Bone Structure Research Articles

Enhanced osteogenic differentiation for osteoporosis treatment through controlled icariin release in the bone cavity via extracorporeal shock wave

Controlling drug release from the bone cavity through physical stimulation remains a challenge because the unique shielding properties of the bone structure make it difficult for many physical stimuli to be effective in skeletal disorders. Herein, we designed a type of high-loading nanocomposites self-assembled from Icariin (ICA), Ca2+, and Zoledronic acid (ZOL), which could respond to extracorporeal shock wave (ESW) to control drug release in the bone cavity and govern osteoblast-adipocyte lineage commitment to prevent osteoporotic fracture. The nanocomposites contain more than 70 % of the payloads and exhibit excellent function in bone marrow targeting. When shocked with ESW, the payloads including ICA, Ca2+, and ZOL, can be released in the bone marrow. ICA can inhibit the formation of adipocyte lineages and promote the formation of osteoblast lineages by inhibiting the transcription of peroxisome proliferators-activated receptor γ (PPARγ) and fatty acid-binding protein 4 (FABP4), ESW, and Ca2+ further increase osteoblast activity meanwhile, and ZOL acts as an inhibitor of osteoclasts, leading to an overall anabolism. In vivo, the treatment contributed to a significant increase in bone anabolism, including bone-related parameters, bone strength, and bone resilience, ultimately greatly reducing the risk of osteoporotic fracture. This study demonstrated the high feasibility of combining the bone-penetrating ESW-responsive drug-controlled release system with the regulation of osteoblast-adipocyte lineage commitment for osteoporotic fracture prevention.

Effects of Endocrine-Disrupting Chemicals on Bone Health.

This comprehensive review critically examines the detrimental impacts of endocrine-disrupting chemicals (EDCs) on bone health, with a specific focus on substances such as bisphenol A (BPA), per- and polyfluoroalkyl substances (PFASs), phthalates, and dioxins. These EDCs, by interfering with the endocrine system's normal functioning, pose a significant risk to bone metabolism, potentially leading to a heightened susceptibility to bone-related disorders and diseases. Notably, BPA has been shown to inhibit the differentiation of osteoblasts and promote the apoptosis of osteoblasts, which results in altered bone turnover status. PFASs, known for their environmental persistence and ability to bioaccumulate in the human body, have been linked to an increased osteoporosis risk. Similarly, phthalates, which are widely used in the production of plastics, have been associated with adverse bone health outcomes, showing an inverse relationship between phthalate exposure and bone mineral density. Dioxins present a more complex picture, with research findings suggesting both potential benefits and adverse effects on bone structure and density, depending on factors such as the timing and level of exposure. This review underscores the urgent need for further research to better understand the specific pathways through which EDCs affect bone health and to develop targeted strategies for mitigating their potentially harmful impacts.

Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network.

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network's recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.

Tracking of Infectious Diseases and Deadly Injuries through Signs Observed in Excavated Human Skeletons of 2000 BC/Iron Age in Iran.

Throughout history, many wars have occurred for various reasons, and many empires and kings have fallen or many people killed by wars. Wars were not always due to the conquest of the country. in the Iron Age, societies were governed by tribes at the head of the tribe, and war was only for to seize property, slaves, and food. Our research area is the same period as the Medes Kingdom, which included the union of small, large tribes, wars between tribes existed in that period, and their signs can be seen on the remains of the people of that period. Our research is related to human remains from Sagezabad cemetery, Qazvin plain, which dates back to 2000 BC (Iron Age 2 and 3) in Iran. The blows on the remains were very serious and caused death. We have discussed how to kill by "considering the injured body". Our investigation of how people were killed in war based on injury marks and bullet holes in bones, and simulating those injuries to body tissues and organs also, people who had bone cuts from the war and survived and had bone repair and died due to lack of nutrients and infection were also discussed.

