Search Results (1,006)

Bone tissue engineering, as an important branch of regenerative medicine, integrates multidisciplinary knowledge from cell biology, materials science, and biomechanics, aiming to develop novel biomaterials and technologies for functional repair and regeneration of bone tissue. Hydrogels are among the most commonly used scaffold materials; however, conventional hydrogels exhibit significant limitations in physical properties such as strength, tensile strength, toughness, and fatigue resistance, which severely restrict their application in load-bearing bone defect repair. As a result, the development of high-strength hydrogels has become a research hotspot in the field of bone tissue engineering. This paper systematically reviews the latest research progress in this area: First, it delves into the physicochemical characteristics of high-strength hydrogels at the molecular level, focusing on core features such as their crosslinking network structure, dynamic bonding mechanisms, and energy dissipation principles. Next, it categorically summarizes novel high-strength hydrogel systems and different types of biomimetic hydrogels developed based on various reinforcement strategies. Furthermore, it provides a detailed evaluation of the application effects of these advanced materials in specific anatomical sites, including cranial reconstruction, femoral repair, alveolar bone regeneration, and articular cartilage repair. This review aims to provide systematic theoretical guidance and technical references for the basic research and clinical translation of high-strength hydrogels in bone tissue engineering, promoting the effective translation of this field from laboratory research to clinical application. Full article

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article

Collagen type I has become a practical cornerstone for constructing biologically meaningful barrier interfaces in microfluidic systems. Its fibrillar architecture, native ligand display, and susceptibility to cell-mediated remodeling support epithelial and endothelial polarization, tight junctions, and transport behaviors that are difficult to achieve with purely synthetic barrier interfaces. Recent advances pair these biological strengths with tighter engineering control. For example, ultrathin collagen barriers (tens of micrometers or less) enable faster molecular exchange and short-range signaling; gentle crosslinking and composite designs limit gel compaction and delamination under flow; and patterning/bioprinting introduce alignment, graded porosity, and robust integration into device geometries. Applications now span intestine, vasculature, skin, airway, kidney, and tumor–stroma interfaces, with readouts including transepithelial/transendothelial electrical resistance (TEER), tracer permeability, and image-based quality control of fiber architecture. Persistent constraints include batch variability, long-term mechanical drift, limited standardization of fibrillogenesis conditions, and difficulties scaling fabrication without loss of bioactivity. Priorities include reporting standards for microstructure and residual crosslinker, chips for continuous monitoring, immune-competent co-cultures, and closer collaboration across materials science, microfabrication, computational modelling, and clinical pharmacology. Thus, this review synthesizes the state-of-the-art and offers practical guidance on technological readiness and future directions for using collagen type I as a biological barrier interface in biomimetic microfluidic systems. Full article

Biological decision-making under uncertainty relies on somatic markers, which are affective signals that bias choices without exhaustive computation. This study biomimetically translates the Somatic Marker Hypothesis (SMH) into synthetic somatic markers (SSMs), a minimal and interpretable evaluative mechanism that assigns a scalar valence to compressed environmental states in the high-dimensional discrete grid-world Pixelverse, without modelling subjective feelings. SSMs are implemented as a lightweight Python routine in which agents accumulate valence from experience and use a simple threshold rule (θ = −0.5) to decide whether to keep the current trajectory or reset the environment. In repeated simulations, agents perform few resets on average and spend a higher proportion of time in stable “good” configurations, indicating that non-trivial adaptive behaviour can emerge from a single evaluative dimension rather than explicit planning in this small stochastic grid-world. The main conclusion is that, in this minimalist 3 × 3 Pixelverse testbed, SMH-inspired SSMs provide an economical and transparent heuristic that can bias decision-making despite combinatorial state growth. Within this toy setting, they offer a conceptually grounded alternative and potential complement to more complex affective and optimisation model. However, their applicability to richer environments remains an open question for future research. The ethical implications of deploying such bio-inspired evaluative systems, including transparency, bias mitigation, and human oversight, are briefly outlined. Full article

