Spatial omics is a broad term referring to technologies that allow for biomolecules to be observed within their native tissue context. These technologies have been used by biomedical researchers to gain a better understanding of cellular interactions, tumor microenvironment dynamics, and immune cell infiltration. While the basic outputs, such as spatial coordinates, segmentation masks, and transcript/protein matrices, are provided by the instrument software, the true biological insights come from several downstream, specialized analysis steps. Since spatial omics remains a relatively new field, no unified analysis pipeline has yet been established to encompass all platforms. Most workflows are adapted from single-cell RNA sequencing analysis frameworks, while incorporating additional steps that are specific to spatial data, especially for imaging-based technologies. At the same time, the diversity of platforms, data modalities, and output formats has introduced substantial challenges for data representation, interoperability, and cross-platform integration, highlighting the need for flexible, spatially aware, and user-friendly data structures made specifically for imaging-based data not merely adapted from other methods. This review summarizes the general analytical steps following spatial omics data acquisition, commonly used data infrastructures and tools, existing gaps, and future directions in the field.

Amino acids are typical naturally occurring chiral compounds, whose enantiomeric pairs show distinct biological functions and markedly different application prospects. The precise chiral recognition of amino acid enantiomers is of great significance in diverse fields including life sciences, medical diagnosis, and pharmaceutical development. Over the past decade, chiral fluorescent probes toward amino acids have gained considerable attention for enantiomer recognition. Among these probes, Schiff base chiral fluorescent probes have emerged as a powerful class of sensors for achieving high enantioselective recognition of amino acids, owing to their advantages of facile synthesis, tunable structure, facile functionalization, and excellent photophysical and coordination properties. This review systematically summarizes the research progress of such probes in the enantiomer recognition of different types of amino acids, including acidic, basic, non-polar, and polar amino acids. It focuses on discussing probe design strategies, enantioselective recognition, interaction mechanisms, and application developments. In addition, this paper also highlights and outlines the major challenges existing in this field and the difficult issues that need to be addressed in the future.

In recent years, attention mechanisms have gained widespread application and significant advancements in the field of biological sequence analysis. This paper systematically summarizes the fundamental principles of attention mechanisms and their latest research progress in biological sequence analysis. First, the development history of attention mechanisms is introduced, with a focus on classic mechanisms such as self-attention, cross-attention, and multi-head attention, along with their improved variants. Next, a brief overview of the classification and characteristics of bioinformatics databases is provided. Subsequently, the application of attention mechanisms in the analysis of DNA, RNA, and protein sequences is highlighted. In the realm of DNA sequence analysis, attention mechanisms have been applied to tasks such as epigenetic analysis and regulatory element identification; in RNA sequence analysis, they play a crucial role in single-cell RNA sequencing, RNA function prediction, and structure prediction; in protein sequence analysis, attention mechanisms are widely used in protein classification, function prediction, structure prediction, site prediction, and interaction prediction. Furthermore, this paper summarizes the applications of attention mechanisms in other biological sequence analysis tasks, such as multi-omics analysis and enzyme analysis. The attention mechanisms can significantly improve the accuracy, interpretability, and computational efficiency of biological sequence analysis, providing powerful computational tools for bioinformatics research.

Neurons rely on precise nutrient-sensing mechanisms to sustain proteostasis and stress resilience across a lifetime. Among these, mechanistic target of rapamycin complex 1 (mTORC1) functions as a central metabolic hub, integrating amino acid availability, growth factor signals, and energetic status to coordinate protein synthesis, autophagy, and neuronal survival. Neuronal mTORC1 regulation is highly specialised, reflecting unique metabolic demands, axonal compartmentalisation, and dependence on long-term homeostatic control that is not shared by non-neuronal cell types. Beyond canonical PI3K–Akt and AMP-activated protein kinase (AMPK) signaling, emerging evidence highlights metabolic intermediates — most notably leucine-derived acetyl-coenzyme A (AcCoA) — as critical upstream regulators that couple nutrient flux to mTORC1 activity via EP300-mediated Raptor acetylation. Chronic dysregulation of these pathways drives persistent mTORC1 hyperactivation, progressive autophagy impairment, and accumulation of proteotoxic species, collectively contributing to neurodegeneration. In Alzheimer’s disease, aberrant mTORC1 activity is linked to tau hyperphosphorylation and amyloid-β accumulation; in Parkinson’s disease, to α-synuclein aggregation and mitophagy failure; in Huntington’s disease, to impaired clearance of mutant huntingtin; and in amyotrophic lateral sclerosis (ALS), to dysregulated proteostasis in motor neurons. This mini review synthesizes current understanding of neuronal mTORC1 regulation, with emphasis on the AcCoA–acetylation axis as an emerging metabolic control mechanism, its disease-specific implications across major neurodegenerative conditions, and the therapeutic opportunities these insights reveal upstream of mTORC1.

