RNA therapeutics provide a revolutionary means of treating a variety of diseases by precisely regulating gene expression and protein synthesis, with great medical significance. However, there are three key challenges to its clinical application: the inherent instability of RNA, the need for controlled regulation of RNA function, and the lack of an efficient delivery system. Computational strategies provide complementary tools for analyzing and optimizing RNA sequence, structure, function, and delivery, accelerating the rational design of RNAs and the optimization of delivery systems. This review systematically introduces two core advances in the field of RNA therapy: (1) the design and optimization of RNAs based on predictive modeling and algorithmic screening; (2) the intelligent transformation of the delivery system through data-driven methods. Based on these developments, we discuss three future directions for computational RNA molecular design across the dimensions of design algorithms, design mechanisms, and design architectures. This review not only summarizes computational approaches developed to address key challenges in RNA therapeutics, but also highlights opportunities for integrated RNA-delivery co-design. These advances may provide useful insights for the development of next-generation precision therapeutics and their future clinical translation.

Circular mRNAs (circRNAs) have shown broad biological application prospects due to their unique closed structure and stability features. Among the methods for producing circRNAs, employing T4 ligases has been successfully developed as a straightforward approach. Expanding RNA research would benefit from the development of more RNA ligases. In this study, RNA ligases from different sources were selected and explored. By comparing the protein expression levels of seven newly constructed RNA ligases and the intracellular translation levels of their circRNA ligation products, the ligases from Naegleria gruberi, Methanothermobacter thermautotrophicus, and Rhodothermus marinus demonstrated better efficiency compared to T4 RNA ligase. Mouse studies further validated the functions of circRNA ligation products. This result can offer valuable guidance for synthesizing and applying circRNAs, enabling them to serve various functions in vaccines, protein replacement therapy, and gene editing.

Recent advances in bottom-up synthetic biology have significantly expanded the ability to construct artificial life systems. While most efforts focus on building protocells, many biomimetic functions arise only when multiple units operate collectively. Prototissues, formed from interconnected protocell assemblies, provide a platform for such emergent behaviors and offer broad potential in biomedicine, biosensing, and smart materials. This review introduces a dual-dimensional framework for understanding prototissue design. The first dimension examines inter-protocell adhesion strategies that define molecular connectivity, and the second examines spatial programming approaches that organize protocells into functional architectures. On this basis, the review summarizes key collective behaviors enabled by these design principles and highlights how advances in materials chemistry, synthetic biology, and advanced manufacturing support the development of increasingly adaptive and functional prototissues. Major challenges remain, including achieving dynamic and selective adhesion, scaling spatial architectures while maintaining resolution, improving signal transport, and enhancing biological integration. The review outlines potential pathways to address these issues and to guide the development of prototissues with more sophisticated, life-like properties. Overall, the conceptual framework and insights presented here provide a foundation for the rational design of next-generation prototissues and advance bottom-up synthetic biology toward more complex artificial life systems.

Space exploration and manufacturing are of critical importance for scientific advancement, technological innovation, national security, and the acquisition of extraterrestrial resources. In view of this, chemical and biological nano-/micro-/meso-scale manufacturing provide complementary approaches to overcome key space exploration challenges by enabling the in-situ production of essential life-support materials, propellants, and other resources. This review examines the origin and historical evolution of space manufacturing and the latest advances across different environments—from orbital space stations and the lunar surface to Mars and asteroids. It is structured to present the current state of research, outline key manufacturing strategies and technologies, assess the technical and environmental challenges, and discuss emerging trends and future directions. Besides, the potential applications of emerging technologies such as synthetic biology and artificial intelligence in overcoming the limitations of microgravity, limited resources, and extreme conditions are discussed. Ultimately, this integrative review could serve to guide future development, from advancing space science and disruptive manufacturing to enabling interdisciplinary and application-level innovations.