Biomimetic quadruped robots, inspired by the musculoskeletal systems of animals, employ pneumatic artificial muscles (PAMs) as compliant actuators to achieve flexible, efficient, and adaptive locomotion. This study focuses on a pneumatic artificial muscle (PAM)-driven biomimetic leg joints system. First, its kinematic and dynamic models are established. Next, to address the challenges posed by the strong nonlinearities and complex time-varying uncertainties inherent in PAMs, an adaptive robust control algorithm is proposed by employing the Udwadia controller. Rigorous theoretical analysis of the adaptive robust control algorithm is verified via the Lyapunov stability method. Finally, numerical simulations and hardware experiments are conducted on the PAM-driven biomimetic leg joints system under desired trajectories, where the adaptive robust control algorithm is systematically compared with three conventional control algorithm to evaluate its control performance. The experimental results show that the proposed controller achieves a maximum tracking error of within 0.05 rad for the hip joint and within 0.1 rad, highlighting its strong potential for practical deployment in real-world environments. Full article

Recent advances in automated learning techniques have enhanced the analysis of biomedical signals for detecting sleep stages and related health abnormalities. However, many existing models face challenges with imbalanced datasets and the dynamic nature of evolving sleep states. In this study, we present a robust algorithm for classifying sleep states using electroencephalogram (EEG) data collected from 33 healthy participants. We extracted dynamic, brain-inspired features, such as microstates and Lempel–Ziv complexity, which replicate intrinsic neural processing patterns and reflect temporal changes in brain activity during sleep. An optimal feature set was identified based on significant spectral ranges and classification performance. The classifier was developed using a convolutional neural network (CNN) combined with gated recurrent units (GRUs) within a reinforcement learning framework, which models adaptive decision-making processes similar to those in biological neural systems. Our proposed biomimetic framework illustrates that a multivariate feature set provides strong discriminative power for sleep state classification. Benchmark comparisons with established approaches revealed a classification accuracy of 98% using the optimized feature set, with the framework utilizing fewer EEG channels and reducing processing time, underscoring its potential for real-time deployment. These findings indicate that applying biomimetic principles in feature extraction and model design can improve automated sleep monitoring and facilitate the development of novel therapeutic and diagnostic tools for sleep-related disorders. Full article

Accurate and quantitative pain assessment remains a major challenge in clinical medicine, especially for patients unable to verbalize discomfort. Conventional methods based on self-reports or clinician observation are subjective and inconsistent. This study introduces a novel automated facial pain assessment framework built on a dual-attention convolutional neural network (CNN) that achieves clinically calibrated, high-reliability performance and interpretability. The architecture combines multi-head spatial attention to localize pain-relevant facial regions with an enhanced channel attention block employing triple-pooling (average, max, and standard deviation) to capture discriminative intensity features. Regularization through label smoothing (α = 0.1) and AdamW optimization ensures calibrated, stable convergence. Evaluated on a clinically annotated dataset using subject-wise stratified sampling, the proposed model achieved a test accuracy of 90.19% ± 0.94%, with an average 5-fold cross-validation accuracy of 83.60% ± 1.55%. The model further attained an F1-score of 0.90 and Cohen’s κ = 0.876, with macro- and micro-AUCs of 0.991 and 0.992, respectively. The evaluation covers five pain classes (No Pain, Mid Pain, Moderate Pain, Severe Pain, and Very Pain) using subject-wise splits comprising 5840 total images and 1160 test samples. Comparative benchmarking and ablation experiments confirm each module’s contribution, while Grad-CAM visualizations highlight physiologically relevant facial regions. The results demonstrate a robust, explainable, and reproducible framework suitable for integration into real-world automated pain-monitoring systems. Inspired by biological pain perception mechanisms and human facial muscle responses, the proposed framework aligns with biomimetic sensing principles by emulating how localized facial cues contribute to pain interpretation. Full article

