Photothermal materials with high conversion efficiency offer a promising approach for preventing ice accretion on infrastructures like wind turbines. However, the conflict between weak-light inefficiency in winter and overheating hazards in summer remains a challenge for composites. Herein, we develop a robust thermochromic photothermal icephobic coating with switchable solar-driven anti-/deicing and anti-overheating modes for all-season demands. During freezing winter, a 0.05 W/cm² weak solar irradiation can raise the black coating (solar absorbance >97%) temperature from −10 to 7.6 °C and facilitate rapid ice shedding from rotating rotor. During summer, the thermochromic coating turns white with strong reflection and limits coating temperature below 51 °C, inhibiting composites oxidation above 60 °C. Strikingly, the interpenetrating elasticity and ultraslippage endow the coating with exceptional ice detachment properties, exhibiting an ultralow ice adhesion strength (<31 kPa) and sliding angle (<8.3°), which are maintained even after 200 Taber abrasion cycles. This study successfully addresses the critical challenge of regulating the all-season temperature of photothermal coatings, pioneering a new pathway for designing intelligent anti-icing coatings for wind turbines and low-altitude rotorcraft.

Continuous-variable quantum secure communication encoded by Gaussian mapping offers high-capacity and high transmission rates. For the theoretically secure encryption scheme of a 1-time pad, a highly reliable coherent-state quantum secure communication system has been established, and its security has been quantitatively evaluated using Wyner’s wiretap channel theory. We also propose an information reconstruction scheme based on multidimensional rotation to extract secret messages at low-to-medium signal-to-noise ratios. Meanwhile, to address the unbalanced optical path, we design a self-balanced homodyne detector based on a programmable gain amplifier, achieving an electronic noise variance of 1.624\times {10}^{-7} {\mathrm{V}}^2 and a bandwidth of 715 MHz. In 10-km optical fiber transmission, the system successfully achieved secure transmission of the dichroic image, processed 1,536 information blocks, each containing 2^{10} continuous variables, with a block error rate of approximately 8.78\times {10}^{-5}, and ultimately achieved the secrecy capacity of 2.44\times {10}^5 bits per second.

Nanozymes overcome key limitations of natural enzymes, including poor environmental tolerance, high production costs, and batch-to-batch variability, with their enhanced physicochemical stability and controllable catalytic performance, thus enabling applications in biocatalysis and disease theranostics. However, their practical applications remain limited by insufficient catalytic efficiency, inadequate target specificity, and restricted programmable dynamic adaptability. DNA nanotechnology offers a versatile and biocompatible framework for the precise regulation of nanozymes due to its sequence-defined programmability and precise molecular recognition capability. This approach improves catalytic performance while maintaining excellent biocompatibility and low immunogenicity. This review systematically summarizes the latest advances in DNA nanotechnology-mediated precise regulation of nanozymes, with particular emphasis on the mechanistic basis of 6 regulatory modes. We further examine the representative applications of these DNA–nanozyme hybrid systems in disease therapy, biomedical sensing, and environmental monitoring, and outline the key challenges and future directions toward clinical and industrial translation.

Endogenous bioelectric signals serve as pivotal physiological cues that govern cellular behavior and tissue regeneration. Conductive hydrogels have become a disruptive platform in the field of tissue engineering because they can promote tissue repair by utilizing endogenous electrical signals. Conventional conductive hydrogels often suffer from weak tissue adhesion and high contact impedance, which sever the continuity of endogenous bioelectric signals essential for regeneration. To bridge this gap, polyphenol-based adhesive-electrocoupling hydrogels have garnered increasing attention. The conductive network within adhesive-electrocoupling hydrogels facilitates electron transfer-mediated polyphenol redox cycling, preserving catechols for robust adhesion. This strong tissue interface integration maintains electrical signal transduction, constructing a conductive-adhesive synergistic circuit. This review first elucidates the biological effects of bioelectricity, establishing a theoretical foundation for conductive hydrogels to promote tissue repair using endogenous electrical signals. Subsequently, this review systematically summarizes the design strategies for adhesive-electrocoupling hydrogels mediated by polyphenol redox interactions, grounded in electron transfer mechanisms. Crucially, this review introduces the distinct biological mechanisms driving regeneration, highlighting the synergistic interplay among the intrinsic bioactivity of polyphenols, the modulation of cell behavior through endogenous electric field coupling, and cell adhesion. Furthermore, the versatile applications of adhesive-electrocoupling hydrogels in repairing electro-sensitive tissues are critically examined. Finally, this review discusses the current challenges and prospects.

Burn wounds are prone to infection, and traditional antibiotics cannot be used locally due to bacterial resistance. As a clinical first-line use of silver ion dressings, their use is often limited due to potential toxic side effects and high costs. To overcome these limitations, a bimetallic nanozyme with multiple antibacterial properties was proposed. CuO nanoflowers were first synthesized via a simple liquid-phase method, and then, Ag ions were doped to synthesize Ag-CuO nanozymes. In vitro/in vivo experiments/RNA sequencing revealed that it not only has a variety of enzyme activities, which can accumulate reactive oxygen species (ROS) for sterilization, but can also cooperate with the starvation-cuproptosis-like death cascade for sterilization. The genes regulating the phosphotransferase system were down-regulated by Ag-CuO nanozymes, which reduced the uptake of carbohydrates needed for energy synthesis by bacteria. The tricarboxylic acid cycle signaling pathway was inhibited, and adenosine triphosphate synthesis was reduced, thus inhibiting its own protection against external stimuli. Moreover, the down-regulation of the lpdA gene, coupled with bacterial starvation, synergistically led to an increase in Cu²⁺ influx and an increase in Cu⁺ accumulation, thereby amplifying bacterial cuproptosis-like death and biofilm inhibition. The unique ROS-starvation-cuproptosis-like death sterilization method overcomes the limitation that traditional antibiotics are easily tolerated by bacteria. Importantly, its efficacy was verified in a mouse New Zealand rabbit model, which strongly demonstrated its potential for clinical use in the treatment of drug-resistant bacterial infections, as it has excellent antibacterial activity and the ability to promote angiogenesis compared with those of commercial silver-based dressings with the same silver ion content.