Lipoprotein(a) (Lp(a)) is a low-density lipoprotein (LDL)–like particle and an established independent risk factor for cardiovascular disease. Its plasma concentration and antifibrinolytic properties are largely genetically determined, primarily by variation in the LPA gene, and in particular, by the number of kringle IV type 2 repeats. Lp(a) contributes to atherogenesis partly through its structural similarity to LDL, promoting cholesterol deposition within the vascular wall. Beyond its proatherogenic effects, Lp(a) plays a key role in acute cardiovascular events through pro-inflammatory and prothrombotic mechanisms. Elevated Lp(a) levels promote a prothrombotic state by increasing tissue factor expression andaccelerating activation of the coagulation cascade. Simultaneously, Lp(a) enhances plaque inflammation and vulnerability by stimulating monocyte activation and through the presence of oxidized phospholipids on its surface. Its structural homology with plasminogen further confers antifibrinolytic properties, allowing Lp(a) to competitively inhibit plasminogen binding to fibrin and impair fibrinolysis. This effect is compounded by increased levels of plasminogen activator inhibitor-1 (PAI-1) and a dysregulation of plasminogen activators (tPA, uPA), plasmin, and other fibrinolytic modulators. The resulting thrombotic risk reflects the dynamic balance between coagulation and fibrinolysis, which can be evaluated using global assays such as overall hemostatic potential. Although novel Lp(a)-lowering therapies achieve substantial reductions in circulating Lp(a) concentrations, their effects on hemostatic balance and clinical outcomes remain to be fully elucidated. This review summarizes current evidence on the role of Lp(a) in coagulation and fibrinolysis, with particular emphasis on the complex interplay between its concentration, structure, genetic deteminants, and contribution to cardiovascular risk.

While genomic instability is a hallmark of aging, and unrepaired or mutagenic double-strand breaks (DSBs) are established drivers, recent evidence suggests that even accurately repaired DSBs contribute to aging. Here, we focus on an intriguing study by Bantele et al. published in Science, which demonstrates that Cas9-induced DSB repair can induce persistent, heritable alterations in higher-order chromatin structure and function, termed "chromatin fatigue". These alterations, characterized by changes in chromatin topology and gene expression, persist long after DNA sequence restoration and are inherited through cell divisions. Crucially, they impair transcriptional responsiveness to physiological stimuli. This finding provides a novel mechanism for DNA damage-driven aging independent of mutations, potentially explaining age-related epigenetic dysfunction. The commentary also highlights key unresolved questions regarding the permanence, locus-specificity, and physiological impact of chromatin fatigue, and explores its interaction with age-related DNA repair decline. This striking molecular phenomenon challenges the notion that faithful repair ensures full functional restoration and opens avenues for future research into interventions against aging and other age-related diseases, such as cancer.

