-translational modifications (PTMs). Phosphorylation, ubiquitination, acetylation, methylation and an expanding repertoire of acylations collectively regulate protein activity, stability, localization and interactions, translating aging-associated stresses, including metabolic imbalance, microenvironmental remodeling and accumulated damage, into functional cellular outcomes. In this review, we examine how PTMs bridge aging and cancer through three interconnected axes: integration of stress signals, encoding of metabolic states, and modulation of the tissue microenvironment. We discuss how major PTM systems coordinate core cellular processes, such as checkpoint control, proteostasis, chromatin organization and cell–environment communication, and how, through dynamic and combinatorial actions, they establish distinct regulatory states that determine whether tissues sustain homeostasis, progress to dysfunction, or undergo malignant transformation. An in-depth understanding of how PTMs define these states provides valuable insights into disease stratification and offers new avenues for therapeutic interventions.

Metasurfaces composed of subwavelength-scale artificial meta-atoms have emerged as a powerful platform for manipulating light. By enabling strong light-matter interactions within ultrathin planar geometries, metasurfaces have opened new avenues for highly integrated photonic devices. Lithium niobate (LiNbO₃) is considered one of the most promising multifunctional integrated photonic platforms due to its outstanding properties, such as a large second-order nonlinear susceptibility, a broad transparency window, and a strong electro-optic (EO) effect. In recent years, integrated photonic devices based on lithium-niobate-on-insulator platforms have experienced rapid development. This review summarizes recent advances in LiNbO₃ metasurfaces, providing a comprehensive overview of their demonstrated applications in nonlinear frequency conversion, wavefront and phase modulation, and dynamic EO modulation. By systematically introducing the interplay between the intrinsic material properties of LiNbO₃ and the structural design principles of metasurfaces, this review offers a coherent framework for understanding their nonlinear and active optical functionalities, and serves as a valuable reference for the design and implementation of nonlinear and actively tunable micro- and nano-photonic devices.

Single-cell RNA sequencing (scRNA-seq) has transformed the profiling of cellular heterogeneity; however, tissue dissociation for scRNA-seq eliminates three critical classes of information that often define cell state: spatial organization, morphology, and protein localization. Spatial transcriptomics partially restores this context, yet many platforms are limited by cost, throughput, or experimental complexity. Single-cell transcriptomics analysis and multimodal profiling (STAMP) tackle these limitations by immobilizing cells or nuclei on a slide, enabling high-content imaging prior to molecular readout and consequently preserving each cell’s visual characteristics alongside high-plex transcript and/or protein measurements. This mini review focuses on the downstream analytical workflow for STAMP datasets using Python, illustrating how standard single-cell methods can be applied to image-derived cell-by-feature matrices enriched with per-cell covariates. The pooled MIX sub-STAMP serves as a working example to illustrate practical steps for quality control, normalization, dimensionality reduction, clustering or label transfer, and program-level interpretation. Morpho-transcriptomic coupling is highlighted as an exploratory strategy that links inferred gene programs to image-derived morphology. Collectively, these analyses establish STAMP as a practical bridge between single-cell transcriptomics and quantitative cell phenotype, enabling interpretable state inference that can be validated by imaging-derived measurements.

Time-restricted eating (TRE), a form of intermittent fasting that limits food intake within a consistent daily window, has gained attention as a feasible strategy to promote metabolic health and align eating with circadian rhythms. Aging and obesity share overlapping pathophysiological mechanisms, including increased visceral adipose tissue (VAT), chronic inflammation, and circadian disruption, which accelerate cardiometabolic decline and multimorbidity. In this Perspective, we examine TRE within a geroscience framework, integrating evidence from human trials and preclinical models to evaluate its potential relevance for aging-related processes. We discuss how the timing of food intake influences VAT distribution and glucose regulation, with early TRE schedules showing particular promise for enhancing nocturnal glycemic control and reducing abdominal subcutaneous adipose tissue. Drawing on circadian biology and caloric restriction literature, we highlight mechanistic insights linking feeding-fasting cycles to autophagy, nutrient sensing, and longevity. We also consider emerging evidence of sex-specific responses to TRE, which may inform personalized approaches. However, most clinical studies remain short-term and focus on cardiometabolic risk markers rather than validated measures of biological aging or functional outcomes. We therefore emphasize the need to distinguish between demonstrated metabolic benefits and proposed gerotherapeutic effects, and argue that future trials should incorporate biomarkers of biological age, circadian robustness, and physiological resilience. TRE represents a low-cost, scalable, and behaviorally simple intervention that could complement existing strategies in geromedicine to extend healthspan and delay age-related decline.