Multisite chronic pain (MCP) frequently co-occurs with immune and depressive disorders, yet whether it reflects coordinated cross-domain multi-omic dysregulation remains unknown. Using UK Biobank data (19,484 baseline participants; 32,870 to 399,476 for 15.9-year follow-up), we identified MCP-related multi-omic signatures spanning 59 biochemical measures, 168 metabolites, and 2,920 proteins. Notably, these signatures showed graded dysregulation [controls < depression < rheumatoid arthritis (RA) < comorbidity] with increasing disease burden, were associated with increased risk of incident RA and depression, and partially mediated their bidirectional association. We further identified HNMT as a depression risk factor, FGF21 as an RA risk factor, MME as a depression protective factor, and platelet count/FGF21/HNMT as shared factors using Mendelian randomization. Beyond disease outcomes, the signatures were associated with brain structural impairment and health-related behaviors (smoking/fish intake/physical activity), and aligned with polygenic liability for immune–metabolic–psychiatric traits. Together, these findings demonstrate that MCP reflects coordinated neuro–immune–metabolic dysregulation underlying RA–depression comorbidity and functions as a systems-level phenotype linking immune processes and depression.

Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path toward enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here, we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a 4-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client substantially improves global accuracy for classifying multipartite entangled and nonstabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a 75% reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet.

The selective activation of H₂O₂ into hydroxyl radicals (•OH) through the synergistic interplay between the nanozyme’s internal orbital interactions and external orbital coupling with intermediates presents an important scientific challenge. In this study, we demonstrate that PdBi aerogels with controllable tensile strain first enhance the Pd d orbital energy level through p–d orbital hybridization, and subsequently, the hybridized Pd d orbitals couple sequentially with the molecular orbitals of H₂O₂, forming a cascade of orbital hybridizations. High-performance liquid chromatography quantitative analysis reveals that the engineered PdBi aerogels remarkably improve •OH selectivity from 73.3% to 95.4%, importantly boosting both the activation efficiency and selectivity. Experimental studies and theoretical calculations have demonstrated that the hybridization of the d orbitals of Pd and the p orbitals of Bi in PdBi aerogels initially raises the d-band energy level of the Pd atoms. During the activation of H₂O₂, the high-energy 4d_xz/yz and 4d_z² orbitals of Pd further hybridize with the 2π* and 5σ orbitals of H₂O₂, thereby enhancing the orbital interaction with H₂O₂ and creating optimal conditions for O–O bond cleavage and consequently enhancing the efficiency of •OH generation. Capitalizing on the differential reactivity patterns of various pesticides with PdBi aerogels, we develop a colorimetric sensor array for the discrimination and simultaneous detection of pesticide residues. Taking chlorpyrifos as an example, the detection limit is 0.23 μM, demonstrating good detection sensitivity. This work presents an orbital-level design strategy for creating highly selective nanozymes in H₂O₂ activation systems.

This narrative review summarizes recent advances in the integration of artificial intelligence (AI)-driven rehabilitation robotics with metabolic regulation in critically ill pulmonary patients. AI-enabled robotic systems, combining multimodal physiological sensing with adaptive machine learning, allow continuous monitoring of cardiopulmonary and metabolic parameters and support individualized, dynamic interventions. Unlike conventional rehabilitation based on fixed protocols, these systems establish closed-loop feedback between metabolic signals and motor output, enabling sustained low-intensity muscle activation while optimizing oxygen utilization, glucose metabolism, and mitochondrial function. Such regulation may interrupt the pathological interplay among inflammation, metabolic imbalance, and muscle atrophy, thereby promoting respiratory and systemic recovery. Recent developments in metabolic monitoring, biofeedback control, and multi-omics integration have further extended these platforms toward comprehensive metabolic management. By integrating biomechanical support with computational and biochemical intelligence, this approach reframes rehabilitation as an active process of metabolic reprogramming. However, current evidence remains heterogeneous, and well-designed clinical studies are needed to validate the reproducibility and clinical efficacy of these strategies.

Cell segmentation in imaging-based spatial transcriptomics (ST) relies on stained image segmentation or transcript clustering, but current methods are limited by dependence on manual annotations, sensitivity to staining quality and cell density, and lack of alignment with true transcriptomic profiles. We introduce RedeFISH, a reinforcement learning-based framework that formulates cell segmentation as a sequential strategic decision-making process. Through iterative optimization of the transcript assignment policy, RedeFISH aligns segmented cell expression profiles with single-cell expressions to achieve optimal segmentation. This approach enables staining-free, transcriptome-informed delineation of individual cells, thereby overcoming key limitations of existing methods. Benchmarking across multiple ST platforms shows that RedeFISH improves cosine similarity of segmented cell expression profiles by 9.7% on average and reduces root mean squared error (RMSE) by 13.9% compared with state-of-the-art methods. It also increases agreement between segmented and ground-truth cell regions by 19%, demonstrating improved accuracy and robustness. RedeFISH further imputes whole-transcriptome profiles from sparse ST measurements via spatially guided transfer from scRNA-seq data. The whole-transcriptome coverage enables unbiased and in situ prioritization of the critical regulators of spatial niches, e.g., mouse intestinal stem cell niches, and unlocks the possibility to delineate, with only one sample, the panoramic molecular and cellular changes along the whole developmental process from initiation to invasion for human cancer.

Aircraft serve as the intelligent extension of human capabilities into 3-dimensional space. Next-generation aircraft will transcend the limitations of traditional fixed aerodynamic configurations, evolving into embodied intelligent agents capable of sensing their environment, making autonomous decisions, acting collaboratively, and self-evolving—ultimately achieving Fly-by-Feel. Flexible sensing skins, with their unique advantages (ultrathin, lightweight, large-area conformal attachment to the aircraft surface, and adaptive regulation), are emerging as the key enabling technologies. Multi-aerodynamic parameters are acquired through in situ sensing, and artificial intelligence (AI)-enhanced decision-making is implemented via edge-deployed chips. Based on this sensing–algorithm collaborative bridge, closed-loop actuation is further realized to support functional implementation. This paper reviews the core composition of the “sensing–decision–regulation” closed-loop system, including aerodynamic disturbance-free in situ sensing and AI-enhanced super-resolution field perception, and demonstrates its applications in 3 key scenarios: anti-icing/deicing, electromagnetic stealth, and lift enhancement/drag reduction. Finally, it highlights the future prospects of programmable intelligent flexible skins, promoting the aircraft’s paradigm shift toward intelligence, efficiency, and safety.

