Biomimetics

14 pages, 829 KB

Open AccessArticle

Use of Amalgam and Composite Restorations Among 12-Year-Old Children in Israel: A Retrospective Study

by Rimah Nassar, Tali Chackartchi, Haim Doron, Jonathan Mann, Mordechai Findler and Guy Tobias

Biomimetics 2025, 10(12), 833; https://doi.org/10.3390/biomimetics10120833 - 12 Dec 2025

Background: This study examined the trends in restorative dental practice among 12-year-old children treated at a nationwide public health maintenance organization in Israel between 2016 and 2022, focusing on the use of amalgam versus composite resin restorations in permanent premolars and molars. Methods: [...] Read more.

Background: This study examined the trends in restorative dental practice among 12-year-old children treated at a nationwide public health maintenance organization in Israel between 2016 and 2022, focusing on the use of amalgam versus composite resin restorations in permanent premolars and molars. Methods: Data were extracted from electronic health records of the second-largest public health organization in Israel, identifying children who underwent restorative treatments during the study period. Restoration rates were compared overall and stratified by gender, socioeconomic status, and number of surfaces restored. Statistical analysis was conducted using SPSS version 27, employing Levene’s test for equality of variances and Welch’s one-way ANOVA. Results: The results showed a statistically significant decline in amalgam use (p < 0.05) alongside a marked increase in composite resin restorations (p < 0.05), consistent across genders and socioeconomic groups. Notably, composite resins were increasingly selected for complex, multi-surface restorations (p < 0.05). Conclusions: These findings highlight a substantial shift in paediatric restorative practice in Israel, reflecting growing preference for composite resins likely influenced by patient demands and national dental reforms that eliminated financial barriers. The observed trend underscores the importance of continued monitoring of material selection to guide evidence-based practice in pediatric dentistry. Full article

(This article belongs to the Special Issue Biomimetic Strategies to Enhance Bone Tissue Healing, Remodeling and Regeneration: 2nd Edition)

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16 pages, 2128 KB

Open AccessArticle

Robust Motor Imagery–Brain–Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach

by Dong-Geun Lee and Seung-Bo Lee

Biomimetics 2025, 10(12), 832; https://doi.org/10.3390/biomimetics10120832 - 12 Dec 2025

Abstract

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. [...] Read more.

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain’s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen’s kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment. Full article

(This article belongs to the Special Issue Advances in Brain–Computer Interfaces (BCI): Challenges and Opportunities)

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24 pages, 4981 KB

Open AccessArticle

Propulsive Force Characterization of a Bio-Robotic Sea Lion Foreflipper: A Kinematic Basis for Agile Propulsion

by Anthony Drago, Nicholas Marcouiller, Shraman Kadapa, Frank E. Fish and James L. Tangorra

Biomimetics 2025, 10(12), 831; https://doi.org/10.3390/biomimetics10120831 - 12 Dec 2025

Abstract

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful [...] Read more.

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

(This article belongs to the Special Issue Bio-Inspired Underwater Propulsion: Actuation, Sensing, Processing and Control)

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18 pages, 4265 KB

Open AccessArticle

Animal Species Classification from Vocalizations Using Cochlear-Inspired Audio Features and Machine Learning

by Karim Youssef, Julien Moussa H. Barakat, Ghina El Mir, Sherif Said, Samer Al Kork and Alaa Eleyan

Biomimetics 2025, 10(12), 830; https://doi.org/10.3390/biomimetics10120830 - 11 Dec 2025

Abstract

Biomimetic approaches have gained increasing attention in the development of efficient computational models for sound scene analysis. In this paper, we present a sound-based animal species classification method inspired by the auditory processing mechanisms of the human cochlea. The approach employs gammatone filtering [...] Read more.