Surgical treatment of L5 spondylolysis in an athlete using custom-made implant

Background: Spondylolysis is one of the most common causes of lower back pain in children and adolescents who are professionally involved in sports. It is noted that spondylolysis is observed more often when practicing a number of sports that are associated with repeated axial load and/or hyperextension of the lumbar spine with rotation. In most cases, the treatment of spondylolysis, including cases of its occurrence in professional athletes, is conservative. Surgical treatment is indicated only if conservative treatment is ineffective or if symptoms progress. One of the most common methods of surgical treatment of spondylolysis is to restore the integrity of the arch using various metal structures. The use of additive methods for the manufacture of individual implants currently allows the manufacture of personalized implants with a number of advantages. The article describes the first experience of using an individual implant for surgical treatment of spondylolysis and provides a brief review of the literature. Clinical case description: A clinical case is presented involving the treatment of a 16-year-old female patient who is a professional gymnast. The report includes a description of the patient’s medical history, clinical manifestations, and specialized diagnostic methods. The preoperative planning, design of a custom implant, the surgical procedure, and long-term treatment outcomes are detailed. A brief literature review highlights the results of conservative treatment, the main indications and methods of surgical therapy for spondylolysis, and justifies the use of a custom-made implant for its surgical treatment. CONCLUSION: For the surgical treatment of L5 spondylolysis and restoration of vertebral arch integrity without limiting motion on L5-S1 level, the use of a custom-made implant is possible. The use of customized implants may improve outcomes in cases where spondylolysis is combined with abnormalities and individual characteristics of the vertebral bone structures, including the patient’s sports activity.

Assessment of biomechanical stability of the thoracolumbar junction with a burst fracture of Th12 following surgical stabilization under rotational loading

The thoracolumbar junction is the most vulnerable to traumatic injuries, with over 65 % of injuries to the thoracolumbar spine occurring in this region. Objective: To examine the stress-strain state of the thoracolumbar spine model with a burst fracture of the Th12 vertebra under various transpedicular fixation options influenced by rotational loading. Materials and Methods: A mathematical finite-element model of the human thoracolumbar spine was developed, including a burst fracture of the Th12 vertebra and a transpedicular stabilization system containing eight screws implanted in the Th10, Th11, L1, and L2 vertebrae. Four variants of transpedicular fixation were modelled using short and long screws passing through the anterior surface of the vertebra, with and without two crosslinks. Results: The analysis showed sufficiently high loading values for both the bone structures of the models and the elements of the metal construct. The maximum stress level in the body of the damaged vertebra was 33.2, 26.7, 30.1, and 24.2 MPa, respectively, for models with monocortical screws without crosslinks, bicortical screws without crosslinks, monocortical screws with crosslinks, and bicortical screws with crosslinks. High values were also recorded for the vertebrae adjacent to the damaged one: 13.0, 8.4, 10.9, and 7.1 MPa for the L1 vertebra and 10.2, 8.9, 7.1, and 6.2 MPa for the Th11 vertebra in the respective models. The stress on the supporting rods was registered at 582.0, 512.5, 512.6, and 452.7 MPa respectively. Conclusion: The conducted analysis demonstrated that under rotational loading, the model with monocortical screws without crosslinks shows the highest peak loads at control points, whereas the model with bicortical screws and crosslinks shows the minimum. Meanwhile, models with short screws and crosslinks and long screws without crosslinks exhibit comparable results

Current Perspectives of Protein in Bone Tissue Engineering: Bone Structure, Ideal Scaffolds, Fabrication Techniques, Applications, Scopes, and Future Advances.

