Benign urological and andrological diseases (BUADs) represent common yet complex clinical challenges, where the diversity and dynamic nature of their microenvironments substantially influence disease onset and progression. In recent years, the emergence of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomic (SRT) technologies has provided unprecedented insights into the cellular heterogeneity and spatiotemporal architecture of these disease microenvironments. This review outlines current applications of scRNA-seq and SRT in BUADs, with a focus on their utility in mapping cell–cell interactions, uncovering pathological mechanisms, and prioritizing therapeutic targets. Across the upper urinary tract, prostate, bladder, urethra, and male reproductive organs, single-cell analyses reveal that parenchymal cell dysfunction and microenvironmental remodeling are the core pathological processes of disease progression. This review also discusses the limitations of scRNA-seq/SRT and the existing challenges of current research methods and paradigms in BUADs and outlines future research directions and integrative strategies to facilitate clinical translation and precision medicine. Collectively, these up-to-date findings not only deepen the understanding of the microenvironment in BUADs but also provide a scientific foundation for the development of novel therapeutic strategies.

Life chooses liquids, whereas modern electronics has long relied on solids. This fundamental distinction has shaped 2 remarkably successful yet largely independent information systems: biological systems that achieve adaptation through dynamic liquid environments and electronic systems that rely on static solid-state architectures. Although flexible electronics have largely improved mechanical compliance, their functionality remains predominantly encoded in fixed conductive pathways and predefined interfaces, limiting their ability to adapt. Here, we introduce LiqMat (liquidity-enabled matters) as a materials paradigm for adaptive electronics. Rather than defining a specific material class, LiqMat describes functional material systems whose properties emerge from liquidity-enabled dynamics rather than relying solely on static structures. We propose that dynamic interfaces, adaptive transport, and structural evolution constitute 3 core characteristics of LiqMat. These characteristics are shared by diverse liquid-enabled material systems, including aqueous systems and hydrogels, ionic liquids and ionogels, and electrofluids, as well as liquid metals and their composites, despite their distinct chemistries and transport mechanisms. Of these, liquid metals provide a particularly illustrative example of LiqMat by combining high electronic conductivity with fluidic adaptability. Looking forward, LiqMat provides a conceptual framework for shifting electronics from mechanically flexible yet functionally static systems toward adaptive electronics inspired by the dynamic organization of living systems.

We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, and defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction and technology computer-aided design (TCAD) fitting. To facilitate this task, we implement a simple data augmentation and pretraining approach by training a secondary neural network to approximate a physics-based device simulator. This method enables high-quality fits after training the neural network on electrical data generated from physics-based simulations of ~500 devices, a factor >40× fewer than other recent efforts. Consequently, fitting can be achieved by training on physically rigorous TCAD models, including complex geometry, self-consistent transport, and electrostatic effects, and is not limited to computationally inexpensive compact models. We apply our approach to reverse-engineer key parameters from experimental monolayer WS₂ transistors, achieving a median coefficient of determination (R²) = 0.99 when fitting measured electrical data. We also demonstrate that this approach generalizes and scales well by reverse-engineering electrical data from high-electron-mobility transistors while fitting 35 parameters simultaneously. To facilitate future research on deep learning approaches for the inverse design of transistors, we have published our code and sample datasets online.

Achieving a prescribed 1- or 2-dimensional curvature from a flat shape, and their active modulation through far-field intelligent control, is crucial for a range of technologically demanding applications. In this article, we endeavor to exploit the bistable mechanics of waterbomb origami units, their strategic assembly, and spatially sequential alteration of local stability states to develop a new class of active metasurfaces. Based on a series of computational and experimental investigations, we propose a sequentially programmable approach of far-field stimuli-responsive activation, exploiting locally controllable bistability for achieving target curvatures that can hold their shapes without any external energy supply. A magnetic beam amplification system is developed and tested, demonstrating contactless stability state inversion of waterbomb units, which would essentially lead to prescribed curvatures in an origami-architected 1- or 2-dimensional domain. We further demonstrate that algorithmic control of localized stability states and their dynamic inversion can lead to robotic locomotion. The presented concept of spatially sequential stability state alteration, leading to the multifunctionality of actively reconfigurable metasurfaces, gripper function, and robotic locomotion, will find critical engineering applications in the fields of active and reconfigurable antennas, solar panels, aerodynamically adaptive aerofoils, soft robotics, energy-efficient grippers, biomedical apparatus, and programmable optical and acoustic devices.