Chiral structures have substantial potential for spin information utilization because of their distinctive spatial symmetry-breaking features and the consequent strong spin–charge correlation. One of the limitations in the development of chiral spintronics for both theoretical and practical applications is the limited range of chiral-related signal transmission, which is only tens of nanometers and is usually determined by the conductivity of the chiral materials and device applicability. In addition, the role of circularly polarized light, an important chiral parameter, in chiral signal transmission has been typically neglected. In this study, targeted chiral induction was conducted using the semiconductor polymer PCDTPT ([4-(4,4-dihexadecyl-4H-cyclopenta[1,2-b:5,4-b′]dithiophen-2-yl)-alt-[1,2,5]thiad-iazolo [3,4-c] pyridine]), which was further used to fabricate a field-effect transistor device. In a dark environment, the chiral-related signal could be transmitted up to 10 μm with an external magnetic field. Through the synergistic effects of spin-selective transition and chiral spin filtering under illumination, the chiral signal expression was fundamentally switched to another form, and the transmission distance was notably increased to the millimeter scale. In addition, owing to the flexible external field tunability (gate voltage, temperature, and polarized light) in multiple scenarios, the chiral signals reflected detailed dynamic changes. The achievement of long-distance chiral signal transmission and circularly polarized light-induced signal conversion and extension is expected to promote the development of chiral spintronics for both theoretical and practical applications.

The structural design of bone scaffolds determines the local fluid mechanical microenvironment; however, how such cues program stem cell metabolism to drive osteogenesis remains unclear. In this study, Voronoi-based trabecular-like scaffolds with tunable porosity were engineered to modulate fluid shear stress (FSS) while preserving a consistent topology. Computational fluid dynamics analyses confirmed that architectures with lower porosity generated higher FSS, enabling controlled investigation of mechano-metabolic coupling. Under dynamic culture conditions, bone marrow mesenchymal stem cells (BMSCs) cultured on high-FSS scaffolds exhibited enhanced osteogenic differentiation in vitro and promoted bone regeneration in vivo. Integrated transcriptomic, proteomic, and metabolomic analyses identified caveolin-1 (CAV1) as a prominent FSS-responsive membrane regulator. Mechanistically, CAV1 enhanced phosphatidylinositol 3-kinase (PI3K)–AKT signaling, stabilized hypoxia-inducible factor-1α (HIF-1α), and induced a glycolytic shift that supports the energetic and biosynthetic demands of osteogenesis. Pharmacological inhibition of PI3K, HIF-1α, or glycolysis abolished FSS-driven osteogenic responses, validating a CAV1-centered mechano-metabolic axis. These findings establish a direct link between scaffold microarchitecture and metabolic regulation of osteogenesis and provide design principles for mechanically instructive bone repair materials.

Developing advanced materials with simultaneously excellent wave transparency and efficient thermal insulation is critical for hypersonic vehicles. While rare earth disilicates (RE₂Si₂O₇) are promising candidates, their vast chemical space and complex polymorphism hinder precise property modulation. Herein, we establish an integrated high-throughput experimental and machine learning strategy to systematically investigate the composition–structure–property relationship of high-entropy RE₂Si₂O₇. The results demonstrate that the average RE³⁺ ionic radius determines the phase boundary. Notably, Sc incorporation jointly reduces both the dielectric constant and thermal conductivity. Specifically, the small size and strong electron localization of Sc minimize the polarizability, while its severe size and mass mismatch with other RE elements intensify phonon scattering. The model’s generalization is further validated by designing a series of high-entropy RE₂Si₂O₇ containing 5 to 9 distinct RE elements. Ultimately, the (Ho_1/5Tm_1/5Yb_1/5Lu_1/5Sc_1/5)₂Si₂O₇ high-entropy ceramic achieves a low dielectric constant (ε = 5.4) and a low thermal conductivity (κ = 1.3 W·m⁻¹·K⁻¹). This data-driven strategy provides a new pathway for the rational design of advanced high-entropy wave-transparent materials for extreme environments.

Wearable electronics serve as critical tools for human health monitoring and equipment operation assessment. A fundamental prerequisite for their stable performance is the ability to attach flexibly and reliably to the surfaces of human bodies or equipment. However, existing adhesion methods for wearable devices face substantial challenges in simultaneously achieving high-strength reversible bonding and environmental adaptability across diverse conditions, hindering the practical applications in complex and variable environments. Here, inspired by the excellent adhesion behaviors of geckos and octopuses, we designed a dual bioinspired adhesive microstructure consisting of an annular stalk, a microdome, and an annular tip. The large-area precise formation of this complex 3-dimensional microstructure is achieved via simple imprinting and photolithography. The fabricated adhesive demonstrated reversible and superior normal adhesion in complex environments including vacuum (120 kPa), dry (201 kPa), moist (173 kPa), and underwater (165 kPa). In situ observations of the contact splitting reveal that its extraordinary performance derives from the unique crack reentry phenomenon across various environments, which effectively inhibits crack propagation and improves the adhesion forces. Simulations further clarify the causes of crack nucleation and the underlying adhesion mechanisms. Demonstrations of the adhesive in skin-attachable electronics across multiple environments highlight its potential for applications in wearable electronics operating under diverse and complex conditions, promoting rapid development of wearable flexible electronics.

Malignant pleural mesothelioma (MPM) is a rare and highly aggressive malignancy arising from the pleural lining, characterized by a dismal prognosis and limited therapeutic options. Mesothelin (MSLN)-directed chimeric antigen receptor (CAR)-armored T (CAR-T) cell therapies have shown encouraging preliminary outcomes; however, autologous manufacturing approaches remain constrained by logistical complexity and prolonged production timelines, which are suboptimal for patients with rapidly advancing disease. Here, we describe the development of human allogeneic interleukin-15-augmented, MSLN-specific, CAR-armored invariant natural killer T (^Allo15MCAR-NKT) cells. These cells are generated through genetic modification of hematopoietic stem and progenitor cells, followed by a clinically guided CAR-NKT cell differentiation, maturation, and expansion process. This approach supports scalable production with high cellular yield, purity, and translational feasibility. Functionally, ^Allo15MCAR-NKT cells exhibit robust antitumor efficacy in vitro and demonstrate robust therapeutic activity across multiple in vivo MPM xenograft models, including subcutaneous and lung metastasis models. In addition, they actively modulate the tumor microenvironment by targeting CD1d⁺ tumor-associated macrophages. Phenotypic analysis reveals a rejuvenated cellular profile, marked by low expression of exhaustion-associated and inhibitory receptors, including PD-1, TIM-3, LAG-3, CTLA-4, and TIGIT, consistent with sustained functional capacity. Importantly, these cells display a favorable safety profile, with minimal evidence of graft-versus-host disease, cytokine release syndrome, brain infiltration or neurotoxicity, and no detectable off-tumor effects. Collectively, these findings support the development of a clinically translatable, off-the-shelf CAR-NKT cell therapy for the treatment of MPM.