In view of their exceptional approach, excellent inherent biocompatibility and biodegradability properties, and interaction with the local extracellular matrix, protein-based polymers have received attention in bone tissue engineering, which is a multidisciplinary field that repairs and regenerates fractured bones. Bone is a multihierarchical complex structure, and it performs several essential biofunctions, including maintaining mineral balance and structural support and protecting soft organs. Protein-based polymers have gained interest in developing ideal scaffolds as emerging biomaterials for bone fractured healing and regeneration, and it is challenging to design ideal bone substitutes as perfect biomaterials. Several protein-based polymers, including collagen, keratin, gelatin, serum albumin, etc., are potential materials due to their inherent cytocompatibility, controlled biodegradability, high biofunctionalization, and tunable mechanical characteristics. While numerous studies have indicated the encouraging possibilities of proteins in BTE, there are still major challenges concerning their biodegradability, stability in physiological conditions, and continuous release of growth factors and bioactive molecules. Robust scaffolds derived from proteins can be used to replace broken or diseased bone with a biocompatible substitute; proteins, being biopolymers, provide excellent scaffolds for bone tissue engineering. Herein, recent developments in protein polymers for cutting-edge bone tissue engineering are addressed in this review within 3-5 years, with a focus on the significant challenges and future perspectives. The first section discusses the structural fundamentals of bone anatomy and ideal scaffolds, and the second section describes the fabrication techniques of scaffolds. The third section highlights the importance of proteins and their applications in BTE. Hence, the recent development of protein polymers for state-of-the-art bone tissue engineering has been discussed, highlighting the significant challenges and future perspectives.

Experimental implementation of skeleton tracking for collision avoidance in collaborative robotics

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.23\\%$$\\end{document} for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$12.23\\%$$\\end{document} for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$208\\,\ extrm{mm}$$\\end{document}, being 200mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$200\\,\ extrm{mm}$$\\end{document} the limit safety distance set by algorithm.

Data-driven image mechanics (D2IM): A deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images – Evaluation of bone mechanics

The recent advent of deep learning (DL) has enabled data-driven models to pave the way for the full exploitation of rich image datasets from which physics can be learnt. Here we propose a novel data-driven image mechanics (D2IM) approach that learns from digital volume correlation (DVC) displacement fields of vertebrae, predicting displacement and strain fields from undeformed X-ray computed tomography (XCT) images. D2IM successfully predicted the displacements in all directions, particularly in the cranio-caudal direction of the vertebra, where high correlation (R2=0.94) and generally minimal errors were obtained compared to the measured displacements. The predicted axial strain field in the cranio-caudal direction of the vertebra was also consistent in distribution with the measured one, displaying generally reduced errors in the regions within the vertebral body. The application of D2IM to lower resolution imaging in initial testing provides promising results indicating the future viability of integrating this technology into a clinical setting. This is the first study using experimental full-field measurements on bone structures from DVC to inform DL-based models such as D2IM, which represents a major contribution in the prediction of displacement and strain fields based only on the greyscale content of undeformed XCT images. In future, D2IM will incorporate a range of biological structures and loading scenarios for accurate prediction of physical fields, aiming at clinical translation for improved diagnostics. Data AvailabilityCode for preparing dataset, training D2IM model and visualising/analysing results has been hosted on GitHub: https://github.com/PeterSoar/D2IM_PrototypeThe dataset used for this study can be found on Figshare: https://doi.org/10.6084/m9.figshare.25404220.v1

Evaluation of fluoride exposure using disability-adjusted life years and health risk assessment in south-western Iran: A novel Monte Carlo simulation

