搜索结果：Nature biomedical engineering

Hydrogels have gained prominence as a class of biomaterials in biomedicine due to their excellent biocompatibility, biodegradability, and high water retention. Among them, hydrogels derived from natural polysaccharides sourced from plants, animals, and microbes are attracting growing interest due to their renewable nature, low toxicity, low immunogenicity, and diverse functional properties. While several recent reviews have addressed polysaccharide-based hydrogels, they have largely focused on isolated aspects-such as 3D bioprinting formulations, double-network mechanical reinforcement, rheological behavior, or single-source polysaccharides-without establishing an integrated framework that links raw material selection, structural diversity, chemical modification, and crosslinking design to clinical translation. This review distinguishes itself by providing a systematic, end-to-end perspective that spans from the structural diversity of plant- and microbe-derived polysaccharides through recent advances in chemical modification and novel cross-linking strategies, to the fine-tuning of physicochemical properties for enhanced therapeutic outcomes. This article provides an overview of the progress made in the emerging biomedical applications and material design of natural polysaccharide hydrogels in terms of raw material selection, chemical modification, cross-linking mechanisms, and functional utilization. It aims to fully explore the potential of these materials and promote integration into advanced biomedical practices.

Polyurethane (PU) hydrogels have been conventionally synthesized using the polyaddition of toxic polyisocyanates with polyols in organic solvents or in bulk, then swelling them in water. In this research work, a previously reported fructose-derived polyhydroxyurethane /poly(sodium acrylate) hydrogel synthesized through a catalyst-free non-isocyanate aqueous route was investigated as a smart pH-responsive platform for the delivery of cefadroxil, a broad-spectrum cephalosporin antibiotic. The novelty of this work lies in the first evaluation of the multifunctional application of a biobased PHU hydrogel for controlled antibiotic delivery, extending its previously reported environmental application to the biomedical domain. The hydrogel exhibited pronounced pH-responsive swelling behaviour, reaching 524.5% swelling at pH 7.4 compared to 54.6% at pH 1.2 after 24 h. Cefadroxil encapsulation efficiency was determined to be 63.7%. Drug release was significantly enhanced at pH 7.4 due to increased ionization of carboxylate groups within the hydrogel network, resulting in increased electrostatic repulsion, network expansion, and accelerated diffusion of the drug. Structural characterisation by FTIR, PXRD, SEM, and TGA confirmed successful hydrogel formation and drug incorporation. Cytocompatibility studies using L929 fibroplast cells demonstrated the non-toxic nature of the hydrogel. The controlled drug release behavior of drug loaded hydrogel followed the Fickian mechanism of diffusion (R2 = 0.9909 at pH 7.4) as suggested by the Korsmeyer-Peppas model. These findings underscore the potential of PHU hydrogels as effective, biocompatible materials for controlled, pH-sensitive drug delivery, contributing to safer and more efficient biomedical applications.

Nature has evolved a remarkable diversity of structured adhesives that enable organisms to achieve robust, reversible, and adaptive attachment in natural environments. Unlike conventional chemical glues, these biological adhesives exhibit strong yet controllable adhesion with residue-free detachment, self-cleaning capabilities, and environmental adaptability, which are primarily enabled by their evolutionarily optimized hierarchical architectures. Deciphering the structural and mechanical principles underlying these systems is therefore essential for the rational design of next-generation bioinspired reversible adhesives. This review examines how biological adhesion principles can be translated into engineered structured adhesives by linking biological archetypes, interfacial mechanics, structural design, and functional integration within a unified framework. We discuss the physical mechanisms governing biological adhesion and the theoretical models that have shaped the current understanding of structured adhesive contacts. We then survey fabrication technologies and design strategies to gain enhanced adhesion, detachment regulation, and improved structural adaptability. Furthermore, we highlight emerging design paradigms, including interfacial stress regulation, internally heterogeneous architectures, programmable reversibility, and adhesion-sensing integration, that are shifting structured adhesives from static attachment structures toward adaptive, multifunctional, and intelligent interfaces. Finally, we outline the persistent challenges in structural design, scalable manufacturing, environmental adaptability, and system integration, while offering perspectives on future opportunities for advanced bioinspired adhesives in robotics, wearable systems, and biomedical applications.