Biomimetic approaches have gained increasing attention in the development of efficient computational models for sound scene analysis. In this paper, we present a sound-based animal species classification method inspired by the auditory processing mechanisms of the human cochlea. The approach employs gammatone filtering to extract features that capture the distinctive characteristics of animal vocalizations. While gammatone filterbanks themselves are well established in auditory signal processing, their systematic application and evaluation for animal vocalization classification represent the main contribution of this work. Four gammatone-based feature representations are explored and used to train and test an artificial neural network for species classification. The method is evaluated on a dataset comprising vocalizations from 13 animal species with 50 vocalizations per specie and 2.76 seconds per vocalization in average. The evaluations are conducted to study the system parameters in different conditions and system architectures. Although the dataset is limited in scale compared to larger public databases, the results highlight the potential of combining biomimetic cochlear filtering with machine learning to perform reliable and robust species classification through sound. Full article

(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)

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18 pages, 8716 KB

Open AccessArticle

Nanomechanical and Optical Properties of Anti-Counterfeiting Nanostructures Obtained by Hydrogel Photoresist in Laser Processing

by Wei Wu, Qingxue Deng, Yuhang Shi and Jiyu Sun

Biomimetics 2025, 10(12), 829; https://doi.org/10.3390/biomimetics10120829 - 11 Dec 2025

Abstract

The microstructures of living creatures are widely used in bionics, and some can generate structural colors on biological surfaces and enable the process of dynamic camouflage. This study presents the hydrogel photoresist synthesized by polymerizing HEMA and MMA in THF solvent with initiator [...] Read more.

The microstructures of living creatures are widely used in bionics, and some can generate structural colors on biological surfaces and enable the process of dynamic camouflage. This study presents the hydrogel photoresist synthesized by polymerizing HEMA and MMA in THF solvent with initiator AIBN. Then, nanostructured gratings were fabricated on the hydrogel photoresists via double-beam interference lithography, and were characterized by scanning electron microscopy, angle-resolved spectroscopy system, and nanoindentation for pattern characterization, and nanomechanical and optical performance, respectively. Under multi-angle incident light, the optical computation of gratings with different depths indicates that a shallow implicit grating does not affect its dynamic color-changing performance. It is established that the laser power of 500 mW, a first exposure time of 5 s, and a second exposure time of 3 s are feasible for achieving efficient anti-counterfeiting nanostructures. The L_500-5-3 has greater Er and H than that of L_500-5 with the second processing, but smaller than ineffective patterns. And the depth of anti-counterfeiting gratings that is less than 0.8 μm is conducive to obtaining anti-counterfeiting gratings with different size parameters. The acquired anti-counterfeiting nanostructures exhibit excellent stability, reliability, and angle-dependent color changes under room light, which provides promising applications for security materials in daily life, sensors, optics, and electronics. Full article

(This article belongs to the Special Issue Bionic Engineering Materials and Structural Design)

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16 pages, 1116 KB

Open AccessArticle

Performance of Hammerstein Spline Adaptive Filtering Based on Fair Cost Function for Denoising Electrocardiogram Signals

by Suchada Sitjongsataporn and Theerayod Wiangtong

Biomimetics 2025, 10(12), 828; https://doi.org/10.3390/biomimetics10120828 - 10 Dec 2025

Abstract

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a [...] Read more.

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals. Full article

(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025)

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20 pages, 3862 KB

Open AccessArticle

Hybrid ANFIS–MPA and FFNN–MPA Models for Bitcoin Price Forecasting

by Ceren Baştemur Kaya, Ebubekir Kaya and Eyüp Sıramkaya

Biomimetics 2025, 10(12), 827; https://doi.org/10.3390/biomimetics10120827 - 10 Dec 2025

Abstract

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple [...] Read more.

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)

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14 pages, 2239 KB

Open AccessArticle

Energy-Efficient Path Planning for Snake Robots Using a Deep Reinforcement Learning-Enhanced A* Algorithm

by Yang Gu, Zelin Wang and Zhong Huang

Biomimetics 2025, 10(12), 826; https://doi.org/10.3390/biomimetics10120826 - 10 Dec 2025

Abstract

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. [...] Read more.