Paramorphism effect could be an effective strategy to design efficient catalysts, but has been rarely explored. In this study, to achieve efficient catalysts for elimination of NO and CO together, Ce₂Sn₂O₇ pyrochlore and Ce₂Sn₂O_8+x solid solution paramorphs were purposely synthesized to support FeO_x. It is found that Fe/Ce₂Sn₂O₇ displays better reaction performance than Fe/Ce₂Sn₂O_8+x. H₂-TPR results have demonstrated that the dispersed FeO_x has differed interaction with the two types of supports. Electron paramagnetic resonance (EPR) and density functional theory (DFT) calculation have testified that it is easier to generate surface vacancies on Fe/Ce₂Sn₂O₇ than on Fe/Ce₂Sn₂O_8+x, thus forming more abundant active oxygen sites. Furthermore, the total number of Lewis and Brønsted sites on Fe/Ce₂Sn₂O₇ is larger. In addition, reactive NH₄⁺ linked to Brønsted acidic sites and bridge nitrite are uniquely formed on Fe/Ce₂Sn₂O₇, thus leading to its much better performance than on Fe/Ce₂Sn₂O_8+x. Notably, Fe/Ce₂Sn₂O₇ also exhibits better sulfur and water tolerance. On both catalysts, the NH₃-selective catalytic reduction (NH₃-SCR) reaction obeys a Langmuir-Hinshelwood pathway, while the CO oxidation follows a Mars-van Krevelen mechanism. In summary, a paramorphism effect is obviously observed, which could give people some new thoughts to design high-performance catalysts.

Secreted proteins mediate intercellular and inter-organ communication and are essential for coordinating physiological processes across tissues. Advances in proteomics and proximity labeling have greatly expanded the catalog of circulating secreted factors; however, for many of these molecules, their cognate receptors and mechanisms of action remain unknown. This lack of receptor annotation represents a major bottleneck in understanding systemic signaling networks and translating secretome discoveries into biological insights. In this review, we summarize and evaluate the strengths and limitations of current strategies for deorphanizing secreted proteins, including 1) biochemical approaches such as affinity purification–mass spectrometry and crosslinking-based receptor capture, 2) genetic screening strategies in both in vivo and in vitro systems, including RNA interference and Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR)-based perturbation and activation platforms, and 3) computational frameworks based on AI-driven protein structure modeling. Finally, we outline future directions aimed at accelerating ligand–receptor identification, including multiplexed screening platforms, approaches to improve sensitivity for low-affinity interactions, synthetic biology tools that convert transient binding events into stable readouts, and integration with single-cell and spatial transcriptomic technologies. Together, these advances provide a roadmap for transforming classical ligand deorphanization into a scalable, context-aware framework for decoding inter-organ communication.

Aims: The Pro-Glu (PE) and Pro-Pro-Glu (PPE) protein family of Mycobacterium tuberculosis plays a critical role in virulence, immune evasion, and host-pathogen interactions. However, the high guanine-cytosine-content and repetitive sequences of these proteins have long hindered accurate gene identification and functional annotation. This study aims to develop an effective computational framework to improve the identification of PE/PPE proteins.

Methods: We propose PIONEER, a structure-aware deep learning framework that integrates embeddings from the pre-trained protein language model ESM Cambrian (ESMC) with structural features. PIONEER represents proteins as residue-level graphs that encode both sequence semantics and three-dimensional topological structure, enabling effective modelling of hierarchical geometric relationships.

Results: Benchmarking demonstrates that PIONEER outperforms 16 traditional machine learning algorithms and the existing deep learning model across multiple evaluation metrics, including accuracy, Matthew’s correlation coefficient, and F1 scores. Ablation experiments confirm the complementary contributions of ESMC embeddings and secondary structure features to model performance. The t-SNE-based visualization results reveal the contributions of features across different network layers to the identification of PE/PPE proteins.

Conclusion: PIONEER improves the accuracy of PE/PPE protein identification by integrating sequence and structural information within a structure-aware graph learning framework. This method provides an effective computational tool for functional annotation, investigation of pathogenicity mechanisms, and vaccine target discovery in M. tb.

Ovarian cancer remains the most lethal gynaecological malignancy, due to late diagnosis, extensive peritoneal dissemination, and the common emergence of therapy resistance. While intrinsic genomic instability and DNA repair defects have long been considered the main biological events underlying ovarian cancer pathogenesis, it is now evident that disease progression is critically shaped by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) constitute the main immune population in both solid lesions and malignant ascites, orchestrating tumor growth, metastatic dissemination, immune evasion, and chemoresistance. In parallel, ferroptosis, an iron-dependent, lipid peroxidation-driven form of regulated cell death, has emerged as a therapeutic vulnerability in ovarian cancer, particularly in platinum- and Poly (ADP-ribose) polymerase (PARP) inhibitor-resistant disease. TAMs and ferroptosis engage in a bidirectional and context-dependent crosstalk: TAMs iron handling, redox activity, and polarization states modulate ferroptotic sensitivity of ovarian cancer cells, while ferroptotic stress reshapes TAMs phenotype, cytokine release, and immunosuppressive capacity. In this review, we reframe ovarian cancer as an immune-metabolic disease in which ferroptosis and TAM biology form a tightly coupled regulatory axis. We synthesize current mechanistic insights and propose that effective therapeutic ferroptosis induction requires concurrent modulation of TAMs plasticity.