Since the early 1960s, nanotechnology has been a critical area of science, allowing for the development of sophisticated nanomaterials. Nanofibers, one of the most widely used nanotechnological drug delivery systems, have emerged as a highly versatile platform within modern pharmaceutical sciences. By combining various polymers with active herbal ingredients, these systems mimic the natural extracellular matrix and provide improved functions such as a high surface area-to-volume ratio, drug targeting, and controlled drug release. Preclinical studies demonstrate that phytochemical-containing nanofibers have improved therapeutic profiles, including higher anti-inflammatory, antioxidant, antimicrobial, and antineoplastic effects. In various biomedical applications, such as promoting tissue engineering (such as bone and nerve regeneration), limiting tumor growth, and accelerating wound closure, it was shown that nanofibers may overcome the physicochemical limitations of herbal drugs such as low solubility and bioavailability. Despite promising preclinical study results, there are significant obstacles in the way of the commercialization of herbal drug-loaded nanofibers. The difficult standardization of multi-component herbal extracts, stabilization drawbacks, and the absence of scalable industrial manufacturing equipment that can maintain repeatable characterization of nanofibers are some of the major obstacles, in addition to a lack of clinical trials. To navigate the clinical translation of nanofibers, interdisciplinary collaboration regarding quality control, safety, and regulatory pathways is strictly necessary due to the case-by-case review approach utilized by regulatory bodies such as the US Food and Drug Administration (FDA) and European Medicines Agency (EMA). This review offers a comprehensive overview of research on herbal drug-loaded nanofibers and contributes a novel perspective with regulatory and clinical translational insights. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

暂无摘要（点击查看详情）

Discovery of NRF2 in 1994 was once assumed simplistic and classical but the following years witnessed phenomenal breakthroughs that led to a rapid increase in the components of complex molecular web. Emerging evidence has enabled us to develop a better understanding of the spatio-temporally placed molecular machinery both upstream and downstream to the NRF2 in different contexts and diseases. The exciting voyage to unravel the multifaceted roles of NRF2 in human diseases has catalyzed innovations in therapeutic approaches, inspiring the design of compounds for pharmacological modulation of NRF2 in disease prevention and intervention. In this review, we have set the spotlight on sketching a detailed landscape both upstream and downstream to the central signaling node of NRF2. Identification of the natural products having unique ability to pharmacologically target NRF2-associated networks. Later, we expand our discussion about ongoing and completed clinical trials related to multifaceted roles of NRF2 as an oncogene and a tumor suppressor. We argue that a holistic view of NRF2-driven upstream and downstream signaling is necessary for the identification of preventive or therapeutic opportunities.

Protein S-palmitoylation is a dynamic and reversible post-translational modification (PTM) that governs diverse cellular processes, yet its study remains constrained by the indirect and ensemble-averaging nature of classical biochemical assays. Here, we report a nanopore-based platform, PALM-Scan (palmitoylation analysis via label-free monitoring and signature capture on nanopore) that directly transduces physicochemical signatures of S-palmitoylated peptides into digital electrical signals. Using an engineered Mycobacterium smegmatis porin A (M2-MspA) nanopore, PALM-Scan enables sequence-independent identification of S-palmitoylation and simultaneously differentiates S-palmitoylation from other cysteine-directed PTMs, including S-nitrosylation and S-glutathionylation, without probes or enrichment. We further demonstrate its translational potential by successfully detecting and distinguishing S-palmitoylated subpopulations of disease-relevant biomarkers, including mouse beclin-1 (BECN1)- and glial fibrillary acidic protein (GFAP)-derived peptides, in complex mixtures with minimal sample input. In summary, by integrating label-free operation, single-molecule resolution, and multiplex discrimination, PALM-Scan provides a transformative tool for interrogating S-palmitoylation dynamics in both basic research and biomedical applications.

Diols represent a vital class of bulk chemicals widely used in polymer materials, fine chemicals, cosmetics, and biomedical fields. Due to the advantages of renewable feedstock, mild reaction conditions, and stereoselectivity, biomanufacturing for diol production has attracted extensive attention. Although biosynthetic pathways for some diols exist in nature, utilizing native microorganisms for diol production faces challenges such as low product titer, limited product diversity, and poor strain tolerance, which fail to meet industrial requirements. This review summarizes both natural and nonnatural biosynthetic pathways of diols, with a focus on recently developed artificial routes. It further highlights advances made by Chinese researchers in strain development, systems metabolic engineering, and fermentation process optimization. Finally, this review discusses major challenges and future directions for diol biomanufacturing, offering significant insights for green and sustainable industrial-scale production.