Oral stem cells (OSCs) represent a group of mesenchymal stromal/stem cell-like populations localized within dental and craniofacial tissues, where they play vital roles in maintaining tissue integrity and supporting regeneration. Although OSCs possess robust self-renewal capacity and multilineage differentiation potential, they progressively develop senescent phenotypes in response to organismal aging, prolonged in vitro culture, and adverse microenvironmental stimuli. The senescent state of OSCs is characterized by diminished proliferation and differentiation, genomic instability, mitochondrial impairment, and the establishment of a senescence-associated secretory phenotype. Mechanistically, OSC senescence arises from a series of interacting processes, including DNA damage, metabolic imbalance, epigenetic reprogramming, and persistent inflammatory signaling. These alterations drive stem cell functional decline and compromise the surrounding regenerative niche. Consequently, OSC aging is closely associated with multiple craniofacial disorders, including periodontal tissue breakdown, defective dentin-pulp regeneration, alveolar bone resorption, and delayed mucosal healing. To reverse these age-related changes, various rejuvenation approaches have been explored, including epigenetic modulation, metabolic intervention, senescence-targeting therapies, extracellular vesicle-mediated strategies, and biomaterial-based niche engineering. Nonetheless, translational challenges remain, particularly in cellular heterogeneity, donor-related variability, and age-dependent functional changes in extracellular vesicles (EVs). Future efforts are expected to focus on developing targeted and clinically translatable strategies to rejuvenate OSCs and enhance regenerative outcomes.

Conductive microneedles (CMNs) combine the minimally invasive characteristics of microneedles with the electrical functionality required for sensing, recording, stimulation, and controlled drug delivery. By penetrating the stratum corneum with reduced pain and tissue damage, they provide efficient access to the skin microenvironment and have shown strong potential in wearable healthcare, precision diagnostics, and intelligent therapeutics. Despite these advantages, challenges remain in balancing mechanical robustness with electrical functionality, improving fabrication precision and reproducibility, maintaining interfacial stability, and achieving scalable manufacturing. In this review, the major fabrication routes for CMNs are summarized and compared in terms of forming principles, material compatibility, and conductivity-introduction strategies. Secondly, research on the key performances of CMNs is discussed. After that, the applications of CMNs in electrochemical sensing, bioelectrical signal acquisition, electrostimulation therapy, and drug delivery are overviewed, followed by a brief discussion of current challenges and future perspectives.

Iron is indispensable for cellular metabolism yet potentially cytotoxic, making its intracellular handling a fundamental determinant of cell fate decisions. The endo-lysosomal system has recently emerged as a central iron rheostat that integrates transferrin uptake, ferritinophagy, and lysosomal iron export to control iron bioavailability for mitochondria and other iron-dependent pathways. Growing studies further show that lysosomal iron is not merely permissive for ferroptosis but can directly initiate lipid damage through localized iron activation, lysosomal lipid peroxidation, and lysosomal membrane permeabilization. At the same time, emerging studies on organelle contact sites reveal that ferroptosis arises from the failure of a coordinated multi-organellar communication system, in which lysosomes, the endoplasmic reticulum, and mitochondria exchange iron, lipids, and redox signals in an effort to metabolically adapt to stress. This perspective is particularly relevant to drug-tolerant persisters and mesenchymal cancer cell states, which rely on rewired lysosomal iron trafficking to sustain plasticity while becoming highly susceptible to ferroptosis. In this minireview, we discuss emerging insights into the spatial organization of iron metabolism and propose a model in which ferroptosis sensitivity depends on the intracellular routing, chemical reactivity, and release dynamics of iron, highlighting lysosomal iron handling as a key therapeutic vulnerability in minimal residual disease.