Major histocompatibility complex class II (MHC-II) neoantigens play a critical role in immunotherapy, either as direct effectors or through their influence on CD8⁺ T cells. However, only a small fraction of tumor DNA mutations qualify as functional neoantigens, and current prediction tools suffer from limited accuracy, making the in vivo design of highly immunogenic neoantigens challenging. Here, we present EpiMII, a structure-aware graph neural network model for de novo design of MHC-II epitopes. By integrating 3-dimensional structural information through an inverse folding strategy, EpiMII generates mimotopes that preserve T cell specificity while enhancing MHC-II binding affinity and immunogenicity. The model was trained on a first-of-its-kind curated dataset of 142,934 homology-modeled MHC-II epitope structures, overcoming the scarcity of experimentally resolved MHC-II–peptide complexes. Benchmarking analyses illustrate that EpiMII achieves a sequence recovery rate of 66.7% on a held-out test set and 79.0% on crystallized epitopes, markedly outperforming existing tools like ProteinMPNN. In a hepatocellular carcinoma case study, all 5 EpiMII-designed epitopes could markedly activate CD4⁺ T cells in vitro and induce the secretion of interferon-γ and tumor necrosis factor-α. Notably, treatment with one epitope (P4) substantially reduced tumor volume in mice in vivo, comparable to a known positive control. These findings establish EpiMII as a powerful tool for structure-guided neoantigen discovery, with broad implications for cancer vaccine development and personalized immunotherapy.

The most common form of hair loss, androgenetic alopecia (AGA), is characterized by progressive follicular shrinkage. Current research efforts are focused on developing novel therapeutic techniques that exhibit better efficacy and safety profiles. In response to this challenge, biomedical technology has emerged as a multidisciplinary field and offered marked advantages in overcoming skin barriers, enabling controlled drug release, and modulating the perifollicular microenvironment. This review provides a comprehensive overview of advanced biomedical technologies for AGA treatment. We first outline the pathogenesis and introduce some conventional therapies. We then delve into the advantages and practical applications of emerging biomedical technologies, including novel pharmacological formulations, nanotechnology, stem cell-based approaches, and microneedle platforms. Subsequently, our focus shifts to the analysis of current therapeutic strategies in addressing core pathogenic factors through the latest and representative case studies. The review concludes by highlighting major challenges and future directions of these biomedical technology-based approaches, aiming to provide insights for the rational design of next-generation AGA therapies.

The efficacy of prostate-specific membrane antigen (PSMA)-targeted radioligand therapy in metastatic castration-resistant prostate cancer is limited by tumor heterogeneity and intrinsic radioresistance. This study investigates 2 natural flavonoids, genistein and baicalein, as potential radiosensitizers to improve the therapeutic effectiveness of ¹⁷⁷Lu-PSMA617 therapy. In vitro, combined treatment with low-toxicity concentrations (5 to 15 μM), based on cellular median inhibitory concentration (IC₅₀) of either flavonoid, dose-dependently enhanced the cytotoxicity of ¹⁷⁷Lu-PSMA617 against the human prostate cancer cell line LNCaP, reducing its IC₅₀ to 14.2% to 45.1% of that with monotherapy. All combination regimens demonstrated synergistic interaction, with combination indices below 0.82. Mechanistically, the combinations (particularly at 10 μM) induced a pro-oxidant shift (increasing reactive oxygen species by 78% to 144%), amplified DNA damage (γ-H2AX increased by 85% to 115%), and promoted apoptosis (caspase-3 activity increased by 325% to 389%) while suppressing prosurvival p-AKT. Transcriptomic profiling further revealed that both flavonoids induced a pro-apoptotic gene signature and markedly modulated multiple cancer-related pathways, particularly inhibiting key DNA double-strand break repair pathways such as nonhomologous end-joining. In vivo, combination therapies profoundly inhibited tumor growth (50.2% to 78.1% reduction versus monotherapy) and extended survival, with no overt systemic toxicity observed in this preliminary assessment. This work establishes genistein and baicalein as effective, multi-targeted radiosensitizers that enhance ¹⁷⁷Lu-PSMA617 efficacy by cooperatively amplifying DNA damage, inhibiting repair mechanisms, and activating the apoptotic cascade. Their natural origin and established safety profile underscore their translational potential. Thus, genistein and baicalein represent promising novel adjuvants for prostate cancer radioligand therapy.

Natural killer (NK) cells are innate cytotoxic lymphocytes capable of eliminating malignant cells independent of prior antigen sensitization, thereby constituting a critical first-line defense in tumor immunosurveillance. Within the tumor microenvironment (TME), metabolic dysregulation profoundly impairs NK cell effector function, and accumulating studies have investigated the regulatory effects of extracellular metabolites on NK cells. This review systematically delineates the direct mechanistic interplay between extracellular metabolites and NK cell antitumor immunity, with particular emphasis on their roles as structural components, bioenergetic substrates, metabolic reprogramming inducers, modification donors, gene transcription regulators, stress response mediators, intracellular pH modulators, and ligands engaging cognate receptors to initiate downstream signaling cascades. Furthermore, we discuss the therapeutic strategies targeting metabolites to potentiate NK cell functionality, encompassing modulation of metabolite availability within the TME and exploitation of metabolite-sensitive signaling axes. In addition, combination of metabolic interventions with other modalities, such as adoptive NK cell transfer and anti-programmed cell death protein 1/programmed death-ligand 1 therapy, is also evaluated for prospective applications. This review provides a conceptual framework for understanding the metabolic regulation of NK cells, highlighting emerging directions for advancing NK-cell-centered cancer immunotherapy through metabolic modulation.