Cells rely on lysosomes and autophagy to maintain homeostasis under fluctuating environmental and metabolic conditions. However, how these degradative systems are dynamically coordinated across stress, senescence, and aging remains incompletely understood. Transcription factor EB (TFEB), a member of the microphthalmia/transcription factor E (MiT/TFE) family, has emerged as a key regulator of lysosomal biogenesis and autophagy by controlling the coordinated lysosomal expression and regulation (CLEAR) gene network, integrating nutrient sensing, mitochondrial status, Ca²⁺, redox signaling, and mechanistic target of rapamycin complex 1 (mTORC1) activity. While TFEB activation promotes lysosomal and metabolic adaptation during acute stress, accumulating evidence indicates that its activity is tightly constrained in time and magnitude, and that altered TFEB dynamics critically shape cellular fate decisions. Here, we synthesize current findings showing that transient TFEB activation supports stress resilience and recovery. In contrast, persistent, insufficient, or dysregulated TFEB signaling contributes to divergent senescence trajectories and age-associated decline in proteostasis. We further discuss how defects in TFEB regulation underlie impaired autophagy–lysosome function during aging across tissues. Notably, both insufficient and excessive TFEB activity can be maladaptive. Together, this framework positions TFEB as a dynamically regulated node linking stress adaptation, senescence progression, and aging, and highlights the need for context- and tissue-specific strategies aimed at restoring TFEB responsiveness rather than constitutively enhancing its activity.

Open-water drowning is a leading serious-injury/fatality risk at public waterfronts and in construction over or adjacent to water, where long stand-off views, glare, waves, and occlusions hinder timely detection. We propose TimeSformer+MIL, a weakly supervised temporal framework for event-level drowning monitoring designed for deployment in safety-critical construction settings and aligned with supervisory workflows. The system standardizes video streams into short clips, extracts spatiotemporal evidence with a divided space–time TimeSformer, and aggregates clip scores via top-k multiple-instance learning with a lightweight consistency prior to stabilize weak labels and support calibration. By avoiding person detection and multi-object tracking, the pipeline reduces engineering complexity and failure modes common in cluttered, low-light, or small-scale scenes, improving reliability without increasing operator load. We curate an open-water dataset spanning construction and public-waterfront contexts and evaluate with event-focused metrics aligned to risk governance: recall at target false-positives per hour, alert latency relative to rescue windows, and calibration-aware ranking and thresholded decisions for alerting and escalation. Across clip lengths and aggregation strategies, the approach delivers robust discrimination and translates it into stable, low-latency alerts that meet rescue-time targets while limiting alarm fatigue. For architectural practice and construction safety management, the framework offers a practical path to augment human surveillance with machine attention, functioning as a verifiable administrative control within the hierarchy of controls and integrating with site safety processes to accelerate incident recognition and strengthen risk governance in dynamic open-water settings.

Aims: Testing the hypothesis that excess mutations induced in primary fibroblasts by a low dose of N-ethyl-N-nitrosourea (ENU) are inversely correlated with species-specific maximum life span.

Aims: Testing the hypothesis that excess mutations induced in primary fibroblasts by a low dose of N-ethyl-N-nitrosourea (ENU) are inversely correlated with species-specific maximum life span.

Methods: To measure excess mutations induced by ENU we treated primary cells of 10 mammalian species, greatly differing in life span. We treated all cells with a low dose, non-toxic dose of ENU (20 ug/ml). We then extracted DNA from all treated and untreated cells and quantified somatic mutation burden by single-molecule sequencing. We measured excessive mutations by calculating the ΔSNVs and we analyzed this across species with linear regression.