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments. Full article

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19 pages, 8700 KB

Open AccessArticle

Human-Inspired Force–Motion Imitation Learning with Dynamic Response for Adaptive Robotic Manipulation

by Yuchuang Tong, Haotian Liu, Tianbo Yang and Zhengtao Zhang

Biomimetics 2025, 10(12), 825; https://doi.org/10.3390/biomimetics10120825 - 9 Dec 2025

Abstract

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper [...] Read more.

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper presents a bioinspired imitation learning framework that models human adaptive dynamics to jointly acquire and generalize motion and force skills, enabling compliant and resilient robot behavior. The proposed framework integrates hybrid force–motion learning with dynamic response mechanisms, achieving broad skill generalization without reliance on external sensing modalities. A momentum-based force observer is combined with dynamic movement primitives (DMPs) to enable accurate force estimation and smooth motion coordination, while a broad learning system (BLS) refines the DMP forcing function through style modulation, feature augmentation, and adaptive weight tuning. In addition, an adaptive radial basis function neural network (RBFNN) controller dynamically adjusts control parameters to ensure precise, low-latency skill reproduction, and safe physical interaction. Simulations and real-world experiments confirm that the proposed framework achieves human-like adaptability, robustness, and scalability, attaining a competitive learning time of 5.56 s and a rapid generation time of 0.036 s, thereby demonstrating its efficiency and practicality for real-time applications and offering a lightweight yet powerful solution for bioinspired intelligent control in complex and unstructured environments. Full article

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24 pages, 6350 KB

Open AccessArticle

Composites Derived from Aluminium-Modified Biphasic Calcium-Phosphate for Bone Regeneration

by Raluca Lucacel-Ciceo, Roxana Dudric, Razvan Hirian, Iulia Lupan, Oana Koblicska, Roxana Strimbu, Radu George Hategan, Dorina Simedru and Zorita Diaconeasa

Biomimetics 2025, 10(12), 824; https://doi.org/10.3390/biomimetics10120824 - 9 Dec 2025

Abstract

In this research, aluminium-doped biphasic calcium phosphate (Al-BCP) was synthesized by co-precipitation and formulated with hydrolyzed collagen and acetylsalicylic acid (ASA) to yield composites designed as a new class of bone-regenerative biomaterials with enhanced biological performance. Undoped and Al-modified powders (5/10 wt% Al [...] Read more.

In this research, aluminium-doped biphasic calcium phosphate (Al-BCP) was synthesized by co-precipitation and formulated with hydrolyzed collagen and acetylsalicylic acid (ASA) to yield composites designed as a new class of bone-regenerative biomaterials with enhanced biological performance. Undoped and Al-modified powders (5/10 wt% Al precursor) were prepared at 40 °C (pH ~ 11) and calcined at 700 °C, and composites were produced at a 1:1:0.1 mass ratio (ceramic–collagen–ASA). Structure and chemistry were assessed by X-ray diffraction (XRD), Fourier-transform infrared (FTIR) and Raman spectroscopies, and X-ray photoelectron spectroscopy (XPS). Morphology and elemental distribution were examined by scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX). Biological performance was preliminarily evaluated using HaCaT (immortalized human keratinocytes) viability and antibacterial assays against Staphylococcus aureus and Escherichia coli. XRD confirmed a biphasic hydroxyapatite/β-tricalcium phosphate system and showed that Al incorporation shifted the phase balance toward hydroxyapatite (HAp fraction 54.8% in BCP vs. ~68.6–68.7% in Al-doped samples). FTIR/Raman preserved BCP vibrational signatures and revealed collagen/ASA bands in the composites. XPS/EDX verified the expected composition, including surface N 1s from organics and Al at ~2–5 at% for doped samples, with surface Ca/P ≈ 1.15–1.16. SEM revealed multigranular microstructures with homogeneous Al distribution. All composites were non-cytotoxic (≥70% viability); M_Al10_Col_ASA exceeded 90% viability at 12.5% dilution. Preliminary antibacterial assays against Gram-positive and Gram-negative strains showed modest, time-dependent reductions in CFU relative to controls. These results corroborate the compositional/structural profile and preliminary biological performance of Al-BCP–collagen–ASA composites as multifunctional bone tissue engineering materials that foster a bone-friendly microenvironment, warranting further evaluation for bone regeneration. Full article