Liquid metals, particularly gallium-based alloys, uniquely combine fluidic compliance with metallic conductivity, which makes them ideal candidates for biomimetic design. Rather than treating biomimicry as the mere imitation of biological forms, we argue that liquid metal biomimicry should be understood as the realization of biological strategies through the intrinsic physics of fluidity and interfacial dynamics. This review organizes existing research within a hierarchical framework that couples physical liquidity, interface biology analogy, and functional emergence to explain how adaptive behaviors naturally arise from dynamic liquid metal systems. We examine representative systems across morphological and functional dimensions and contend that their true significance lies not in replicating nature but in addressing problems that conventional rigid materials cannot solve. Looking forward, we identify several transformative directions that collectively chart a roadmap toward truly intelligent and autonomous bioinspired systems. By bridging the physics of fluidity with the principles of biological adaptability, liquid metal biomimicry holds transformative potential for soft robotics, wearable electronics, neuromorphic computing, and biomedical engineering.

Interpretation and mapping strategies for 4D-scanning transmission electron microscopy (4D-STEM) are well-developed for crystalline materials, yet in the case of amorphous and mixed materials it is significantly more challenging to separate different phases. Nonnegative matrix factorization (NMF) in principle would allow separation of 4D-STEM data into components with interpretable diffraction signatures and intensity maps, independent of the crystalline, amorphous or mixed nature of the material. However, adoption of NMF in this field is hampered by large datasets and conceptual hurdles: NMF tackles a nonconvex optimization problem, requiring iterative algorithms. Additionally, the stopping condition has to be chosen carefully. In this work, we show that the factorization of large 4D-STEM datasets can be drastically accelerated using a QB decomposition (i.e., randomized NMF or RNMF), leading to much shorter time per iteration. This allows structure-independent phase mapping on very large 4D-STEM datasets. We validate this approach on a synthetic literature dataset (mixed ZrCuAl), before mapping a thin TiO2 layer on top of SiO2, and an interface between a lithium-ion cathode and solid-state electrolyte. We also demonstrate that, before using NMF to transform the data on an interpretable, nonnegative basis, principal component analysis (PCA) can be used for fast exploratory analysis to assess dataset dimensionality and linearity.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

An innovative strategy for collagen self-assembly with accelerated intra and extrafibrillar mineralisation is introduced to generate bone scaffolds with biomimetic properties. This method, termed Rapid Fibrillogenic Mineralisation (RFM), leverages coprecipitation with 10× Simulated Body Fluid (10× SBF) during fibril formation to maximise nucleation, particularly within intrafibrillar zones at molecular termini. Densification is achieved within minutes via plastic compression driven by capillary action, producing bone-like scaffold density without compromising the collagen matrix. Transmission electron microscopy confirms intrafibrillar hydroxyapatite crystals within 15 min, while X-ray diffraction demonstrates distinct HA peaks across groups. Scanning electron microscopy verified extrafibrillar mineralisation after 4 h, with saturation by 6 h, yielding 'nanoflower' crystal clusters. Infrared spectra showed increased carbonate content over time, indicating lattice substitutions characteristic of natural bone. Enhanced mineralisation translated into significant mechanical gains as Dynamic Mechanical Analysis revealed compressive moduli approaching cancellous bone (up to 283 ± 31 MPa). In addition, a decrease in the piezoelectric coefficient occurs with increased mineralisation process, highlighting the effects of mineral inclusions on collagen fibre composition and anisotropy. Biologically, mineralised scaffolds supported cellular growth compared to collagen controls. RFM thus enables rapid, reproducible fabrication of biomimetic bone scaffolds that closely emulate native mineralisation patterns and mechanical behaviour. Beyond offering a practical route for scaffold production in tissue engineering, the process also provides new insights into bone physiology and in vitro modelling. By reshaping collagen into a synthetic echo of nature's bone, RFM establishes a rapid approach for designing functional biomaterials with translational potential.