Volumetric muscle loss (VML) causes irreversible loss of contractile tissue and creates a hostile regenerative niche marked by sustained inflammation, oxidative stress, fibrosis, and poor functional recovery. Here, we develop an injectable and photocurable microsphere-in-hydrogel platform that couples structural support with sustained small-molecule immunoregulation. To counteract this hostile microenvironment, we utilized emodin, a natural anthraquinone recognized for its potent anti-inflammatory and reactive-oxygen-species-scavenging properties. Emodin-loaded sodium alginate microspheres were generated via ionic crosslinking and embedded within a gelatin methacryloyl matrix to form E-AMs@GM. The composite hydrogel exhibited defect-conforming moldability, porous microarchitecture, tunable swelling/degradation, and broad interfacial adhesion. In vitro, E-AMs@GM showed excellent cytocompatibility and attenuated intracellular reactive oxygen species in human bone-marrow-derived mesenchymal stem cells under oxidative challenge. In macrophages, E-AMs@GM reduced pro-inflammatory activation while enhancing pro-regenerative programs, accompanied by decreased inflammatory cytokines and increased interleukin-10. E-AMs@GM also promoted C2C12 myogenic differentiation and myotube maturation. In a murine VML model, E-AMs@GM alleviated inflammation and fibrotic remodeling, increased myogenic progenitor activity and myofiber regeneration, and improved locomotor performance by CatWalk analysis. Mechanistically, scRNA sequencing revealed that E-AMs@GM enriches a reparative Mmp12⁺ macrophage subset and rewires macrophage–muscle satellite cell interactions through SPP1–CD44 and SPP1–integrin signaling, consistent with accelerated transition from inflammatory clearance to tissue reconstruction. Together, this depot-enabled hydrogel provides an instructive biomaterial strategy for functional VML repair.

Background: Glioblastoma, IDH-wild-type, is a highly invasive tumor, and prognosis largely depends on the extent of resection. Contrast-enhanced (CE) MRI often underestimates tumor borders, resulting in incomplete resection. Prospective multicenter evidence on positron emission tomography (PET)/magnetic resonance (MR)-guided resection planning in newly diagnosed glioblastoma remains limited. This study is the first nationwide, multicenter evaluation of ¹⁸F-fluoroethyltyrosine (FET) PET-guided glioma resection. Methods: We conducted a multicenter, prospective cohort study to evaluate the efficacy of a novel ¹⁸F-FET PET-based surgical planning system (PET/MR cross-modal tumor delineation system [PCMDS]) in glioblastoma, IDH-wild-type resection. The study comprised 239 patients: 115 underwent ¹⁸F-FET PET-guided surgery and 124 underwent CE MRI-guided surgery. The PCMDS integrated multimodal image registration and automated metabolic segmentation to delineate surgical margins. Resection outcomes were intraoperatively validated using ultrasound, MRI, and ultramicroscopic cellular imaging. Results: Compared with the CE MRI group, the ¹⁸F-FET PET group achieved a significantly higher gross total resection rate (91.3% versus 72.7%, P < 0.05) and improved survival outcomes (median overall survival: 19.7 versus 16.0 months, P = 0.0099; median progression-free survival: 12.5 versus 9.3 months, P = 0.0078). ¹⁸F-FET PET-defined margins extended beyond CE MRI-defined boundaries, and both histopathological analysis and single-cell RNA sequencing confirmed the presence of infiltrative tumor cells in ¹⁸F-FET PET-positive but MRI-negative regions. Conclusion: This multicenter study demonstrates that ¹⁸F-FET PET-based surgical planning significantly increases the extent of glioblastoma, IDH-wild-type resection and prolongs patient survival. Integrating ¹⁸F-FET PET into routine surgical practice could substantially improve clinical outcomes for patients with glioblastoma, IDH-wild-type.