(This article belongs to the Special Issue Advances in Bioceramics for Bone Regeneration: 2nd Edition)

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32 pages, 4759 KB

Open AccessArticle

Development of a Bayesian Network and Information Gain-Based Axis Dynamic Mechanism for Ankle Joint Rehabilitation

by Huiguo Ma, Yuqi Bao, Jingfu Lan, Xuewen Zhu, Pinwei Wan, Raquel Cedazo León, Shuo Jiang, Fangfang Chen, Jun Kang, Qihan Guo, Peng Zhang and He Li

Biomimetics 2025, 10(12), 823; https://doi.org/10.3390/biomimetics10120823 - 9 Dec 2025

Abstract

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical [...] Read more.

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training. Full article

(This article belongs to the Special Issue Advanced Service Robots: Exoskeleton Robots 2025)

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17 pages, 1330 KB

Open AccessSystematic Review

Chitosan-Based Nanoparticles and Biomaterials for Pulp Capping and Regeneration: A Systematic Review with Quantitative and Evidence-Mapping Synthesis

by Saleh Ali Alqahtani, Mohammad Alamri, Ghadeer Alwadai, Naif N. Abogazalah, Vinod Babu Mathew and Betsy Joseph

Biomimetics 2025, 10(12), 822; https://doi.org/10.3390/biomimetics10120822 - 9 Dec 2025

Abstract

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and [...] Read more.

Preserving dental pulp vitality is a key goal in minimally invasive dentistry. Conventional materials such as calcium hydroxide and mineral trioxide aggregate (MTA) are effective but limited in bioactivity and mechanical strength. This systematic review evaluated the biological efficacy of chitosan-based nanoparticles and biomaterials for pulp capping and regeneration. Following PRISMA 2020 guidelines, electronic searches were conducted across five databases up to April 2025. Controlled in vitro and animal studies using chitosan-based nanoparticles, hydrogels, or composite scaffolds were included. Risk of bias was assessed using SYRCLE (animal) and ToxRTool (in vitro), and certainty of evidence was rated via the GRADE-Preclinical framework. Due to methodological heterogeneity, data were synthesized using direction-of-effect coding and visualized through Albatross and heatmap plots. Sixteen studies met the criteria, consistently demonstrating enhanced cell viability, mineralization, and upregulation of odontogenic and angiogenic markers (BMP-2, TGF-β₁, VEGF, DSPP) compared with MTA or calcium hydroxide. Animal models confirmed improved angiogenesis, reparative dentin formation, and pulp vitality preservation. Despite uniformly positive biological outcomes, the overall certainty was rated Low to Very Low owing to small samples and unclear randomization. Chitosan-based biomaterials show promising regenerative potential, warranting well-designed preclinical and clinical studies for translational validation. Full article

(This article belongs to the Section Biomimetics of Materials and Structures)

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14 pages, 1815 KB

Open AccessArticle

Bioactive Glass Modified by Sonochemistry Improves Peri-Implant Bone Repair in Ovariectomized Rats

by Marcelly Braga Gomes, Nathália Dantas Duarte, Gabriel Mulinari-Santos, Fábio Roberto de Souza Batista, Luy de Abreu Costa, Paulo Roberto Botacin, Paulo Noronha Lisboa-Filho and Roberta Okamoto

Biomimetics 2025, 10(12), 821; https://doi.org/10.3390/biomimetics10120821 - 8 Dec 2025

Abstract

Estrogen deficiency is a primary cause of osteoporosis, compromising bone mineral density that may impair peri-implant healing. Given the compromised bone environment associated with estrogen deficiency, strategies such as particle reduction via sonochemistry are promising approaches to enhance regenerative outcomes. However, its effects [...] Read more.