Antioxidant nanozymes regulate reactive oxygen species homeostasis by mimicking the core catalytic functions of natural antioxidant enzymes, including superoxide dismutase-, catalase-, and glutathione peroxidase-like activities. The clinical translation of natural antioxidant enzymes has long been hampered by inherent limitations: short in vivo half-life, susceptibility to inactivation under physiological conditions, cumbersome purification processes, high production costs, non-negligible immunogenicity, and limited targeting capacity. In contrast, antioxidant nanozymes can overcome these bottlenecks with superior structural stability, tunable catalytic activity, low preparation cost, and flexible multifunctional modification. Guided by the catalytic mechanisms of natural enzymes, researchers have established rational design strategies for antioxidant nanozymes. To date, a diverse array of antioxidant nanozymes have been developed, with promising applications in multiple biomedical fields, including inflammatory diseases, ischemia–reperfusion injury, neurodegenerative disorders, and cancer adjuvant therapy. Notably, landmark clinical progress has been achieved: The catalytic nanocrystal suspension CNM-Au8, a therapeutic candidate for amyotrophic lateral sclerosis, has advanced to phase II clinical trials. This review systematically summarizes the core catalytic mechanisms of antioxidant nanozymes, clarifies the structure–activity relationships between rational material design and catalytic performance, reviews the latest advances in their biomedical applications, and dissects the key bottlenecks restricting preclinical research and clinical translation. It aims to provide rational design principles for researchers in this field, reduce empirical trial and error in material development, and provide guidance for the further optimization and clinical translation of antioxidant nanozymes.

Communications between organs contribute to propel physiological functions, as well as pathological processes. Growing evidence indicates that bone is continuously regulated by multiple layers of endocrine, immune, metabolic, and neural networks. The common bone diseases, including osteoporosis, osteoarthritis, rheumatoid arthritis, and intervertebral disc degeneration, are reported with tight associations to other nonbone organs. These organ–bone axes emphasize the interorgan communications that maintain the bone homeostasis and maintain individual health. In this review, we systematically summarize recent advances in the organ–bone communications, highlighting mechanistic innovations, such as the gut–bone–immune axis, neuro–immune–bone integration, hypothalamic–pituitary–adrenal axis, adipo–neuro–bone axis, and their connections with bone pathophysiology. Furthermore, we highlight circulating biomarkers and imaging advances reflecting organ–bone crosstalk. Finally, we discuss the clinical relevance, current research gaps, and unsolved questions of organ–bone communications in the diagnostic stratification, treatment selection, and multidisciplinary management of bone diseases.

The intractability of diabetic bone defects mainly results from derailed inflammation. While peripheral neuropathy is a common comorbidity, whether sensory dysfunction contributes to uncontrolled inflammation in diabetes is poorly understood. Here, within diabetic bone defects, we show that diminished sensory innervation is coupled with disrupted immune dynamics, characterized by both delayed neutrophil chemotaxis and abnormal neutrophil retention that resulted from impaired macrophage efferocytosis. Therefore, we design a chocolate chip cookie-like scaffold, in which the surface-embedded microspheres function as “chips” enabling burst interleukin-8 (IL-8) release, while the surrounding matrix provides sustained nerve growth factor release from silk fibroin matrix. Timely neutrophil chemotaxis induced by IL-8 triggers bone healing via stem cell recruitment, which is reinforced by sensory innervation by inducing neutrophil N2 polarization. Notably, macrophages preferentially established intimate physical proximity to outgrowing neurites to form a synapse-like structure, where they restore efferocytosis driven by neuronal Galectin-3. Moreover, spatiotemporally regulating neuroimmune circuit enhances mandibular bone regeneration in diabetic rats, highlighting the therapeutic potential of neuroimmune interaction in programming diabetic inflammation resolution.

Background: Asthma involves chronic inflammation linked to metabolic reprogramming, but how metabolites reshape epigenetics through posttranslational modifications remains unclear. Methods: We used house dust mite (HDM)-induced asthmatic mice with multiomics analyses (metabolomics, posttranslational modification-proteomics, and chromatin immunoprecipitation sequencing) and validated findings through gene editing and adeno-associated virus interventions. Results: Asthmatic airways showed lactate-driven glutaminolysis, causing lactate/succinate accumulation. Phosphoenolpyruvate carboxykinase 2 (PCK2) succinylation at K100 enhanced stability by antagonizing ubiquitination, creating a lactate-generating feedback loop. Accumulated lactate triggered polyglutamine-binding protein 1 (PQBP1) lactylation at K223, enabling protein arginine methyltransferase 5 (PRMT5)/WD repeat domain 77 complex inhibition. This erased H4R3me2s repressive marks from proinflammatory gene promoters, particularly mitogen-activated protein kinase pathway genes, causing transcriptional derepression. Airway epithelium-specific Pqbp1 knockout reduced inflammation, goblet cell hyperplasia, and T helper 2 responses. Pck2-short hairpin RNA or oxamate treatment ameliorated asthmatic pathology. Conclusion: We identified a PCK2–lactate–PQBP1–PRMT5 axis linking metabolic reprogramming to epigenetic dysregulation in asthma. PCK2-K100 succinylation drives lactate accumulation, inducing PQBP1-K223 lactylation that inhibits PRMT5 and activates inflammatory genes, representing a therapeutic target for asthma.

Vehicle–road wear microplastics (VRWMPs) are microplastic-sized polymer-containing particles generated from the vehicle–road system, including tire–road wear particles (TRWPs) as the dominant composite class and other polymer-bearing wear debris (e.g., road marking wear). Their complex chemical composition and wide particle size distribution pose potential ecological risks. However, their impacts on ecosystems have long been overlooked because overlapping terminology (e.g., TRWP versus broader non-exhaust emissions) and nonstandardized characterization methods hinder cross-study comparability, while a tire-centered research focus and limited field monitoring obscure the contribution of pavement materials and realistic exposure scenarios. Existing studies largely emphasize tire-derived contributions, while the role of pavement materials remains underrepresented, resulting in an incomplete understanding of VRWMP formation mechanisms. In addition, limited long-term and systematic monitoring data constrain current knowledge of VRWMP migration, transformation, and environmental risks. From a road engineering perspective, this review synthesizes the full lifecycle of VRWMP, from generation to environmental fate. It focuses on formation mechanisms, preparation and characterization methods, migration, and transformation processes within roadway systems, and associated ecological and human health effects. Evidence indicates that VRWMP generation is jointly controlled by tire characteristics and pavement materials, yet a standardized characterization framework is still lacking. The migration and transformation of VRWMP are difficult to model due to data scarcity and pronounced regional variability related to geography, climate, and traffic conditions. Soil and aquatic environments represent major sinks, and exposure pathways such as inhalation may induce adverse biological effects, including oxidative stress and DNA damage. With the increasing complexity of pavement materials, establishing full-chain control of VRWMP, from generation to environmental fate, is becoming an urgent research priority. This review provides a scientific basis for advancing cleaner and more sustainable transportation infrastructure.