As global water scarcity and security challenges intensify, traditional water treatment materials struggle to meet practical demands due to their limited efficiency and single-functionality. Smart materials—particularly those with stimulus-responsive and adaptive properties—have emerged as promising alternatives, thanks to their dynamic functional regulation, environmental adaptability, and multifunctional integration capabilities. This review systematically examines the principles of smart materials from micro to macro scales, covering environmental sensing and output mechanisms, including pH, temperature, light, and magnetic responses, as well as self-cleaning and self-healing functions. It critically compares their performance in pollution remediation, resource recovery, and energy capture. The study highlights inherent limitations in current materials, such as reduced responsiveness in complex aquatic environments, insufficient long-term stability, functional conflicts, and scalability challenges. Furthermore, it explores forward-looking strategies to address these issues, including orthogonal multi-stimulus response design, performance validation under real-world conditions, environmentally friendly material alternatives, and integration with real-time sensing and artificial intelligence technologies. By evaluating material performance and summarizing future development directions, this review provides a systematic theoretical framework and practical guidance for developing next-generation smart water treatment systems that prioritize sustainable water security, resource recycling, and energy synergy.

Based on the self-assembling properties of the SpyCatcher/SpyTag system and the structural advantages of Dps protein, this study successfully constructed a three-dimensional nano-enzyme cascade reactor (3DNECR) through the covalent self-assembly of SpyTag-ADH and SpyCatcher-Dps-ATA117 fusion proteins. The 3DNECR exhibited significantly enhanced catalytic efficiency compared to the two-dimensional control, attributed to optimized spatial organization promoting substrate channeling. The reactor exhibited remarkable storage, pH, and thermal stability. It maintained over 80

Light-driven soft robots have enabled untethered locomotion and stimulus-responsive functions, yet most reported systems remain dominated by a single behavior, with limited actuation orthogonality, weak system reconfigurability, and insufficient task-level integration on complex water surfaces. These limitations are particularly restrictive in confined aquatic environments, where floating solids, oil films, and dissolved pollutants frequently coexist. Here, we report a centimeter-scale floating soft robot that addresses this challenge through wavelength-selective modular integration. The system combines UV-responsive LCE-PDMS actuators for reversible gripping, NIR-responsive GTC/PDMS for Marangoni propulsion and oil adsorption, and xenonresponsive TiO2/PAM hydrogel for photocatalytic degradation, enabling decoupled optical control of locomotion, manipulation, and chemical treatment within one untethered platform. The robot can be reconfigured into task-oriented architectures for oil adsorption, solid capture, dye degradation, and integrated remediation. The GTC/PDMS module drives interfacial motion at speeds up to 3.3 cm center dot s-1 and provides an oil-loading capacity of 0.13 g center dot g- 1, the LCE-PDMS actuator achieves reliable gripping within 20 s across 6-27 mm targets, and the TiO2/ PAM module reaches 91% methylene blue degradation within 2.5 h. Beyond ideal conditions, the integrated robot retains multimodal remediation capability in saline, alkaline, turbid, and vortex-disturbed environments. This work establishes a system-level strategy for combining orthogonal light actuation, modular reconfiguration, and multimodal remediation in a single floating soft robotic platform for adaptive water-surface cleaning.

Building a living cell from scratch requires overcoming a bottleneck that has remained unresolved despite decades of progress: orchestrating the spatiotemporal integration of core functional modules. To tackle this barrier, the SynCell Asia Initiative outlines a strategy for developing core functional modules followed by their systems-level integration through the establishment of a centralized, artificial intelligence (AI)-driven biofoundry.

The eukaryotic cell-free protein synthesis (CFPS) system, endowed with intrinsic post-translational modification capabilities and a complex molecular chaperone network, efficiently synthesizes functional proteins with correct conformation and biological activity. This effectively compensates for the structural limitations of prokaryotic systems in expressing complex eukaryotic proteins. This paper aims to comprehensively review and analyze the latest advances in the field of eukaryotic CFPS from a systems engineering perspective. The paper delves into the diversification of host chassis, rational design of core reaction components, and the pivotal role of novel biomaterial integration and high-throughput reaction equipment development in system reconfiguration. At the application level, it summarizes the platform's latest achievements, including elucidation of fundamental mechanisms, complex protein engineering, and metabolic synthesis. It particularly highlights its potential in emerging areas such as the construction of artificial cells, the development of bioelectronic interfaces, and the design of microarray chips. Furthermore, addressing the current standardization and cost bottlenecks hindering industrialization, this paper proposes a solution strategy based on artificial intelligence and synthetic biology tools, aligning with the shift from empirical trial-and-error to rational design paradigms. By integrating the current technological landscape with emerging trends, this review aims to provide theoretical references and practical guidance for constructing an economical, high-throughput eukaryotic cell-free biomanufacturing platform.