Several viral vectors have been developed for gene therapy due to their high transduction efficiency, but some integrate into the host genome, raising safety concerns. Recent studies have identified recombinant adeno-associated virus serotype 6 (rAAV6) as a promising vector for hematopoietic stem and progenitor cell-targeted gene therapy because of its non-pathogenic nature, low integration frequency, and capacity for sustained episomal transgene expression. Nevertheless, its chromosomal integration profile remains incompletely defined, warranting a comprehensive evaluation to assess long-term safety. In this study, human CD34＋ cells were transduced with rAAV6 under varying vector doses and transgene contexts, and integration-site mapping was performed using the integration-site enriched library sequencing approach. Consistent with the largely episomal nature of AAV, high vector sequence alignment rates were observed across all groups. rAAV6 integrations occurred randomly throughout the genome, showing a broad pan-chromosomal distribution without evidence of sequence-specific targeting or clustering. Although integrations were more frequent in CpG islands, commonly located within open chromatin, this pattern likely reflects chromatin accessibility rather than targeting bias. Functional enrichment analysis indicated associations with general cellular and structural processes, without enrichment in oncogenic pathways. Distance-based analysis confirmed that integration sites were mapped at a distance from oncogenes and tumor suppressor genes, even under high-dose conditions. The data support the genomic safety of rAAV6 and its applicability to hematological gene therapy.

Breast cancer has emerged as a significant health concern in recent years, with a higher incidence rate but a persisting lack of effective therapeutic agents. This study focuses on the formulation of ZrO2 nanoparticles using both an effective hydrothermal method (CS-ZrO2) and an eco-friendly green synthesis method using Dioscorea alata leaf extract (GS-ZrO2). The synthesised ZrO2 nanoparticles were characterised using XRD, which confirmed their cubic crystal structure. Further, the average crystalline size (D) was 12.08 nm and 26.69 nm for hydrothermally synthesized and green synthesized ZrO2 nanoparticles. The determined band gap values were 2.78 eV and 3.09 eV for CS-ZrO2 and GS-ZrO2 nanoparticles. The reduced band gap value of CS-ZrO2 nanoparticles corresponds to their decreased crystalline size, which results in increased ROS generation. Subsequently, the synthesized ZrO2 nanoparticles were evaluated for their anti-diabetic, anti-inflammatory, and cytotoxicity properties through the α-amylase inhibition technique, Bovine albumin serum (BSA) denaturation technique, and MTT assay. Anti-diabetic and Anti-inflammatory activities of ZrO2 nanoparticles exhibited an increased percentage of inhibitions, 82.36%, and 90.11% respectively for CS-ZrO2 nanoparticles. The cytotoxicity potential against the MDA-MB-231, MCF-7 breast cancer cell lines and HEK 293 normal cell line was studied, showed a dose-dependent nature and decreased cell viability. The reduced viability attributed to the generation of reactive oxygen species (ROS) is associated with the reduced band gap and the decreased size of the ZrO2. Thus, the synthesized ZrO2 nanoparticles show potential as an efficient therapeutic agent and can be considered for various biomedical applications as a proficient material.

Zwitterionic hydrogels have attracted considerable attention for protein stabilization, cell encapsulation, and therapeutic delivery owing to their highly hydrated and bioinert nature. Nevertheless, developing injectable zwitterionic hydrogels that can form under mild and cytocompatible conditions remains a major challenge, especially for the delivery of fragile proteins and living cells. This challenge mainly stems from the weak intrinsic intermolecular interactions and limited reactive groups of zwitterionic polymers, which make it difficult to establish stable hydrogel networks without additional molecular modification or external crosslinking. Here, we report an injectable zwitterionic supramacromolecular hydrogel (SSH) formed by the spontaneous assembly of poly(methacryloyloxyethyl sulfobetaine) (PSBMA) and poly(styrenesulfonate) (PSSNa). Molecular dynamics simulations support the involvement of anion-π interactions in gelation, leading to a self-healing physically crosslinked network. SSH exhibits injectability, self-healing behavior, intrinsic antiadhesive properties, and favorable immunocompatibility. The mild gelation process enables efficient cell encapsulation and injection while maintaining high cell viability. In addition, SSH stabilizes and delivers basic fibroblast growth factor (bFGF), resulting in improved therapeutic efficacy in a diabetic wound-healing model. This work establishes a mild gelation strategy for injectable zwitterionic hydrogels and highlights their potential in biomedical delivery and cell-based therapies.