Real-time moisture sensing is crucial for environmental monitoring and food safety. However, conventional humidity sensors relying on moisture-induced changes in conductivity or capacitance are often constrained by sluggish water adsorption–desorption kinetics and inefficient ion transport through disordered nanochannels. Here, we develop cellulose aerogel fibers derived from natural rattan, featuring hierarchical and interconnected porosity, to enable ultrafast hydrovoltaic moisture sensing. The tailored pore network expedites the diffusion of trace moisture and substantially amplifies the solid–liquid interfacial area within the fiber, thereby boosting the formation of electric double layers and enhancing water-ion coupling. Consequently, the self-powered sensor delivers a detectable voltage response within 70 ms upon exposure to only 10 μl of water microdroplets, achieving both rapid detection and high sensitivity. Moreover, the device exhibits versatile responsiveness to multiple environmental parameters, and its sensing behavior can be readily tuned by manipulating the fiber configuration (linear, helical, and branched). All 3 configurations achieve a fast response to approximately 0.1 V within 70 to 310 ms, followed by a voltage increase to 0.49 to 0.63 V within 3 to 5 s. This work highlights the critical role of porosity engineering in cellulose aerogel fibers and provides a new strategy for developing high-performance biomass-based hydrovoltaic sensing systems.

The clinical utility of nanobodies in solid tumor therapy is constrained by a fundamental biophysical trade-off: rapid renal clearance necessitates half-life extension, which in turn demands ultrahigh affinity to prevent dissociation from the target under systemic washout conditions. While generative artificial intelligence has substantially advanced structure prediction, it often fails to resolve the subtle energetic frustrations at protein–protein interfaces required for affinity maturation. Here, we present a physics-informed artificial intelligence framework that integrates AlphaFold 3 structural priors with molecular dynamics simulations to rationally design a picomolar anti-carcinoembryonic antigen nanobody. By employing variable dielectric molecular mechanics/generalized Born surface area decomposition, we identified interfacial residues that were structurally permissible but thermodynamically suboptimal. We subsequently constructed a focused library to resolve these bottlenecks through electrostatic optimization, desolvation penalty minimization, and van der Waals packing refinement. This strategy achieved a 99% binding positivity rate and yielded variants with picomolar affinity (K_D ≈ 44 pM)—an ~306-fold improvement over the parental clone—without compromising thermal stability (T_m > 63 °C). To translate these biophysical gains into therapeutic efficacy, we engineered bispecific nanobodies fusing the affinity-matured domains with an anti-human serum albumin binder. In vivo longitudinal imaging of colorectal cancer xenografts revealed a “lock-and-hold” phenotype, characterized by deep intratumoral penetration and sustained retention (>168 h). This work demonstrates that coupling geometric deep learning with rigorous physical principles overcomes the inefficiencies of stochastic screening, providing a valuable framework that may be adapted for the rational development of high-potency biologics across various therapeutic targets.

The aging of the global population exacerbates the burden of age-related diseases; however, therapies that can intervene in fundamental aging processes are lacking. Senescent cells drive chronic inflammation and multitissue dysfunction through the secretion of proinflammatory and profibrotic senescence-associated secretory phenotype cells, making them emerging therapeutic targets. Although first-generation senolytic drugs have entered clinical trials, they face limitations such as insufficient targeting specificity and transient efficacy. The success of chimeric antigen receptor T cell (CAR T cell) therapy in cancer immunotherapy has validated its precision clearance capabilities as a “living drug”. This review systematically elaborates on the paradigm shift of extending CAR T cell therapy to aging medicine, from the discovery and validation of surface targets on senescent cells to a CAR engineering design tailored to the senescent microenvironment and from evidence of reversing fibrosis and improving metabolic function in preclinical models to the challenges of specificity, safety, and manufacturing faced in clinical translation. Finally, future directions for integrating technologies such as mRNA delivery and artificial intelligence are envisioned in this article, which proposes that CAR T cell therapy may drive the evolution of medicine from “treating single diseases” to “intervening in shared aging processes”, offering transformative strategies to achieve healthy aging.

Nanobody–antigen molecular recognition underpins nanobody discovery and development, necessitating accurate determination of binding occurrence, interface residues, and affinity. Current predictors are architecturally designed for the massive, heterogeneous spectrum of general protein–protein interactions, diluting the limited, complementarity-determining region (CDR)-dominated nanobody–antigen interaction (NAI) data and masking the decisive CDR signal. The scarcity of experimental affinity data precludes direct regression-based estimation of binding affinity. Here, we present NanoBind, a mechanism-driven deep learning framework that embeds the CDR-dominated binding pattern within its encoder, enabling robust prediction of binding occurrence and interface residues from limited NAI data. Constrained by scarce affinity data, NanoBind generates quantitative affinity ranges for nanobody–antigen pairs without extra experiments. Systematic benchmarking demonstrates that NanoBind surpasses state-of-the-art methods in accuracy and robustness, and interpretability analyses confirm that the model’s decisions align with the CDR-dominated binding mechanism. When million-sequence immune repertoires are screened against 4 antigens, NanoBind reduces candidate nanobodies to fewer than 100 per target. For the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD)–nanobody F2 complex, NanoBind correctly predicts binding occurrence, matches experimentally validated interface residues, and generates an affinity range quantitatively supported by molecular dynamics simulations. A server is available at http://liulab.top/NanoBind/server.

