Construction of drug carriers or prodrugs is a common approach to improve the pharmacokinetics of molecular drugs and enhance the tumor curative effect. Nanocarriers that can be activated by the tumor microenvironment including enzymes, pH, redox status, or external stimuli such as ultraviolet, ultrasound, and magnetism, are booming. As a commonly used clinical theranostic approach with excellent spatiotemporal controllability and deep tissue penetration, X-ray has attracted much attention as an external stimulus in recent years. The rapid development of nanomaterials as radiosensitizers or radioluminescents further accelerates the construction of X-ray-responsive nanocarriers. In this review, the currently developed X-ray activated drug release systems in cancer therapy are summarized with a focus on controlled release design.

Human tooth enamel, consisting of hydroxyapatite nanocrystals (∼85-88 vol%), proteins (∼2-3 vol%), and requisite water (∼10-12 vol%), is 1-3 mm thick at the outer layer of the tooth. It possesses remarkable properties such as high stiffness, hardness, strength, and viscoelasticity. However, when tooth enamel undergoes deterioration due to various factors, including unhealthy dietary habits, wear and tear of the enamel and others, it progresses from surface stains to enamel loss, ultimately necessitating entire tooth enamel replacement. This is because the tooth enamel lacks the ability to generate cells on the damaged side of the tooth after eruption, preventing self-repair. Thus, enamel repair materials are in urgent demand, but clinical tooth enamel restorative materials nowadays cannot fully replicate the microstructure and function of natural tooth enamel. Numerous efforts have been made to develop the next generation restorative materials for tooth enamel to address different types of damage, including stains, defects, and loss. Here, we introduce the fundamental characteristics of tooth enamel at the beginning. Then, we provide a summary of the preparation process and function of existing tooth enamel restorative materials based on the various types of tooth enamel defects in detail. Finally, suggestions and development directions in the field of tooth enamel restoration are presented, with the aim of providing a theoretical reference for the creation of tooth enamel restorative materials that better match the microstructure and function of natural tooth enamel.

Recent strides in non-invasive drug delivery have spurred innovation in alternatives to traditional needle injections. Transdermal drug delivery systems (TDDs) have emerged as a particularly promising avenue, boasting minimal rejection rates, user-friendly administration, and enhanced patient adherence. Beyond pharmaceuticals, TDDs show potential in skincare and cosmetics, leveraging their ability to facilitate localized drug delivery while minimizing systemic exposure. Nonetheless, the intricate physicochemical nature of the skin presents formidable obstacles, prompting intensive exploration to overcome these barriers. This comprehensive review delves into the landscape of TDDs methodologies, critically analyzing their respective advantages, limitations, characterization methodologies, and prospective applications. Recent advancements underscore the robust efficacy of TDDs, positioning it as a versatile and indispensable modality poised for widespread integration across multifarious fields.

Smart biomaterials that can self-adapt or respond to microenvironmental factors or external signals hold excellent potential for a variety of biomedical applications, from biosensing, drug delivery, and cell therapy to tissue engineering. The complexity of smart biomaterials, including the rational design of their structure and composition, the accurate analysis and prediction of their properties, and the automatic and scale-up synthesis remains a critical challenge but can be addressed by the recent rise of artificial intelligence (AI). To bridge the literature gap, the current mini-review will introduce the background of why marrying AI with smart biomaterials is essential and how biomaterial scientists can integrate machine learning (ML) and AI for the discovery, design, analysis, and synthesis of smart biomaterials. For this purpose, the basic principles of ML and AI will first be introduced so that biomaterial scientists can use ML and AI as a tool for basic research. Next, representative examples of using AI to high throughput screen and establish big data of structure-function relationship of smart biomaterials responding to both chemical, biological, and physical signals. Most importantly, the applications of the AI-designed or AI-discovered biomaterials will be overviewed, with a focus on the field of tissue engineering. Lastly, new directions, such as robot-chemists-assisted fabrication of biomaterials will be highlighted. Taken together, by engaging biomaterial scientists with the most recent updates in AI material science, we expect to observe continuous growth of the field of AI for science and benefit clinical translation of smart biomaterials for treating a variety of diseases.

High recurrence rates and critical bone defects after surgical treatment seriously hinder the curing rate of malignant bone tumors. Thus, developing a multifunctional platform to achieve effective tumor elimination and bone regeneration is urgently needed. Recently, gas therapy combined with hyperthermia has shown an intriguing synergetic therapeutic effect on tumor, avoiding limited spot areas, insufficient heat in deep or surrounding regions, and other inherent limitations of photothermal therapy. Therefore, calcium alginate hydrogel was coated on the surface of 3D printing polycaprolactone (PCL) scaffold and loaded with sodium nitroprusside (SNP) to fabricate the PCLA-SNP composite scaffold. SNP can coordinately react with Fe³⁺ to form Prussian blue-like precipitates after ultraviolet irradiation to reduce the toxicity of SNP metabolites and improve the biocompatibility of materials. Hence, photothermal generated by a near-infrared laser and controlled release of nitric oxide (NO) were obtained to enhance ablation of bone tumors. Furthermore, the calcium and iron ions released from PCLA-SNP scaffolds can accelerate bone regeneration. With stable release of NO, efficient photothermal conversion, and promotion of bone regeneration, PCLA-SNP scaffolds have enlightened an interesting platform for synergistic gas-photothermal therapy for bone tumors.