Body temperature is a key physiological indicator, requiring continuous, full-range monitoring from normothermia to hyperthermia for effective heatstroke management. To address the limitations of mercury thermometers and existing sensors in visualized real-time screening, three monomers S-1, S-2, and S-3 featuring flexible aliphatic chains and ester terminals, anhydride terminals, and aromatic rings with amide hydrogen bonds and π-π stacking are designed and polymerized into Poly(S-1), Poly(S-2), and Poly(S-3), respectively. Among these polydiacetylenes (PDAs) with symmetric functionalization, Poly(S-1) exhibits a multi-step, naked-eye-visible thermochromic transition within 35°C-43°C, attributed to its flexible aliphatic chains and ester linkages that enable side-chain motion and main-chain conformational changes at low temperatures. In contrast, the rigid structural motifs present in Poly(S-2) and Poly(S-3) introduce stronger intermolecular forces and higher energy barriers, necessitating elevated temperatures to induce comparable thermochromic transitions. Notably, Poly(S-1) is successfully fabricated into a functional patch; its irreversible thermochromic nature within the physiological range meets the requirements of disposable medical devices, helping to prevent cross-infection and facilitating temperature recording. This study enables precise tuning of the thermochromic temperature of PDA materials, expands their applications in biomedical monitoring and smart responsive materials, and offers design insights and theoretical foundations for developing next-generation visual body temperature sensors.

Hypothesis generation in biomedicine is constrained by human cognitive limitations in synthesizing insights from fragmented biomedical knowledge and multimodal data sources. Here we introduce XunZi, an AI biologist that integrates logical reasoning and multimodal data fusion to autonomously generate de novo therapeutic target hypotheses with testable mechanisms. XunZi has been trained on 24.4 million publications and 613.6 TB of multisource data spanning 21,008 human genes and 5,850 diseases, and outperforms existing methods in both accuracy and interpretability across diverse disease contexts. In Parkinson's disease (PD), where complex mechanisms and limited targets hamper therapy development, XunZi identifies aberrant activation of CHK2 and IRAK4 kinases across multiple models. Pharmacological or genetic inhibition of Chk2 rescues dopaminergic neuron loss and motor deficits in PD mice. We further demonstrate XunZi's broad versatility in diseases such as non-small-cell lung cancer. XunZi establishes a paradigm-shifting framework to translate fragmented biomedical knowledge and data into actionable therapeutics.

Accurate prediction of variants within intrinsically disordered regions (IDRs) is crucial for advancing disease diagnosis and biomedical interpretation. However, the intrinsic lack of stable structural conformations and the high sequence variability of IDRs make it challenging for existing predictors to achieve robust performance in these regions. Here, we introduce DisoPatho, a deep learning framework specifically tailored for predicting disease-associated variants in IDRs. DisoPatho features a novel mutation-centric architecture that utilizes the variant site as an anchor for feature construction and interaction. The core innovation lies in a cross-view adaptive-feature interaction mechanism, which synergistically integrates IDR-specific energy representations with embeddings from protein language models, including xTrimoPGLM and Evolutionary Scale Modeling. This strategy enables the comprehensive capture of evolutionary constraints and physicochemical patterns without requiring explicit structural descriptors, multiple-sequence alignments, or hand-crafted conservation scores. Consequently, DisoPatho exhibits enhanced discriminative power better adapted to the highly flexible nature of IDRs. Comprehensive evaluations across multiple IDR data sets demonstrate that DisoPatho substantially outperforms existing methods. In 5-fold cross-validation, it achieves average AUCs of 0.899 and 0.840, with ACCs of 0.862 and 0.860 on two data sets. Notably, on a highly confounded independent test set where phylogenetic constraints offer limited discriminative signals, DisoPatho yields a 50.2% relative improvement in MCC over AlphaMissense on their respective predictable variants, while achieving broader prediction coverage. In-depth analyses of the prediction results further confirm the effectiveness and stability of the framework in IDR-specific scenarios. The code, data sets, and predictions for DisoPatho are available for academic use at https://github.com/IBHFLab/DisoPatho.

Phenolic compounds pose a major threat to water pollution due to their high toxicity. Enzymes have emerged over recent years to act as a green alternative for cleaning up and utilizing phenolic compounds in nature. DyP peroxidases, a member of the oxidoreductase family, have shown promising ability to convert dyes into harmless compounds and have been found indispensable tools for green polymerization. In this study, three recombinant DyP peroxidases were used to convert pyrogallol (PG) into a polymer which upon characterization using Fourier-transform infrared spectroscopy (FT-IR), Raman spectroscopy, powder X-ray diffraction (XRD) and scanning electron microscopy (SEM) have revealed intricate details characteristic of poly(pyrogallol) (PPG). The synthesized polymer showed a rigid structure, with potential use in material science applications such as coatings or binding agents, as well as broader aspects in environmental and biomedical research.