Consumption of fluoride-contaminated water is a worldwide concern, especially in developing countries, including Iran. However, there are restricted studies of non-single-value health risk assessment and the disease burden regarding fluoride intake nationwide. Prolonged exposure to excessive fluoride has been linked to adverse health effects such as dental and skeletal fluorosis. This can lead to under-mineralization of hard tissues, causing aesthetic concerns for teeth and changes in bone structure, increasing the risk of fractures. As such, we aimed to implement probability-based frameworks using Monte Carlo methods to explore the potential adverse effects of fluoride via the ingestion route. This platform consists of two sectors: 1) health risk assessment of various age categories coupled with a variance decomposition technique to measure the contributions of predictor variables in the outcome of the health risk model, and 2) implementing Monte Carlo methods in dose-response curves to explore the fluoride-induced burden of diseases of dental fluorosis and skeletal fractures in terms of disability-adjusted life years (DALYs). For this purpose, total water samples of 8053 (N=8053) from 57 sites were analyzed in Fars and Bushehr Provinces. The mean fluoride concentrations were 0.75 mg/L and 1.09 mg/L, with maximum fluoride contents of 6.5 mg/L and 3.22 mg/L for the Fars and Bushehr provinces, respectively. The hazard quotient of the 95th percentile (HQ>1) revealed that all infants and children in the study area were potentially vulnerable to over-receiving fluoride. Sobol’ sensitivity analysis indices, including first-order, second-order, and total order, disclosed that fluoride concentration (Cw), ingestion rate (IRw), and their mutual interactions were the most influential factors in the health risk model. DALYs rate of dental fluorosis was as high as 981.45 (uncertainty interval: UI 95 % 353.23–1618.40) in Lamerd, and maximum DALYs of skeletal fractures occurred in Mohr 71.61(49.75–92.71), in Fars Province, indicated severe dental fluorosis but mild hazard regarding fractures. Residents of the Tang-e Eram in Bushehr Province with a DALYs rate of 3609.40 (1296.68–5993.73) for dental fluorosis and a DALYs rate of 284.67 (199.11–367.99) for skeletal fractures were the most potentially endangered population. By evaluating the outputs of the DALYs model, the gap in scenarios of central tendency exposure and reasonable maximum exposure highlights the role of food source intake in over-receiving fluoride. This research insists on implementing defluoridation programs in fluoride-endemic zones to combat the undesirable effects of fluoride. The global measures presented in this research aim to address the root causes of contamination and help policymakers and authorities mitigate fluoride's harmful impacts on the environment and public health.

2D human skeleton action recognition with spatial constraints

AbstractHuman actions are predominantly presented in 2D format in video surveillance scenarios, which hinders the accurate determination of action details not apparent in 2D data. Depth estimation can aid human action recognition tasks, enhancing accuracy with neural networks. However, reliance on images for depth estimation requires extensive computational resources and cannot utilise the connectivity between human body structures. Besides, the depth information may not accurately reflect actual depth ranges, necessitating improved reliability. Therefore, a 2D human skeleton action recognition method with spatial constraints (2D‐SCHAR) is introduced. 2D‐SCHAR employs graph convolution networks to process graph‐structured human action skeleton data comprising three parts: depth estimation, spatial transformation, and action recognition. The initial two components, which infer 3D information from 2D human skeleton actions and generate spatial transformation parameters to correct abnormal deviations in action data, support the latter in the model to enhance the accuracy of action recognition. The model is designed in an end‐to‐end, multitasking manner, allowing parameter sharing among these three components to boost performance. The experimental results validate the model's effectiveness and superiority in human skeleton action recognition.

Transdermal delivery of iguratimod and colchicine ethosome by dissolving microneedle patch for the treatment of recurrent gout

This study introduces a novel approach of repetitive modeling to simulate the pathological process of recurrent gout attacks in humans. This methodology addresses the instability issues present in rat models of gout, providing a more accurate representation of the damage recurrent gout episodes inflict on human skeletal systems. A soluble nanoneedle system encapsulating colchicine and iguratimod ethosomal formulations was developed. This system aims to modulate inflammatory cytokines and inhibit osteoclast activity, thereby treating inflammatory pain and bone damage associated with recurrent gout. Additionally, a comprehensive evaluation of the microneedles' appearance, morphology, mechanical properties, and penetration capability confirmed their effectiveness in penetrating the stratum corneum. Dissolution tests and skin irritation assessments demonstrated that these microneedles dissolve rapidly without irritating the skin. In vitro permeation studies indicated that transdermal drug delivery via these microneedles is more efficient and incurs lower drug loss compared to traditional topical applications. In vivo pharmacodynamic assessments conducted in animal models revealed significant analgesic and anti-inflammatory effects when both types of microneedles were used together. Further analyses, including X-ray imaging, hematoxylin and eosin (H&E) staining, Safranin-O/fast green staining, tartrate-resistant acid phosphatase staining, and quantification of osteoclasts, confirmed the bone-protective effects of the microneedle combination. In conclusion, the findings of this research underscore the potential of this novel therapeutic approach for clinical application in the treatment of recurrent gout.