Results: The average values for ΔSNV were found to range from 0.773 in mice to 0.367 in whale, resulting in a modest inverse correlation with species-specific maximum life span (R² = 0.2067, P < 0.001).

Conclusion: We conclude that DNA repair accuracy, the main determinant of genome sequence integrity, modestly correlates with life span suggesting that longer lived species have better repair capacities compared to shorter-lived species, which is in keeping with genome instability being a primary hallmark of aging and highlights its important role for longevity.

Autophagy is a conserved cellular clearance pathway that supports homeostasis by removing damaged or superfluous intracellular components. Within microglia, autophagy is emerging as a regulator of key processes that modify neurodegeneration, including phagocytosis, cytokine secretion, and senescence. Many studies that have examined the effect of disrupted autophagy on microglial functions have used genetic knockouts of the machinery required to conjugate microtubule-associated light chain 3 (LC3) to the autophagic membrane. However, much of this molecular machinery is also required for a set of distinct but related pathways known as the conjugation of ATG8s to single membranes (CASM). CASM includes processes of particular importance in microglia, such as LC3-associated phagocytosis and LC3-associated endocytosis. It is thus not clear which of the effects of the disruption of LC3 conjugation in microglia are attributable to the loss of autophagy or the loss of CASM function. In this review, we describe the mechanisms of autophagy and CASM and highlight the effects of the loss of these pathways on key microglial processes relevant to brain ageing and neurodegenerative diseases. We discuss recent literature that has revealed the effects of ageing and neurodegeneration on microglial autophagy, and the effects of microglial autophagy and/or CASM disruption on key microglial functions such as phagocytosis, cytokine secretion, and senescence. Finally, we discuss the potential therapeutic implications of these findings for neurodegeneration and highlight key unanswered questions for future research.

Aims: To develop a robust and clinically feasible framework for cancer survival prediction using only histopathology images while leveraging transcriptomic knowledge during training.

Aims: To develop a robust and clinically feasible framework for cancer survival prediction using only histopathology images while leveraging transcriptomic knowledge during training.

Methods: The study proposed Adaptive Multi-modality Knowledge Distillation (AMKD), a framework designed to transfer complementary molecular-level information from transcriptomic data to pathology-based models. The AMKD framework consists of two essential elements. First, a gene-guided pathology enhancement module is designed to inject genomics-aware information from a multimodal teacher into pathology features. Second, an adaptive redundancy reduction loss is introduced to regulate knowledge distillation by accounting for prediction discrepancies between teacher and student models. This design allows the student model to retain biologically meaningful knowledge during training and remain effective with only histopathology data at inference.

Results: Comprehensive experiments on four The Cancer Genome Atlas (TCGA) cancer cohorts demonstrate that AMKD achieves state-of-the-art survival prediction performance, with an average concordance index (C-index) of 0.669, surpassing both unimodal pathology-based and fully multi-modal approaches. External validation on the independent Clinical Proteomic Tumor Analysis Consortium head and neck squamous cell carcinoma (CPTAC-HNSC) cohort further confirms the robustness and cross-dataset generalization ability of the proposed framework. Ablation studies further confirm the effectiveness of each proposed component in enhancing cross-modal knowledge transfer and improving generalization under incomplete modality scenarios.

Conclusion: The proposed AMKD framework provides a clinically practical solution for robust cancer survival analysis when transcriptomic data are unavailable. By adaptively distilling multi-modal knowledge into a pathology-based model, AMKD bridges the gap between research and clinical applicability, enabling scalable and cost-effective prognostic prediction in real-world settings.