Soft robots based on optically responsive smart materials have attracted extensive research interest for their unique capabilities. However, achieving adaptive, multifunctional mode switching remains challenging. Inspired by wrist rotation, considering the high response speed, miniaturization, and discrete programmability of magnetic actuators, we designed magnetic joints with different magnetization profiles. An assembly method was further proposed, utilizing magnetic actuator materials as joints and optical actuator materials as the skeleton. This approach enables functional synergy while realizing actuation decoupling. Through this functional allocation, the optical skeleton focuses on functional execution, while the magnetic joints concentrate on multimodal adjustment, thereby designing complex and hybrid driving behaviors. This endows soft robots with enhanced maneuverability through multimodal switching capabilities, demonstrating excellent adaptability across diverse operational environments. This approach can provide solutions for the future expansion of soft robot application scenarios and their integration with other functional devices.

Electronic devices that harness the interaction between electrical energy and the environment have become powerful tools with applications across various scenarios, from living systems to external settings, including biomedical implants, wearables, environmental sensing, and catalysis. As demand for diverse environments and complex tasks grows, electronic devices are increasingly required to possess adaptability and learning capabilities, thereby achieving intelligence. However, traditional silicon-based integrated circuit rigid electronic devices exhibit limitations, primarily in their inability to adapt to miniaturization and dynamic environments, as well as to support centralized information processing for large-scale data processing. To address these issues, integrating material intelligence into the architecture of traditional electronic devices has emerged as a new trend, enabling distributed intelligence across each module to enhance overall intelligent performance. This review examines how intelligent matter integrates embodied intelligence into various modular designs through its intrinsic physicochemical properties. Moving beyond a redundant layout of traditional performance metrics, this work provides a deep analysis of how intelligent matter empowers electronic devices with advanced “bio-like” behaviors, including multimodality, proactive environmental adaptation, and learning capabilities. Subsequently, we discuss existing challenges in this field and outline future development directions. Our aim is to provide design principles and technical pathways for the deep integration of intelligent materials and electronic systems, laying the foundation for advancing next-generation intelligent devices in applications such as bionic sensing, adaptive control, and environmental interaction.

Antibiotic resistance, bacterial biofilms, and resulting dysregulated immune responses pose major challenges in the management of bacterial infections, often leading to severe complications such as sepsis. This study introduces a multifunctional, pH-responsive nanocarrier based on self-assembled micelles composed of poly(ethylene glycol)-poly(β-amino ester) (PEG-PAE) and the antibiotic azithromycin (Azi). The prepared Azi-PEG-PAE micelles exhibited a negative charge under physiological conditions, which converted to positive charge in acidic environments. In acidic biofilm environments, these micelles effectively targeted negatively charged bacteria and released azithromycin, achieving enhanced biofilm eradication compared to free azithromycin. Furthermore, the micelles bound pathogen-associated molecular patterns (PAMPs), thereby mitigating excessive inflammation, reducing sepsis incidence and increasing survival in a murine peritonitis model. The desorbed PAMPs from the micelles subsequently triggered macrophages toward the M1 phenotype, further promoting bacterial clearance. This dual antibacterial and immunomodulatory strategy demonstrates a promising approach to overcome the challenges of antibiotic-resistant infections and immune dysregulation in sepsis. STATEMENT OF SIGNIFICANCE: Severe bacterial infections and dysregulated immune responses trigger sepsis. We introduce a multifunctional, pH-responsive micellar system that enhances azithromycin delivery within acidic biofilm environments while concurrently binding pathogen-associated molecular patterns (PAMPs). By integrating targeted antimicrobial activity with immunomodulation, these micelles significantly reduce sepsis incidence and improve survival in a murine peritonitis model, offering a promising therapeutic strategy for treating bacterial sepsis.