Hierarchically porous bone scaffold fabricated via direct foam writing with TCP/ZrO2 composite ink

Customizing bone scaffolds to mimic the hierarchically porous structure of natural bone presents a promising strategy for treating critical bone defects. Although low-cost direct ink writing (DIW) is often used to customize bone scaffolds, the limited print resolution made it difficult to print microporous structures. In this study, DIW was combined with the direct foam method to construct bone scaffolds with hierarchical and interconnected macro-micro porous structures. The foam stabilization mechanism of pure TCP was investigated to prepare TCP-enhanced ZrO2-based composite foam inks. Incorporating TCP endowed a richer interconnection structure to the composite foam. The printed bone scaffolds feature three scales of pore structure: interconnected macropores of 400–500 μm, bubble template pores of 50 μm, and open pores of <20 μm. The scaffold exhibited properties comparable to cancellous bone, such as 90 % porosity, 3.1 MPa compressive strength, and 1.0 GPa elastic modulus. In vitro experiments have demonstrated that cells can grow into the interior of the scaffold by following the connecting pores. And, incorporating TCP significantly enhances osteogenic protein and gene expression in bionic bone scaffolds. This facile and controllable approach offers an alternative solution to addressing the challenge of clinical implant shortages.

Graphene Oxide Nanosheets for Bone Tissue Regeneration.

Bone tissue engineering is a promising alternative to repair wounds caused by cellular or physical accidents that humans face daily. In this sense, the search for new graphene oxide (GO) nanofillers related to their degree of oxidation is born as an alternative bioactive component in forming new scaffolds. In the present study, three different GOs were synthesized with varying degrees of oxidation and studied chemically and tissue-wise. The oxidation degree was determined through infrared (FTIR), X-ray diffraction (XRD), X-ray photoelectron (XPS), and Raman spectroscopy (RS). The morphology of the samples was analyzed using scanning electron microscopy (SEM). The oxygen content was deeply described using the deconvolution of RS and XPS techniques. The latter represents the oxidation degree for each of the samples and the formation of new bonds promoted by the graphitization of the material. In the RS, two characteristic bands were observed according to the degree of oxidation and the degree of graphitization of the material represented in bands D and G with different relative intensities, suggesting that the samples have different crystallite sizes. This size was described using the Tuinstra-Koenig model, ranging between 18.7 and 25.1 nm. Finally, the bone neoformation observed in the cranial defects of critical size indicates that the F1 and F2 samples, besides being compatible and resorbable, acted as a bridge for bone healing through regeneration. This promoted healing by restoring bone and tissue structure without triggering a strong immune response.

GR‐Former: Graph‐reinforcement transformer for skeleton‐based driver action recognition

AbstractIn in‐vehicle driving scenarios, composite action recognition is crucial for improving safety and understanding the driver's intention. Due to spatial constraints and occlusion factors, the driver's range of motion is limited, thus resulting in similar action patterns that are difficult to differentiate. Additionally, collecting skeleton data that characterise the full human posture is difficult, posing additional challenges for action recognition. To address the problems, a novel Graph‐Reinforcement Transformer (GR‐Former) model is proposed. Using limited skeleton data as inputs, by introducing graph structure information to directionally reinforce the effect of the self‐attention mechanism, dynamically learning and aggregating features between joints at multiple levels, the authors’ model constructs a richer feature vector space, enhancing its expressiveness and recognition accuracy. Based on the Drive &amp; Act dataset for composite action recognition, the authors’ work only applies human upper‐body skeleton data to achieve state‐of‐the‐art performance compared to existing methods. Using complete human skeleton data also has excellent recognition accuracy on the NTU RGB + D‐ and NTU RGB + D 120 dataset, demonstrating the great generalisability of the GR‐Former. Generally, the authors’ work provides a new and effective solution for driver action recognition in in‐vehicle scenarios.