Estrogen deficiency is a primary cause of osteoporosis, compromising bone mineral density that may impair peri-implant healing. Given the compromised bone environment associated with estrogen deficiency, strategies such as particle reduction via sonochemistry are promising approaches to enhance regenerative outcomes. However, its effects in promoting bone formation remain insufficiently explored. Therefore, this study evaluated the potential of two sonicated biomaterials to improve peri-implant repair in ovariectomized rats. Fifty female rats were allocated into five groups: blood clot (CLOT), Biogran^® (BGN), sonicated Biogran^® (BGS), Bio-Oss^® (BON), and sonicated Bio-Oss^® (BOS). Tibial peri-implant defects were created 30 days after ovariectomy and analyzed 28 days later by removal torque, microcomputed tomography, and confocal microscopy. BGS exhibited the highest removal torque (6.28 Ncm), followed by BON (5.37 Ncm), BOS (3.92 Ncm), BGN (3.15 Ncm), and CLOT (2.58 Ncm). Micro-CT revealed bone volume fraction (BV/TV) values of 8.07% (CLOT), 6.47% (BOS), 6.02% (BGS), 5.55% (BGN), and 2.84% (BON). For the trabecular number (Tb.N), BGS (1.11 mm⁻¹) showed a significant increase compared with BGN (0.69 mm⁻¹), p < 0.05. These findings show that sonochemically modified bioactive glass improves mechanical stability and trabecular microarchitecture under estrogen-deficient conditions. However, further studies are needed to standardize sonication parameters for different biomaterials and expand their translational applicability. Full article

(This article belongs to the Special Issue Functional Biomimetic Materials and Devices for Biomedical Applications: 5th Edition)

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11 pages, 839 KB

Open AccessArticle

Step Timing Change over Time During Wearable Exoskeleton-Assisted Gait Training: A Cross-Sectional Study

by Tomohito Ito, Soichiro Koyama, Koki Tan and Shigeo Tanabe

Biomimetics 2025, 10(12), 820; https://doi.org/10.3390/biomimetics10120820 - 7 Dec 2025

Abstract

This study aimed to investigate the timing of foot-off and initial contact at the end of the first walking training session with a Wearable Power-Assist Locomotor (WPAL) in novice healthy users. Eight healthy volunteers with no walking experience with the WPAL participated in [...] Read more.

This study aimed to investigate the timing of foot-off and initial contact at the end of the first walking training session with a Wearable Power-Assist Locomotor (WPAL) in novice healthy users. Eight healthy volunteers with no walking experience with the WPAL participated in this study. The participants walked back and forth on a straight 5 m path for 60 min with the WPAL. We calculated the differences between the participant’s foot-off and initial contact timing, as well as the start and end timing of the pre-programmed WPAL lower-limb swing time. Data were divided into four segments of 100 data points. We calculated the median of the last 100 data points and examined whether it falls within an appropriate time range. The foot-off timing tended to be within the appropriate time range (median, −0.44 s); however, the initial contact timing was earlier than the appropriate time range (median, −0.17 s). Although some participants performed foot-off within the appropriate time range, all performed initial contact earlier than the appropriate time range. These findings may contribute to establishing practice protocols for stable walking with wearable robotic exoskeletons in patients with spinal cord injury. Full article

(This article belongs to the Special Issue Bionic Technology—Robotic Exoskeletons and Prostheses: 3rd Edition)

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17 pages, 583 KB

Open AccessSystematic Review

Smart Ring in Clinical Medicine: A Systematic Review

by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee and Gwang Ho Baik

Biomimetics 2025, 10(12), 819; https://doi.org/10.3390/biomimetics10120819 - 5 Dec 2025

Abstract

Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We [...] Read more.

Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We searched PubMed/MEDLINE, Embase, Cochrane Library, and Web of Science through 31 July 2025. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and RoB 2.0. Results: From 862 citations, 107 studies met inclusion criteria including approximately 100,000 participants. Studies were equally distributed between sleep (47.7%) and non-sleep applications (52.3%). Smart rings demonstrated high accuracy: heart rate r² = 0.996, heart rate variability r² = 0.980, and sleep detection 93–96% sensitivity. Predictive capabilities included COVID-19 detection 2.75 days pre-symptom (82% sensitivity), inflammatory bowel disease flare prediction 7 weeks early (72% accuracy), and bipolar episode detection 3–7 days early (79% sensitivity). However, 65% of studies had moderate-to-high bias risk. Limitations included small samples, proprietary algorithms (89%), poor diversity reporting (35%), and declining adherence (80% at 3 months to 43% at 12 months). Conclusion: Smart rings have evolved into clinical tools capable of early disease detection. However, algorithmic opacity, population homogeneity, and adherence challenges require attention before widespread implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)

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16 pages, 700 KB

Open AccessReview

Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies

by Kealan Westby, Daniel Westby, Kevin McKevitt and Brian M. Moloney

Biomimetics 2025, 10(12), 818; https://doi.org/10.3390/biomimetics10120818 - 5 Dec 2025

Abstract

Artificial intelligence (AI) is increasingly shaping interventional oncology, with growing interest in its application across thermal ablation modalities such as radiofrequency ablation (RFA), cryoablation, high-intensity focused ultrasound (HIFU), and microwave ablation (MWA). This review characterises the current landscape of AI-enhanced thermal ablation, with [...] Read more.

Artificial intelligence (AI) is increasingly shaping interventional oncology, with growing interest in its application across thermal ablation modalities such as radiofrequency ablation (RFA), cryoablation, high-intensity focused ultrasound (HIFU), and microwave ablation (MWA). This review characterises the current landscape of AI-enhanced thermal ablation, with particular emphasis on emerging opportunities within MWA technologies. We examine how AI-driven methods—convolutional neural networks, radiomics, and reinforcement learning—are being applied to optimise patient selection, automate image segmentation, predict treatment response, and support real-time procedural guidance. Comparative insights are provided across ablation modalities to contextualise the unique challenges and opportunities presented by microwave systems. Emphasis is placed on integrating AI into clinical workflows, ensuring safety, improving consistency, and advancing personalised therapy. Tables summarising AI methods and applications, a conceptual workflow figure, and a research gap analysis for MWA are included to guide future work. While existing applications remain largely investigational, the convergence of AI with advanced imaging and energy delivery holds significant promise for precision oncology. We conclude with a roadmap for research and clinical translation, highlighting the need for prospective validation, regulatory clarity, and interdisciplinary collaboration to support the adoption of AI-enabled thermal ablation into routine practice. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)

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24 pages, 17472 KB

Open AccessArticle

A Biomimetic Roll-Type Tactile Sensor Inspired by the Meissner Corpuscle for Enhanced Dynamic Performance

by Kunio Shimada

Biomimetics 2025, 10(12), 817; https://doi.org/10.3390/biomimetics10120817 - 5 Dec 2025

Cited by 1

Abstract

Highly sensitive bioinspired cutaneous receptors are essential for realistic human-robot interaction. This study presents a biomimetic tactile sensor morphologically modeled after the Meissner corpuscle, designed for high dynamic sensitivity achieved using a coiled configuration. Our proposed electrolytic polymerization technique with magnet-responsive hybrid fluid [...] Read more.

Highly sensitive bioinspired cutaneous receptors are essential for realistic human-robot interaction. This study presents a biomimetic tactile sensor morphologically modeled after the Meissner corpuscle, designed for high dynamic sensitivity achieved using a coiled configuration. Our proposed electrolytic polymerization technique with magnet-responsive hybrid fluid (HF) was employed to fabricate soft, elastic rubber sensors with embedded coiled electrodes. The coiled configuration, optimized by electrolytic polymerization, exhibited high responsiveness to dynamic motions including pressing, pinching, twisting, bending, and shearing. The mechanism of the haptic property was analyzed by electrochemical impedance spectroscopy (EIS), revealing that reactance variations define an equivalent electric circuit (EEC) whose resistance (R_p), capacitance (C_p), and inductance (L_p) change with applied force; these changes correspond to mechanical deformation and the resulting variation in the sensor’s built-in voltage. The roll-type Meissner-inspired sensor demonstrated fast-adapting behavior and broadband vibratory sensitivity, indicating its potential for high-performance tactile and auditory sensing. These findings confirm the feasibility of electrolytically polymerized hybrid fluid rubber as a platform for next-generation bioinspired haptic interfaces. Full article