Emerging and re-emerging infectious diseases ranging from the 1918 H1N1 influenza pandemic to the recent SARS-CoV-2 and monkeypox virus outbreaks continue to pose profound threats to global public health. These crises underscore the critical need for high-fidelity and human-relevant infection models. Organoid technology has emerged as a cornerstone platform for pathogen research by faithfully recapitulating the 3-dimensional architecture and physiological microenvironment of native human tissues in vitro. This review systematically examines the development and structural refinement of organoid-based infection models with an emphasis on evidence-based strategies for stem cell source selection, extracellular matrix optimization, and dynamic culture system engineering. Such advancements enable the robust generation of multi-organ models including respiratory, intestinal, and neural organoids tailored for investigating viral tropism, spatiotemporal infection kinetics, and host immune responses. Furthermore, we evaluate the translational utility of organoids in high-throughput antiviral drug screening and preclinical vaccine assessment. To further enhance physiological relevance and functional fidelity, organoid platforms are being increasingly combined with advanced engineering strategies, including coculture approaches, CRISPR–Cas9-mediated genetic perturbation, engineered microphysiological systems (such as organ-on-a-chip), and 3D bioprinting. These integrated technologies improve biomimicry while expanding experimental controllability and scalability. In addition, we critically examine the major bottlenecks limiting clinical translation and discuss emerging frontiers driven by artificial intelligence and synthetic biology. Through iterative technological refinement and cross-disciplinary convergence, organoids have evolved beyond reductionist in vitro surrogates into physiologically informed and mechanism-driven platforms that advance our understanding of host–pathogen interactions while enhancing global preparedness against emerging pathogens.

Early-life adversity, including prenatal stress exposure, has enduring effects on stress responsivity later in life. Yet the impact of maternal stress on offspring susceptibility to stress and its underlying biological mechanisms remain to be elucidated. Here, we established a “2-hit” stress model to investigate whether maternal chronic unpredictable stress (E9.5 to E18) increases offspring susceptibility to social isolation during adolescence (P28 to P49). Our study reveals that maternal stress increases susceptibility to adolescent social isolation in male—but not female—offspring, manifested as increased anxiety- and depressive-like behaviors. This vulnerability is mediated by the priming of hippocampal dentate gyrus microglia through the complement C3–C3aR signaling pathway, which promotes the aberrant phagocytosis of excitatory synapses and reduces glutamatergic neuronal activity. Notably, pharmacological blockade of C3aR or chemogenetic activation of glutamatergic neurons in the dentate gyrus during adolescence effectively alleviated stress susceptibility. Therefore, our findings identify a targetable immune-mediated mechanism in the hippocampus that underlies stress vulnerability in offspring exposed to gestational maternal stress, offering new avenues for preventing adolescent-onset mood disorders.

The strong interaction is the fundamental force that holds quarks and the gluon force carriers together to form protons and neutrons and also binds the atomic nucleus. The theory governing quark–gluon interactions is quantum chromodynamics (QCD). A wide variety of experimental data teaches us that quarks and gluons cannot be observed in isolation, a phenomenon known as confinement that is unique to QCD. But no one has used QCD to mathematically prove confinement. Here, we show how to define and measure the force on quarks in the proton using available experimental data. Direct evidence for confinement is obtained because the force is found to be attractive and constant for a wide range of quark positions. This work guides future experimental efforts at future electron-ion colliders aimed at obtaining a rigorous quantitative understanding of confinement and the origin of nuclear mass.

Immune evasion in lung cancer is tightly regulated by epigenetic mechanisms. This study identifies key epigenetic drivers of immune escape in lung cancer and elucidates how they remodel the tumor microenvironment to induce CD8⁺ T cell exhaustion. We performed a CRISPR-negative screen targeting 159 epigenetic regulators (mEpi driver library) in a mouse model of lung metastasis and used single-cell sequencing, cell-based experiments, and chromatin analyses to identify mechanisms of immune escape. SET domain-containing protein 2 (SETD2) emerged as a critical suppressor of immune evasion. Loss of SETD2 markedly increased lung colonization and metastasis in mice with intact T cells, whereas this effect disappeared after T cell depletion. Mechanistically, SETD2 recruited adenine–thymine-rich interaction domain-containing protein 2 (ARID2) to maintain a repressive chromatin state at genes encoding the chemokines C-X-C motif chemokine ligand 1, 2, and 5. Loss of the SETD2–ARID2 axis increased secretion of these chemokines and recruited polymorphonuclear myeloid-derived suppressor cells, which inhibit T cell responses. These cells increased expression of free fatty acid receptor 2 (FFAR2), depleted arginine from the tumor environment, and impaired signaling required for T cell activation, driving cytotoxic T cells into terminal exhaustion. Depleting polymorphonuclear myeloid-derived suppressor cells or conditionally deleting FFAR2 in these cells restored T cell function and reduced metastasis of SETD2-deficient tumors. These findings show that loss of SETD2 creates an arginine-depleted, immunosuppressive tumor environment through a chemokine–FFAR2 pathway, revealing an epigenetic–metabolic immune checkpoint that may inform combination treatments for lung cancer.