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review.

This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans. PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings. Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection. The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.

The hyaluronic acid-gelatin hierarchical hydrogel for osteoporotic bone defect repairment

Osteoporotic bone defects are serious medical problems due to their sparse bone structure, difficulty in restoration and reconstruction, and high recurrence rates, which also result in heavy economic and social burdens. Herein, we developed a hierarchical hydrogel composed of alendronate sodium (AS)/Mg2+-loaded inverse opal methylpropenylated gelatin (GelMA) hydrogel microspheres (IOHM-AS-Mgs) within methylpropenylated poly(hyaluronic acid) (HAMA) for osteoporotic bone defect treatment. The IOHM-AS-Mgs displayed good cytocompatibility and cell adhesion and strongly stimulated osteogenesis at the transcriptomic and protein levels. When this treatment was applied to the osteoporotic bone defect area, HAMA was used to fix the microspheres. The results of the microcomputed tomography (micro-CT) and histological analyses indicated that the hierarchical hydrogel had the best therapeutic effect. Therefore, this hydrogel is a new candidate for osteoporotic bone defect treatment.

Human movement science-informed multi-task spatio temporal graph convolutional networks for fitness action recognition and evaluation

In recent years, with the rise of health consciousness, people’s demand for fitness has steadily increased. Utilizing automated human action recognition technology to monitor users’ movements during exercise continuously would help prevent situations where incorrect movements lead to injuries while working out. Skeleton-based human action recognition methods can overcome the susceptibility of past color-based and depth-based methods to various external backgrounds and noise, becoming a more successful solution in recent years. In this study, the auto-fitness advisor system we propose not only identifies the category of the action but also assesses the quality of the action and provides suggestions. We integrate human movement science, such as the Five Primary Kinetic Chains (5PKC), which defines the primary physiological principles in human movement, to enhance the accuracy of fitness action recognition by providing a more precise relationship between the human skeleton and muscles. For assessing the quality of movements and providing suggestions, we have designed a multi-task objective function within our model. Overall, the proposed model is a multi-task model based on Spatio Temporal Graph Convolutional Networks (ST-GCN), which employs the Five Primary Kinetic Chains (5PKC) as a partitioning strategy for skeletal information. In our experiments, we not only collected a certain amount of datasets in gyms to validate the performance of our model but also compared it with other current methods using existing public datasets.

Computational comparison study of virtual compression and shear test for estimation of apparent elastic moduli under various boundary conditions.

Virtual compression tests based on finite element analysis are representative noninvasive methods to evaluate bone strength. However, owing to the characteristic porous structure of bones, the material obtained from micro-computed tomography images in the finite-element model is not uniformly distributed. These characteristics cause differences in the apparent elastic moduli depending on the boundary conditions and affect the accuracy of bone-strength evaluation. Therefore, this study aimed to evaluate and compare the apparent elastic moduli under various, virtual-compression and shear-test boundary conditions. Four, nonuniform models were constructed with increasing model complexity. For representative boundary conditions, two, different, testing directions, and constrained surfaces were applied. As a result, the apparent elastic moduli of the nonuniform model varied up to 55.2% based on where the constrained surface was located in the single-end-cemented condition. Additionally, when connectivity in the test direction was lost, the accuracy of the apparent elastic moduli was low. A graphical comparison showed that the equivalent-stress distribution was more advantageous for analyzing load transferability and physical behavior than the strain-energy distribution. These results clearly show that the prediction accuracy of the apparent elastic moduli can be guaranteed if the boundary condition on the constraint and loading surfaces of the nonuniform model are applied symmetrically and the connectivity of the elements in the testing direction is well maintained. This study will aid in precision improvement of bone-strength-indicator determination for osteoporosis prevention.

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture. To evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA). Using QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture. Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76). This pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.