(This article belongs to the Special Issue Smart Artificial Muscles and Sensors for Bio-Inspired Robotics)

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22 pages, 5627 KB

Open AccessReview

Biomimetic Artificial Muscles Inspired by Nature’s Volume-Change Actuation Mechanisms

by Hyunsoo Kim, Minwoo Kim, Yonghun Noh and Yongwoo Jang

Biomimetics 2025, 10(12), 816; https://doi.org/10.3390/biomimetics10120816 - 4 Dec 2025

Abstract

Artificial muscles translate the biological principles of motion into soft, adaptive, and multifunctional actuation. This review accordingly highlights research into natural actuation strategies, such as skeletal muscles, muscular hydrostats, spider silk, and plant turgor systems, to reveal the principles underlying energy conversion and [...] Read more.

Artificial muscles translate the biological principles of motion into soft, adaptive, and multifunctional actuation. This review accordingly highlights research into natural actuation strategies, such as skeletal muscles, muscular hydrostats, spider silk, and plant turgor systems, to reveal the principles underlying energy conversion and deformation control. Building on these insights, polymer-based artificial muscles based on these principles, including pneumatic muscles, dielectric elastomers, and ionic electroactive systems, are described and their capabilities for efficient contraction, bending, and twisting with tunable stiffness and responsiveness are summarized. Furthermore, the abilities of carbon nanotube composites and twisted yarns to amplify nanoscale dimensional changes through hierarchical helical architectures and achieve power and work densities comparable to those of natural muscle are discussed. Finally, the integration of these actuators into soft robotic systems is explored through biomimetic locomotion and manipulation systems ranging from jellyfish-inspired swimmers to octopus-like grippers, gecko-adhesive manipulators, and beetle-inspired flapping wings. Despite rapid progress in the development of artificial muscles, challenges remain in achieving long-term durability, energy efficiency, integrated sensing, and closed-loop control. Therefore, future research should focus on developing intelligent muscular systems that combine actuation, perception, and self-healing to advance progress toward realizing autonomous, lifelike machines that embody the organizational principles of living systems. Full article

(This article belongs to the Special Issue Bionic Technology—Robotic Exoskeletons and Prostheses: 3rd Edition)

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22 pages, 2215 KB

Open AccessArticle

Robot-Assisted Dynamic Interaction of Hemiplegic Upper Limbs with Complex Objects Based on Enhanced Feedforward-Impedance Control

by Jing Bai, Ruoyi Zhu, Yicheng Jiang and Xiaofei Du

Biomimetics 2025, 10(12), 815; https://doi.org/10.3390/biomimetics10120815 - 4 Dec 2025

Abstract

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients’ high-level sensorimotor integration capabilities. To address [...] Read more.

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients’ high-level sensorimotor integration capabilities. To address this gap, this study proposes an innovative robotic rehabilitation training system designed for functional occupational therapy. Specifically, the task of transporting a water cup was abstracted into a cup–ball system integrated with a robotic arm. The ball was modeled as a point mass, and kinematic and dynamic analyses of the system were conducted. A visual tracking method was employed to monitor the ball’s motion and calculate its slosh angle. Owing to the impaired fine motor control in stroke patients, a sloshing suppression control strategy integrating exponential filtering, feedforward force compensation, and impedance control was proposed to prevent the ball from spilling. Experiments validated the effectiveness of the proposed method. The results indicated that with full compensation, the oscillation rate of the ball was significantly reduced, and the smoothness of the hand force was markedly improved. This study presents an effective method for addressing dynamic uncertainty in rehabilitation robot training, thus significantly improving the functional relevance of the training. Full article

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