Contemporary neural and generative architectures are deficient in self-preservation mechanisms and sustainable stability. In uncertain or noisy situations, they frequently demonstrate oscillatory learning, overconfidence, and structural deterioration, indicating a lack of biological regulatory principles in artificial systems. We present Bio-RegNet, a meta-homeostatic Bayesian neural network architecture that integrates T-regulatory-cell-inspired immunoregulation with autophagic structural optimization. The model integrates three synergistic subsystems: the Bayesian Effector Network (BEN) for uncertainty-aware inference, the Regulatory Immune Network (RIN) for Lyapunov-based inhibitory control, and the Autophagic Optimization Engine (AOE) for energy-efficient regeneration, thereby establishing a closed energy–entropy loop that attains adaptive equilibrium among cognition, regulation, and metabolism. This triadic feedback achieves meta-homeostasis, transforming learning into a process of ongoing self-stabilization instead of static optimization. Bio-RegNet routinely outperforms state-of-the-art dynamic GNNs across twelve neuronal, molecular, and macro-scale benchmarks, enhancing calibration and energy efficiency by over 20% and expediting recovery from perturbations by 14%. Its domain-invariant equilibrium facilitates seamless transfer between biological and manufactured systems, exemplifying a fundamental notion of bio-inspired, self-sustaining intelligence—connecting generative AI and biomimetic design for sustainable, living computation. Bio-RegNet consistently outperforms the strongest baseline HGNN-ODE, improving ARI from 0.77 to 0.81 and NMI from 0.84 to 0.87, while increasing equilibrium coherence $\kappa$ from 0.86 to 0.93. Full article

This study outlines the design and evaluation of a biomimetic robotic hand tailored for Human–Robot Interaction (HRI), focusing on improvements in tactile fidelity driven by material choice. Thermoplastic polyurethane (TPU) was selected over polylactic acid (PLA) based on its reported elastomeric characteristics and mechanical compliance described in prior literature. Rather than directly matching human skin properties, TPU was perceived as providing a softer and more comfortable tactile interaction compared to rigid PLA. The robotic hand was anatomically reconstructed from an open-source model and integrated with AX-12A and MG90S actuators to simplify wiring and enhance motion precision. A custom PCB, built around an ATmega2560 microcontroller, enables real-time communication with ROS-based upper-level control systems. Angular displacement analysis of repeated gesture motions confirmed the high repeatability and consistency of the system. A repeated-measures user study involving 47 participants was conducted to compare the PLA- and TPU-based prototypes during interactive tasks such as handshakes and gesture commands. The TPU hand received significantly higher ratings in tactile realism, grip satisfaction, and perceived responsiveness (p < 0.05). Qualitative feedback further supported its superior emotional acceptance and comfort. These findings indicate that incorporating TPU in robotic hand design not only enhances mechanical performance but also plays a vital role in promoting emotionally engaging and natural human–robot interactions, making it a promising approach for affective HRI applications. Full article

Chronic and complex wounds, including diabetic foot ulcers, venous leg ulcers, burns and post-surgical defects, remain difficult to manage due to persistent inflammation, impaired angiogenesis, microbial colonization and insufficient extracellular matrix (ECM) remodeling. Conventional dressings provide protection, but they do not supply the necessary biochemical and structural signals for effective tissue repair. This review examines recent advances in hydroxyapatite–collagen (HAp–Col) composite dressings, which combine the architecture of collagen with the mechanical reinforcement and ionic bioactivity of hydroxyapatite. Analysis of the literature indicates that in situ and biomimetic mineralization, freeze-drying, electrospinning, hydrogel and film processing, and emerging 3D printing approaches enable precise control of pore structure, mineral dispersion, and degradation behavior. Antimicrobial functionalization remains critical: metallic ions and locally delivered antibiotics offer robust early antibacterial activity, while plant-derived essential oils (EOs) provide broad-spectrum antimicrobial, antioxidant and anti-inflammatory effects with reduced risk of resistance. Preclinical studies consistently report enhanced epithelialization, improved collagen deposition and reduced bacterial burden in HAp–Col systems; however, translation is limited by formulation variability, sterilization sensitivity and the lack of standardized clinical trials. Overall, HAp–Col composites represent a versatile framework for next-generation wound dressings that can address both regenerative and antimicrobial requirements. Full article

Pharmaceuticals and personal care products (PPCPs) are emerging contaminants posing ecological risks in wastewater. Constructed wetlands (CWs) offer sustainable treatment through integrated biological processes. In this study, a biomimetic microbial CW reactor was developed using 30 L aquariums with porous media, aeration setups, and surface plants to simulate natural wetland conditions. This design combines enhanced microbial degradation strategies using fungal (Trametes versicolor), bacterial (Pseudomonas aeruginosa), and consortia degradation, integrating multiple biological pathways. Synthetic wastewater containing 100 mg/L of selected PPCPs, including caffeine, methylparaben, and trichlorocarbanilide (TCC), was used to evaluate the degradation potential of these microbial treatments. While caffeine and methylparaben were effectively targeted, TCC degradation was inconclusive due to solubility limitations in the selected solvent. Over three months, system stability, plant growth, and microbial biomass were monitored, and contaminant degradation was tracked using Nuclear Magnetic Resonance analysis. Results demonstrated that individual fungal and bacterial treatments achieved near-complete caffeine degradation (99–100%) within seven weeks, while the combined treatment accelerated this process to just four weeks. Methylparaben followed a similar trend, achieving complete degradation by the seventh week. This study highlights the potential of microbial CW systems fortified with targeted microbial consortia as a scalable solution for pollutant removal. Future work should refine microbial combinations and analytical methods to expand the range of treatable pollutants. Full article

Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models. Full article

Cell membrane-coated nanoparticles represent a biomimetic drug delivery approach that integrates biological membrane functions with synthetic nanomaterials. Among the various membrane sources, those derived from blood cells such as red blood cells, platelets, and leukocytes offer distinctive advantages, including immune evasion, prolonged systemic circulation, and selective tissue targeting. These properties collectively enable efficient and biocompatible delivery of therapeutic agents to diseased tissues, minimizing off-target effects and systemic toxicity. This review focuses on blood cell membrane-derived nanocarriers as drug delivery and immune-regenerative platforms, in which membrane-mediated immunomodulation synergizes with therapeutic payloads to address inflammatory or degenerative pathology. We discuss recent advances in blood cell membrane coating technologies, including membrane isolation, nanoparticle core selection, fabrication techniques, and the development of hybrid and engineered membrane systems that enhance therapeutic efficacy through integrated immune regulation and localized drug action. To illustrate these advances, we also compile membrane type-specific nanocarrier systems, summarizing their core nanoparticle designs, coating strategies, therapeutic cargoes, and associated disease models. Challenges related to biological source variability, scalability, safety, and regulatory standardization remain important considerations for clinical translation. In this review we systematically address these issues and discuss emerging solutions and design strategies aimed at advancing blood cell membrane-based nanocarriers toward clinically viable immune-regenerative therapies. Full article

Antimicrobial peptides have been increasingly recognized as potential anticancer agents, with the KLA peptide (KLAKLAK₂) being one of the most well-known and successful examples. The research interest in the KLA peptide is attributed to its ability to induce apoptosis in cancer cells by disrupting the mitochondrial membrane. However, the KLA peptide exhibits poor cellular uptake and it lacks targeting specificity, limiting its clinical potential in cancer therapy. In this review, recent advances in nano-engineered delivery platforms for overcoming the limitations of KLA peptides and enhancing their anticancer efficacy are discussed. Specifically, various nanocarrier systems that enable targeted delivery, controlled release and/or improved bioavailability, including pH-responsive nanosystems, photo-chemo combination liposomes, self-assembled peptide-based nanostructures, nanogel-based delivery systems, homing domain-conjugated KLA structures, inorganic-based nanoparticles, and biomimetic nanocarriers, are highlighted. Additionally, synergistic strategies for combining KLA with chemotherapeutic agents or immunotherapeutic agents to overcome resistance mechanisms in cancer cells are examined. Finally, key challenges for the clinical application of these nanotechnologies are summarized and future directions are proposed. Full article

In vitro testing of ventricular assist devices, constructing a mock circulation system that reproduces physiological cardiac function, is critical. However, current ventricular simulators often lack biomimetic fidelity and may introduce hemolysis and coagulation risks during prolonged operation, affecting hemocompatibility assessment. This study proposes a motor-driven torsional 3D-printed left ventricular simulator to reconstruct the hemodynamics of severe heart failure and related pathological conditions. The system integrates a 3D-printed elastic ventricular model with programmable torsional actuation, allowing the simulation of various cardiac conditions by adjusting the motor torsion angle and rotational speed, peripheral resistance and compliance. Fresh porcine blood was circulated for 4 h in a closed-loop system, with periodic measurements of plasma-free hemoglobin (PfHb), thrombin–antithrombin complex (TAT), and P-selectin. The results show that the system successfully reproduces typical hemodynamic features of severe heart failure, while hemolysis and coagulation markers remain low. After 4 h, PfHb was below 20 mg/dL, with no significant platelet activation or thrombosis. This study demonstrates that the proposed system enhances biomimicry while maintaining excellent hemocompatibility, offering a reliable platform for in vitro performance and safety evaluation of ventricular assist devices. Full article