Exosomes have the potential to be a noninvasive tumor biomarker for cancer diagnosis and classification. However, high-efficiency capture and analysis of exosomes in complex biological samples remain challenging. Here, we propose a high-throughput, rapid, ultrasensitive, and low-detection approach based on a spatially patterned antibody barcodes for quantitative analysis of exosomes. The combination of carbon dots (CDs) self-assembly substrate and microfluidic technology enables the patterning of antibody barcodes to capture exosomes. Then, the fluorescently labeled detection antibody CD63 is to react with the surface exosomal antigen CD63. The double-positive detection approach not only recognizes the identification of exosomes but also demonstrates that the exosomes express the targeted membrane marker. Based on the serum exosome detection results, it achieves 100% accuracy to differentiate ovarian cancer patients from healthy donors, and 90% accuracy in patient subgroup distribution. At the same time, the detection approach has a low detection limit of 65 particles/μL. The technology of spatially patterned antibody barcodes is promising in biology studies, early disease diagnosis, and new biomarker screening.

Multi-drug resistant cancer cells with over-expression of Bcl2 anti-apoptotic proteins can be effectively killed by transfecting them with Nur77 transcription factor (pNur77). Previous attempts are generally therapeutically unsatisfactory due to low sustained gene expression and are further disadvantaged by their multi-component designs requiring complicated preparation. Herein, we designed a single cationic amphiphilic copolymer from branched poly(ethylenimine) (PEI-25k), the gold standard non-viral vector, that functions simultaneously as the non-viral vector and hydrogel forming polymeric matrix. Our single-component hydrogel gene delivery platform is highly facile to prepare as it only requires co-dissolution of the copolymer with pNur77 to form a homogeneous sol. Due to the high cationic charge density of PEI-25k, this PEI-based copolymer effectively complexes a high payload of the plasmid. Subsequently, the copolymer's thermogelling ability enables it to spontaneously self-assemble into a hydrogel depot by simply being warmed to physiological temperatures upon intra-tumoral injection. Leveraging upon the high transfection efficiency of PEI-25k, this PEI-based thermogel achieved prolonged localized release of pNur77 with high transfection efficiency. This leads to successful tumor size reduction and suppressed tumor reoccurrence in mouse models with low systemic cytotoxicity. We believe this single-component cationic thermogel non-viral gene delivery platform is highly attractive for gene therapy.

Photodynamic therapy (PDT) utilizes light and photosensitizer (PS) to generate reactive oxygen species (ROS) to kill cancer cells, presenting a promising strategy as an anti-cancer treatment. However, the low hydrophilicity and poor targeting of currently used PSs, as well as the abnormal tumor microenvironment (TME), limit the clinical application of PDT. Herein, non-toxic liposomes with the ability to incorporate both hydrophilic and hydrophobic PS are used as the nanocarrier to co-load Hemoporfin, L-buthionine sulfoximine (BSO), and catalase (CAT) to obtain BSO/CAT@Liposomes-Hemoporfin nanoparticles (BCHL NPs), which could be used to remodel the TME and to enhance Hemoporfin-mediated PDT efficacy. BCHL NPs exhibit a long blood circulation time and can accumulate in the tumor. BSO can reduce the cytosolic concentration of glutathione (GSH), a natural scavenger of ROS. CAT catalyzes the endogenously overexpressed H₂O₂ in the tumor site into H₂O and O₂, thus relieving tumor hypoxia and enhancing ROS generation. Upon irradiation, the synergetic effects of reduced GSH synthesis by BSO and relieved hypoxia by CAT were observed in 4T1 tumor-bearing mouse model. Compared to the tumor treated by free Hemoporfin, BCHL NPs-mediated PDT resulted in 1.25-fold higher inhibition of tumor growth due to the enhanced ROS generation. The present study provides insight into the design of efficient strategies for enhanced clinic Hemoporfin-mediated PDT efficiency.

Single-cell sequencing has been constrained by the trade-off among throughput, capture bias, and compatibility with cells of unusual size or morphology. A recent innovative approach, Stereo-cell, addresses these constraints by coupling high-density DNA nanoball patterned arrays with planar in situ RNA capture and microscopy-guided segmentation, thereby eliminating droplet encapsulation while scaling the field of view by chip size. This planar architecture scales from thousands to >10⁶ cells per chip, maintains robust RNA in situ capture, and natively integrates multiplex immunofluorescence and oligo-barcoded antibodies to deliver concurrent transcriptomic and proteomic readouts. This perspective evaluates Stereo-cell relative to droplet- and plate-based methods across throughput, sensitivity, spatial resolution, and sample versatility, and outlines practical considerations for rare-cell detection and subcellular transcript localization. By bridging single-cell and spatial omics in a unified workflow, Stereo-cell offers a general-purpose platform to map cellular states, interactions, and subcellular organization at unprecedented scale.

Artificial intelligence (AI) has emerged as a transformative force in biomedical engineering, catalyzing a shift toward data-driven, intelligent research paradigms. With its advanced capabilities in computation, pattern recognition, and large-scale data analysis, AI significantly enhances the efficiency, precision, and reproducibility of biomedical research. In particular, the convergence of AI with biomedical engineering has given rise to the emerging field of Digital Biomedical Engineering, which emphasizes the integration of AI technologies, big data analytics, and computational modeling to enable smart, predictive, and personalized solutions across life sciences. This review provides a comprehensive overview of the current landscape of AI applications in biomedical engineering, highlighting advances in medical image analysis, biosignal processing, biomaterials design, and drug development. It also emphasizes how AI improves diagnostic precision, accelerates material and drug discovery, and fosters personalized and predictive medicine. Additionally, it discusses the limitations and regulatory challenges of AI adoption, while outlining future directions to guide research innovation and clinical translation in the era of digital biomedicine.