The escalating atmospheric CO2 concentration calls for advanced carbon capture solutions. However, developing a stable and efficient adsorbent with high selectivity for CO2 capture remains a significant challenge. Microporous materials capture CO2 primarily through physisorption based on pore structure and van der Waals forces. In this study, three silicon-aluminum zeolite molecular sieve materials were synthesized and hydrophilic modification of ZIF-8 was achieved through post-synthetic coordination with polyethylene glycol. A systematic comparison of the CO2 adsorption performance of these materials under different conditions was then conducted. Zeolites exhibited exceptional thermal stability (<5% mass loss from 25 to 800 degrees C under an air atmosphere) and cyclability (retaining >95% of initial adsorption capacity throughout cycling), outperforming ZIF-8 analogues. 85-nm zeolite achieved the optimal CO2 uptake (1.8 mmol g(-1)) at 25 degrees C and ZIF-8 showed the best humidity resistance (40% decrease at 80% RH). Additionally, ZSM-5 exhibited enhanced gas retention of 1498 s, indicating superior CO2 adsorption affinity. These results demonstrate the excellent thermal stability and cycling stability of the zeolite materials. This study provides actionable insights for future material design: 1) prioritizing zeolite-based frameworks for scenarios requiring long-term cyclic stability and thermal robustness; 2) optimizing pore size and surface properties to balance adsorption capacity and selectivity; 3) developing anti-poisoning modifications targeting SO2 competitive adsorption in flue gas.

Reoccluding the exposed dentinal tubules is crucial for treating dentin hypersensitivity (DH). However, the narrow structure and high-density negative charges of dentinal tubules impede the penetration of filling repair materials to effectively achieve deep-induced mineralization for DH treatment. Herein, a morphologically controllable Fe3O4@SiO2 magnetic Janus nanomotors (MJNs) were synthesized via a seeded emulsion-mediated surface growth strategy. The as-achieved MJNs exhibit magnetic response, with a head diameter of about 200 nm, and snake-like morphology with a tunable tail length regulated 200 nm-4 µm. By adjusting the magnetic field variations, it is permitted to control and transform the cluster structure of MJNs, forming vortex and ribbon clusters. Induced by the magnetic field, the MJNs can infiltrate into narrow dentinal tubules and reaching depths of up to ∼36 µm. To enhance the interaction with dentin, polycatechol group was introduced onto the surface of MJNs to promote mineral formation within the tubules. As a result, dentinal tubule occlusion could be achieved within 3 days under the tested conditions, indicating the potential of this approach for DH treatment.

Gas sensing plays a critical role across diverse fields, including medical diagnostics, industrial safety, and environmental monitoring. However, conventional gas sensors often suffer from poor selectivity and limited sensitivity, especially at low concentrations. Herein, we present a light-regulated gas-sensing system based on MXene-hydrogel composites, integrated with machine learning, for a low detection limit (5 ppb) and high-accuracy classification. PNIPAM hydrogel provides a reconfigurable and adsorptive surface, while MXene offers excellent electrical conductivity and photothermal conversion. Near-infrared light modulation further enhanced selectivity and reduced response/recovery times. When integrated with machine learning classification algorithms, the sensing system enabled robust classification of ten gas molecules with an accuracy of 98.64%. In a pilot breath-sample discrimination task, the system further distinguished cancer patients from healthy controls with a binary classification accuracy of 97.3%. These findings highlight the potential of combining light-regulated sensing materials with machine learning analysis for compact gas identification and exploratory breath-based screening.

Brain metastasis (BM) is one of the leading causes of cancer-related deaths in patients with advanced non-small cell lung cancer (NSCLC). However, limited treatments are available due to the presence of the blood-brain barrier (BBB). Upregulation of lysophosphatidylcholine acyltransferase 1 (LPCAT1) in NSCLC has been found to promote BM. Conversely, downregulating LPCAT1 significantly suppresses the proliferation and metastasis of lung cancer cells. In this study, we firstly confirmed significant upregulation of LPCAT1 in BM sites compared to primary lung cancer by analyzing scRNA dataset. We then designed a delivery system based on a single-chain variable fragment (scFv) targeting the epidermal growth factor receptor (EGFR) and exosomes derived from HEK293T cells to enhance cell-targeting capabilities and increase permeability. Next, we loaded LPCAT1 siRNA (siLPCAT1) into these engineered exosomes (exoscFv). This novel scFv-mounted exosome successfully crossed the BBB in an animal model and delivered siLPCAT1 to the BM site. Silencing LPCAT1 efficiently arrested tumor growth and inhibited malignant progression of BM in vivo without detectable toxicity. Overall, we provided a potential platform based on exosomes for RNA interference (RNAi) therapy in lung cancer BM.