Self++ is a conceptual design framework for human–Artificial Intelligence (AI) symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both perceptual evidence and action, apparently ‘helpful’ assistance can drift into over-reliance, covert persuasion, and blurred responsibility. Self++ grounds interaction in two complementary theories: Self-Determination Theory (autonomy, competence, relatedness) and the Free Energy Principle (predictive stability under uncertainty). It operationalises these foundations through co-determination, treating the human and the AI as a coupled system that must keep intent and limits legible, tune support over time, and preserve the user’s right to endorse, contest, and override. These requirements are summarised as the co-determination principles (T.A.N.): Transparency, Adaptivity, and Negotiability. Self++ organises augmentation into three concurrently activatable overlays spanning sensorimotor competence support (Self: competence overlay), deliberative autonomy support (Self+: autonomy overlay), and social and long-horizon relatedness and purpose support (Self++: relatedness and purpose overlay). Across the overlays, it specifies nine role patterns (Tutor, Skill Builder, Coach; Choice Architect, Advisor, Agentic Worker; Contextual Interpreter, Social Facilitator, Purpose Amplifier) that can be implemented as interaction patterns, not personas. The contribution is a role-based conceptual map that generates testable design propositions for XR-AI systems that grow capability without replacing judgement, enabling symbiotic agency in work, learning, and social life and resilient human development.

Genomic instability (GI), characterized by the progressive failure of mechanisms that maintain genome integrity, serves as a fundamental link between aging and cancer at the molecular level. It not only drives the aging process but also promotes tumorigenesis through multiple pathways: on one hand, GI can induce cellular senescence and create a pro-inflammatory and tissue remodeling microenvironment via the senescence-associated secretory phenotype; on the other hand, GI can bypass senescence, directly facilitating tumor progression through mechanisms such as aneuploidy, the expansion of pre-malignant clones, and chronic inflammation mediated by DNA damage-associated molecular patterns. The decline in physiological functions accompanying aging and the increased risk of cancer are closely associated with the accumulation of GI, while aging itself may exert anti-cancer effects through irreversible cell cycle arrest in specific contexts. Therefore, a thorough investigation of GI’s dual role in aging and cancer can help reveal the shared biological basis of both processes and provide new strategies for the precise prevention and treatment of age-related tumors.

Accumulating evidence has indicated that the normal tissue adjacent to tumor (NAT) is distinct from both healthy and tumor tissues. It is suggested that the crosstalk between NAT and tumor tissues helps shape the tumor microenvironment and promotes cancer progression, but the molecular and cellular evidence for this crosstalk is scarce. In this perspective, we propose that NAT tissue constitutes a unique “peritumoral microenvironment (Peri-TME)” mostly conditioned by the tumor. Furthermore, cellular senescence is identified as a key characteristic of Peri-TME, which accelerates tumor growth, as illustrated in our recent studies in colorectal cancer (CRC). Finally, strategies to target the senescent Peri-TME may represent an effective means to disrupt the vicious interaction between Peri-TME and Tumor and enhance therapeutic efficacy.

Aging is a heterogeneous, multi-system process driven by the interplay between accumulating molecular damage and the progressive erosion of resilience. While damage accumulates in a ubiquitous and homogeneous fashion, resilience is finite and unequally distributed across physiological systems and individuals, yielding distinct biological trajectories that diverge early in life, giving rise to the individual-specific decline in physiological function and the manifestation of a diverse spectrum of organ-specific diseases, and converge only when multisystem dysregulation overwhelms compensatory capacity. Early deviations in mitochondrial function, proteostasis, inflammation, and metabolic regulation often remain clinically silent, detectable only through gerodiagnostics, longitudinal, sensitive biomarkers of aging. Yet most biomarkers were developed to detect disease rather than quantify aging biology, and their mechanistic specificity declines with advancing multimorbidity. Precision geromedicine therefore requires the integration of gerodiagnostics that capture system-level resilience and stress responsiveness with measures of functional reserve, behavior, physiology, and the exposome, enabling the identification of individualized aging trajectories and the biological pathways that drive them. This combined approach clarifies causal pathways, enables earlier detection of vulnerability and supports individualized gerointerventions that modify aging trajectories rather than specifically but narrowly focusing on individual age-related diseases.

Knots and links play a fundamental role across a wide range of physical fields, from classical to quantum physics. In optics, structured light fields with multiple controllable degrees of freedom provide a versatile experimental platform for investigating topological properties. Knot theory underpins the topological control of high-dimensional structured light, thereby giving rise to fundamental physical effects and applications. Furthermore, topologically structured light has attracted significant attention for light-matter interaction. Here we review the recent advances in topologically structured light from the perspective of knot theory. Starting from the basics of knots and related braids, we introduce the generation and manipulation of topologically structured light from the purely spatial domain across to the spatiotemporal domain. Moreover, we demonstrate that the particle-like structured light, such as photonic skyrmions and hopfions, can host the topologies of high dimensional space, followed by brief discussions on potential applications as well as an outlook and future trends and challenges in this field.