The continuous development of bright x/gamma-ray sources has opened up new frontiers of science and advanced applications. Currently, there is still a lack of efficient approaches to produce high-energy gamma-rays with peak brilliance comparable to modern free-electron lasers. Here, we report a novel mechanism called beam fast pinching radiation burst to generate such gamma-ray sources. It is achieved by injecting a giga-electron volt electron beam into a submillimeter plasma with an upramp density profile, enabling violent beam pinching to occur rapidly. During this process, a burst of collimated gamma-rays is efficiently produced with photon energies ranging from mega-electron volts to giga-electron volts, electron-to-photon energy conversion efficiency above 20%, and peak brilliance exceeding 10²⁸ photons s⁻¹ mm⁻² mrad⁻² per 0.1% bandwidth. All of these are several orders of magnitude higher than existing gamma-ray sources. This opens a novel avenue for the development of extremely bright gamma-ray sources for both fundamental research and cutting-edge applications.

Persistent androgen receptor (AR) signaling, metabolic alterations, and changes in the immune microenvironment are contributing factors to the progression of prostate cancer (PCa); however, therapeutic strategies capable of simultaneously targeting these interconnected vulnerabilities remain limited. Here, we identify a combinatorial regimen consisting of dihydroartemisinin and eupatilin (D&E) as a suppressor of PCa progression through coordinated disruption of tumor-intrinsic survival signaling and protumor immune interactions. Functionally, D&E suppressed the proliferation of PCa cells. Mechanistically, D&E induces ferroptosis in PCa cells, as evidenced by lipid peroxidation, intracellular iron accumulation, malondialdehyde elevation, and glutathione reduction. We further identified AR as an important target of D&E and found that it promotes resistance to ferroptosis by sustaining the SLC7A11 antioxidant axis through transcriptional activation of SLC7A11. Upstream, D&E inhibited the NF-κB subunit p65, which transcriptionally maintains AR expression, thus establishing an NF-κB/AR/SLC7A11 signaling cascade underlying ferroptosis resistance in PCa. Beyond tumor-intrinsic effects, D&E also remodels the tumor microenvironment. Specifically, AR-high tumor cells were found to facilitate M2-like macrophage polarization. In turn, M2-like macrophages could secrete SPP1, which potentially augments malignant properties and AR signaling in tumor cells via the CD44 pathway, thereby constituting a protumorigenic positive feedback loop. Notably, D&E attenuated the M2-like phenotype, reduced SPP1 secretion, and reduced macrophage-mediated promotion of PCa proliferation. In immunocompetent mouse models, D&E further synergized with anti-PD-L1 to suppress PCa progression. Together, these findings indicate that D&E exert antitumor effects in PCa by promoting ferroptosis-associated cell death and modulating macrophage-associated microenvironmental signaling, with potential therapeutic relevance particularly in AR-positive PCa.

Computational pathology enables scalable analysis of pathology images for cancer diagnosis and research, but conventional deep learning models remain constrained by annotation dependence and task-specific development. Recent advances in self-supervised learning, transformer-based architectures, and large-scale pretraining have given rise to computational pathology foundation models (CPathFMs), which are designed to learn transferable and reusable representations from diverse pathology data. This review first traces the development of CPathFMs by examining the data resources, backbone architectures, representation levels, input modalities, and pretraining strategies that shape their design. We then examine their applications across major cancer pathology tasks, including tumor detection, grading, and subtyping, molecular biomarker and gene expression prediction, prognostic assessment, tissue phenotyping, as well as multimodal retrieval and report generation. We further discuss key challenges that limit translation and deployment, including underrepresentation of normal tissues and benign lesions, limited generalization across domains and institutions, data overlap and benchmark contamination, task-centric evaluation and insufficiently standardized metrics, high training and infrastructure costs, and ethical concerns in model governance. Overall, CPathFMs mark an important shift from task-specific pathology artificial intelligence toward reusable representation learning. Their clinical value remains to be established through improved model performance and generalizability, more comprehensive and standardized evaluation frameworks, and prospective evidence of utility in real-world pathology workflows.

Fe single-atom catalysts with well-defined porous architectures and optimized Fe–N_x microenvironments show great potential for enhancing the oxygen reduction reaction. Herein, we report Fe–N₅ single-atom catalysts, where each Fe atom coordinates with 4 pyridinic N and 1 axial pyrrolic N, for efficient oxygen reduction. A dual-confinement strategy, combining the wood framework with Fe³⁺ coordination, directs self-assembly of cellulose nanocrystals (CNCs) into a porous wood-derived architecture. Subsequent pyrolysis yields Fe–N₅ catalysts anchored on N, S-codoped carbon with hollow, hierarchically interconnected 3-dimensional pores. Notably, coordination between CNCs and Fe³⁺ guides the formation of Fe–N_x moieties within a tailored microenvironment, enabling control over the coordination number, heteroatom doping, and the electronic structure. X-ray absorption spectroscopy and density functional theory calculations reveal that FeN₅ moieties are optimized through 3 synergistic factors: Fe coordination with 4 pyridinic N and 1 axial pyrrolic N, S doping from residual sulfate ester groups in CNCs, and adjacent micropores. Collectively, these effects lower the *OH desorption barrier, accelerating the adsorption/desorption of oxygenated intermediates. Consequently, Fe–N₅ single-atom catalysts exhibit an exceptional oxygen reduction reaction activity with a half-wave potential of 0.964 V. This dual-confinement strategy enables high-performance non-precious-metal catalysts for metal–air batteries, as evidenced by Fe–N₅-based zinc–air batteries outperforming Pt/C.

Chiral spin textures in magnetic insulators promise magneto-electric (ME) spintronics with orders-of-magnitude lower power consumption than metallic systems. However, realizing the short magnetic periods required for high-density device integration remains difficult, as conventional Dzyaloshinskii–Moriya interaction (DMI)-based mechanisms typically constrain spiral periods to tens of nanometers. While theory predicts that strong single-ion anisotropy (SIA) on frustrated lattices can stabilize complex non-coplanar textures, the potential for using this mechanism to engineer such compact textures remains largely unexplored. Here, we report that a cubic chiral insulator Co₅TeO₈ provides an experimental example of this paradigm. Comprehensive neutron scattering and magnetometry reveal helimagnetic spirals with continuously tunable pitch of 5.7 to 10 nm embedded in a complex phase diagram spanning 8 distinct phases. Capacitance anomalies throughout the phase diagram indicate ME coupling, pointing to the possibility of future E-field control of these textures. The temperature and field dependence of the helical wavevector strongly support a scenario in which site-dependent SIA provides the leading contribution to the selection of the helical period from a frustration-induced degenerate manifold. Consistent with this interpretation, ab initio calculations place SIA approximately an order of magnitude above DMI, distinct from conventional helimagnets. Co₅TeO₈ thus offers an experimental realization of sub-10-nm helimagnetism and motivates a design principle for anisotropy-engineered correlated insulators.