Photocatalytic hydrogen production offers a sustainable solar-to-hydrogen conversion route by splitting water under light irradiation. However, most systems remain confined to laboratory conditions because of limited efficiency, stability, and scalability. This review provides an overview of how materials design, system engineering, and data-driven tools can be integrated to advance photocatalytic hydrogen production toward practical deployment. Fundamental strategies for improving solar-to-hydrogen efficiency are first summarized, focusing on band-structure modulation, charge-carrier dynamics, surface catalytic-site optimization, and stability enhancement. Representative photocatalyst families-including metal oxides, metal sulfides, MOFs, MXenes, and their heterostructures-are discussed in terms of how S-scheme and Z-scheme architectures, cocatalyst loading, facet control, and defect engineering collectively govern light absorption, charge separation, and reaction kinetics. Beyond powder suspensions, approaches such as photothermal catalysis, photoelectrocatalysis, magnetic-field-assisted photocatalysis, and photobiocatalytic systems that couple biological components with inorganic photocatalysts are highlighted. Emphasis is placed on the role of artificial intelligence in accelerating materials discovery, elucidating structure-property-performance relationships, and guiding reactor and process optimization. By linking mechanistic insights with system-level design and using quantitative performance indicators such as AQE and STH, this review outlines a framework for developing efficient, durable, and scalable photo-catalytic hydrogen production technologies suitable for real-world deployment.

Photothermal therapy is an effective approach to overcome bacterial antibiotic resistance; however, challenges remain in improving the targeting of photothermal agents and minimizing inflammation to healthy tissues. Herein, we present hydrogen-loaded AuPd nanoparticles stabilized through strong metal-carbene interactions using a micro-environment responsive N-heterocyclic carbene ligand (NHC-AuPdH), intended to effectively combat biofilm infections by photothermal effect. The NHC-AuPdH nanoparticles exhibit pH-responsiveness, whereby protonation of NHC induces a positive surface charge of nanoparticles in the acidic environment of biofilms. These positively charged nanoparticles enhance electrostatic interactions with the negatively charged bacterial surfaces, providing a versatile platform for targeted therapeutic applications. Meanwhile, the hydrogen stored in NHC-AuPdH nanoparticles is released by near infrared irradiation to react with reactive oxygen species and therewith eliminate inflammation induced by hyperthermia and bacterial infections. Furthermore, the NHC-AuPdH nanoparticles are accelerating wound healing by simultaneously facilitating rapid bacterial biofilm eradication and inflammation relief to protect healthy tissue and promote angiogenesis. This study provides a new micro-environment responsive N-heterocyclic carbene ligand as a general chemical platform for stabilizing metal nanoparticles and enabling targeted antibacterial therapy.

Efficient regeneration of the coenzymes NADH and NADPH is crucial for sustaining biocatalytic processes, particularly in driving the biocatalysis reaction. In this study, the photocatalytic properties of alkali-doped graphitic carbon nitrates were explored to achieve efficient NAD(P)H regeneration. g-C3N4 doped with potassium (KOH), sodium (NaOH), and calcium (Ca(OH)2) was synthesized and characterized, showing significant enhancements in light absorption, charge separation, and photogenerated electron transport. Among the synthesized photocatalysts, KOH-doped g-C3N4 (KCN) exhibited the best photocatalytic performance, regenerating 95 % NADH and 92 % NADPH within 30 min. This result compares favorably with those reported in previous studies. Based on this superior performance, K-0.2 was further applied to the in vitro CO2 reduction to formate as a proof of concept. To clarify the mechanistic insights of the improved photocatalytic performance of KCN, the chemical model of g-C3N4 and KCN was established, and DFT calculations were performed to reveal the electronic structure modifications responsible for the enhanced photocatalytic activity. Furthermore, to expand the light utilization capability of the system, upconversion nanoparticles (UCNPs) were incorporated into the system to utilize near-infrared light (980 nm), which successfully achieved NADH regeneration under near-infrared excitation and extended the light absorption range of the photocatalytic system. Overall, this study demonstrated that alkali-doped g-C3N4 offered a highly efficient platform for NAD(P)H regeneration, and the integration of UCNPs further enriched the coenzyme regeneration scenario, suggesting a promising strategy for advancing sustainable coenzyme regeneration technologies.