Lymphatic endothelial cells (LECs) and the lymphatic vasculature have evolved from being viewed as passive conduits for fluid drainage and metastatic dissemination to active, dynamic regulators of inflammation and tumor immunity. In solid tumors, both tumor-associated and lymph node (LN)–resident LECs engage in complex interactions with their environment to orchestrate immune processes, including antigen transport and presentation to T cells, leukocyte recruitment and trafficking via chemokine gradients, and local immune modulation through the expression of co-inhibitory ligands such as programmed death-ligand 1 (PD-L1). These multifaceted roles enable LECs to either amplify effector responses or induce tolerance, profoundly influencing the efficacy of cancer immunotherapies depending on their activation state, tissue context, and molecular programming. This minireview synthesizes and discusses recent advances in tumor lymphangiogenesis, the role of LECs and their intensive crosstalk with the immune compartments, in the coordination of anti-tumor immune responses, with particular focus on LEC-autophagy as a lipid metabolic checkpoint controlling lymph node T cell egress, and its far-reaching implications for optimizing immunotherapy outcomes in solid tumors.

Artificial intelligence (AI) is revolutionizing the design of ionizable lipids, the pivotal components of lipid nanoparticles (LNPs) for messenger RNA (mRNA) delivery, enabling efficient exploration of vast chemical space of ionizable lipids beyond the reach of traditional methods. This mini-review explores the burgeoning field of AI-powered design and optimization of ionizable lipids for mRNA delivery. We also discuss the critical role of high-throughput experimental strategies, particularly barcoding coupled with next-generation sequencing, in generating the large-scale in vivo datasets for model training. Finally, we discuss current challenges, including data quality and the necessity for domain-specific modeling strategies, and present a future outlook on the integration of AI with scientific computing for LNP research.

Ferroptosis, a regulated cell death modality, driven by iron-dependent lipid peroxidation, is intrinsically coupled with mitochondrial metabolic turbulence and redox dysregulation. While the mitochondrial sirtuin Sirtuin 3 (SIRT3) is canonically viewed as a master regulator of energy homeostasis, its defensive repertoire against ferroptosis extends far beyond the simplistic activation of antioxidant enzymes. In this review, we synthesize emerging evidence to construct an integrated “metabolic-structural” defense model orchestrated by SIRT3. We first delineate how SIRT3 functions as a metabolic rheostat, rewiring tricarboxylic acid (TCA) cycle flux via the deacetylation of isocitrate dehydrogenase 2 (IDH2) to sustain the nicotinamide adenine dinucleotide phosphate (NADPH)/glutathione (GSH) reservoir. Breaking away from the classical enzymatic paradigm, we highlight a novel non-enzymatic substrate regulatory mechanism where SIRT3 stabilizes the glutamate transporter SLC25A22 through specific deacetylation-ubiquitination crosstalk, thereby limiting ferroptotic susceptibility. Furthermore, we expand the SIRT3 signaling landscape by proposing a “SIRT3-nuclear factor erythroid 2-related factor 2 (Nrf2) deacetylation axis” that bridges mitochondrial stress signals to nuclear transcriptional defense, and by detailing its control over iron entry via the iron regulatory protein 1 (IRP1)-transferrin receptor 1 (TfR1) pathway. At the organelle level, we examine how SIRT3 remodels mitochondrial dynamics, upregulating optic atrophy-associated protein 1 (OPA1) while suppressing dynamin-related protein 1 (DRP1), to construct a “fusion network barrier” that dilutes ROS toxicity. We also posit a critical hypothesis: SIRT3 safeguards the integrity of mitochondria-associated endoplasmic reticulum membranes, preventing structural decoupling and calcium overload that triggers ferroptotic sensitization. Finally, we reconcile the context-dependent duality of SIRT3 in cancer and degenerative diseases, outlining future therapeutic strategies that leverage these multidimensional vulnerabilities.