Extensive data resources are critical for broadening the application scope and improving the performance of large language models (LLMs). Local deployment of LLMs enables users to leverage the advanced capabilities of LLMs while maintaining secure access to local data. Therefore, local LLMs deliver more domain-specific services than general LLMs. However, concerns over data security and the absence of economic incentives restrict cross-industry collaborative utilization of data resources. To address this challenge, an interdisciplinary framework integrating artificial intelligence, economic theory, and data security technologies is required to explore the data trading mechanism for local LLMs. The key aspects of a data market framework, economic evaluation for data pricing, and trusted data trading are analyzed to support efficient utilization of cross-industry data resources. By constructing a data trading mechanism, industry-specific local LLMs can be further enhanced through cross-industry knowledge integration, thereby improving productivity across sectors.

Patients with diabetic kidney disease (DKD) at chronic kidney disease (CKD) stages 3 to 4 are at high risk for rapid renal function decline within 1 year. However, owing to the multifactorial complexity of the disease, effective prognostic tools that integrate multidimensional clinical and pathological information are currently lacking for this specific population. We conducted a retrospective cohort study involving 322 patients with biopsy-proven DKD (CKD stages 3 to 4) from the China-Japan Friendship Hospital and Hebei University Affiliated Hospital. Their clinical data and 2,576 renal biopsy pathology images were used to develop and validate a multimodal model. Machine learning was applied to integrate clinical composite indices and renal biopsy images to develop a prognostic prediction tool. Four key clinical predictors were identified: estimated glomerular filtration rate, 24-h urinary protein, systemic immune inflammation index, and estimated pulse wave velocity. Among the 6 machine learning algorithms used to develop the prediction models, the random forest algorithm achieved the best performance in the test set, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 and a precision–recall AUC (PR-AUC) of 0.921 for predicting the 1-year composite renal endpoint. The integration of pathological features led to a marked improvement in the performance of the model (ROC-AUC: 0.923 vs. 0.898). External validation demonstrated that incorporating pathological information into the model increased the ROC-AUC from 0.885—achieved when clinical composite indices alone were used as predictors—to 0.930. In this study, machine learning-based automated image analysis of glomerular crescent-shaped changes and renal interstitial fibrosis was integrated with established clinical composite indices to construct an accurate model for predicting short-term renal prognosis of DKD at CKD stages 3 to 4 and to provide a potential tool for improved risk stratification.

Pulmonary fibrosis (PF) is a fatal interstitial lung disease characterized by excessive extracellular matrix deposition and irreversible architectural distortion. The mechanisms driving the transition from tissue repair to fibrosis are complex and remain poorly understood. By analyzing interpatient variation across 75 idiopathic PF lungs, we identified a conserved profibrotic macrophage subset, distinct from canonical M1/M2 or SPP1⁺ states, characterized by high legumain (LGMN) expression and enrichment of gene signatures implicated in leukocyte activation and matrix remodeling. LGMN⁺ macrophages localize within fibroblastic foci and are associated with disease progression and poor prognosis. Lineage-tracing and RNA velocity analyses revealed that LGMN⁺ macrophages arise from monocytes through fibroblast-derived macrophage colony-stimulating factor signaling, which activates Maf BZIP transcription factor B-dependent differentiation programs. Pharmacological inhibition or macrophage-specific deletion of Lgmn markedly attenuated bleomycin-induced lung fibrosis, reduced extracellular matrix accumulation, and improved lung architecture. LGMN activates cathepsin S to mediate degradation of basement membrane collagen IV, thereby disrupting the alveolar–capillary barrier. In parallel, secreted LGMN acts as a paracrine signal to activate fibroblasts and promote collagen I deposition, collectively fostering a profibrotic niche. Together, these findings establish LGMN as a macrophage effector that links immune activation to matrix remodeling, thereby driving the transition from tissue injury to fibrosis.

Monitoring adoptive cell therapy in solid tumors is critical for evaluating treatment efficacy and guiding clinical medication but is also hindered by poor sensitivity, high background signals, and disruptions of therapeutic functions in existing techniques. In this study, a membrane-mimicking conjugated oligoelectrolyte with second near-infrared (NIR-II) fluorescence, conjugated oligoelectrolytes-benzobisthiadiazole (COE-BBT), is applied for the first time to label and track chimeric antigen receptor (CAR)-engineered natural killer (CAR-NK) cells and T (CAR-T) cells in vivo. COE-BBT stably embeds in lipid bilayers through combined electrostatic and hydrophobic interactions, resists membrane crossing, and supports long-lasting labeling with a lighting-up property. The optimized labeling approach achieves high sensitivity, enabling the detection of as few as ~20 labeled cells in vitro and ~50 cells in vivo under NIR-II imaging. In orthotopic and subcutaneous glioma models, NIR-II fluorescence imaging enables continuous tracking of CAR-NK and CAR-T cell proliferation, migration, tumor homing, and blood–brain barrier penetration for up to 14 d posttransfer as the fluorescence signal is enhanced during proliferation, without compromising cell viability or cytotoxic function. The COE-BBT probe also exhibits favorable biosafety, underscoring its translational potential as a robust imaging strategy to improve solid tumor adoptive cell therapy monitoring and clinical guidance of therapeutic dosing.

Constrained combinatorial optimization (CCO) problems are prevalent across various fields and represent key challenges in computational science and engineering. Although numerous classical and quantum algorithms have been proposed to tackle these problems, substantial limitations still persist. Classical algorithms exhibit exponential computational complexity growth with scale and persistent vulnerability to local minima traps. Quantum computing, while offering theoretical advantages through global superposition, faces practical barriers such as short decoherence times and current hardware limitations. To address these challenges, we propose a quantum-inspired fast algorithm for solving CCO problems. Our approach enhances global search capability with superposition encoding and avoids constraint-induced local minima​​ via a project–feedback strategy. Particularly, our method aligns with mature electronics manufacturing and demonstrates a proof-of-concept implementation in classical systems, indicating high-efficiency potential for solving constrained optimization problems.