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Bone has a special structure that is both stiff and elastic, and the composition of bone confers it with an exceptional mechanical property. However, bone substitute materials that are made of the same hydroxyapatite (HA) and collagen do not offer the same mechanical properties. It is important for bionic bone preparation to understand the structure of bone and the mineralization process and factors. In this paper, the research on the mineralization of collagen is reviewed in terms of the mechanical properties in recent years. Firstly, the structure and mechanical properties of bone are analyzed, and the differences of bone in different parts are described. Then, different scaffolds for bone repair are suggested considering bone repair sites. Mineralized collagen seems to be a better option for new composite scaffolds. Last, the paper introduces the most common method to prepare mineralized collagen and summarizes the factors influencing collagen mineralization and methods to analyze its mechanical properties. In conclusion, mineralized collagen is thought to be an ideal bone substitute material because it promotes faster development. Among the factors that promote collagen mineralization, more attention should be given to the mechanical loading factors of bone.

High-Intensity Focused Ultrasound (HIFU) is a non-invasive technology widely used in aesthetic dermatology for skin tightening and facial rejuvenation. This study aimed to evaluate the safety and efficacy of a modified HIFU device, More Young, compared to the standard 7D HIFU system through a randomized, single-blinded clinical trial. The More Young device features enhanced focal depth precision and energy delivery algorithms, including nine pre-programmed stabilization checkpoints to minimize treatment risks. A total of 100 participants with facial wrinkles and skin laxity were randomly assigned to receive either More Young or 7D HIFU treatment. Skin improvements were assessed at baseline and one to six months post-treatment using the VISIA® Skin Analysis System (7th Generation), focusing on eight key parameters. Patient satisfaction was evaluated through the Global Aesthetic Improvement Scale (GAIS). Data were analyzed using paired and independent t-tests, with effect sizes measured via Cohen’s d. Both groups showed significant post-treatment improvements; however, the More Young group demonstrated superior outcomes in wrinkle reduction, skin tightening, and texture enhancement, along with higher satisfaction and fewer adverse effects. No significant differences were observed in five of the eight skin parameters. Limitations include the absence of a placebo group, limited sample diversity, and short follow-up duration. Further studies are needed to validate long-term outcomes and assess performance across varied demographics and skin types.

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase–amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Highlights This review categorically analyzes the state of the art of the structural complexity of melt electrowriting (MEW) scaffolds, ranging from 1D, 2D to 3D architectures, and presents advanced strategies to enhance scaffold quality. This review systematically elucidates the principles of MEW-based 4D printing, including material considerations, actuation methods, and structure design strategies, along with shape programming and morphing mechanisms. This review highlights the advances of MEW 4D scaffolds in tissue engineering, personalized biomedical implants, and drug delivery systems. Melt electrowriting (MEW) enables the precise deposition of polymeric fibers at micro-/nanoscale, allowing for the fabrication of 3D biomimetic scaffolds. By incorporating stimuli-responsive polymers and/or functional fillers, MEW-based 4D printing creates scaffolds capable of undergoing controlled, reversible shape transformations in response to external stimuli over time. These dynamic 4D scaffolds can be tailored for minimally invasive delivery, remote actuation, and real-time responsiveness to physiological environments, making them highly relevant for biomedical applications. This review systematically elucidates the principles of MEW-based 4D printing, including material considerations, actuation methods, and structure design strategies, along with shape programming and morphing mechanisms. The versatility of MEW for rational fabrication of biomimetic scaffolds is firstly introduced. Subsequently, the critical elements underpinning MEW-based 4D printing process are overviewed, including an analysis of stimuli-responsive materials compatible with MEW, an evaluation of applicable external stimuli, and a discussion on the advancements in design strategies for 4D scaffolds. Recent progress of MEW 4D scaffolds for applications in tissue engineering, biomedical implants, and drug delivery systems are highlighted. Finally, key challenges and perspectives toward material innovation, fabrication optimization, and actuation control are discussed. This review aims to provide valuable insights for design and creation of multifunctional biomimetic dynamic scaffolds by MEW-based 4D printing.

The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.Anzai et al. propose a deep learning approach to estimate the 3D hemodynamics of complex aorta-coronary artery geometry in the context of coronary artery bypass surgery. Their method reduces the calculation time 600-fold, while allowing high resolution and similar accuracy as traditional computational fluid dynamics (CFD) method.

Active artificial bone substitutes are crucial in bone repair and reconstruction. Calcium phosphate bone cement (CPC) is known for its biocompatibility, degradability, and ability to fill various shaped bone defects. However, its low osteoinductive capacity limits bone regeneration applications. Effectively integrating osteoinductive magnesium ions with CPC remains a challenge. Herein, we developed magnesium malate-modified CPC (MCPC). Incorporating 5% magnesium malate significantly enhances the compressive strength of CPC to (6.18 ± 0.49) MPa, reduces setting time and improves disintegration resistance. In vitro, MCPC steadily releases magnesium ions, promoting the proliferation of MC3T3-E1 cells without causing significant apoptosis, proving its biocompatibility. Molecularly, magnesium malate prompts macrophages to release prostaglandin E2 (PGE2) and synergistically stimulates dorsal root ganglion (DRG) neurons to synthesize and release calcitonin gene-related peptide (CGRP). The CGRP released by DRG neurons enhances the expression of the key osteogenic transcription factor Runt-related transcription factor-2 (RUNX2) in MC3T3-E1 cells, promoting osteogenesis. In vivo experiments using minipig vertebral bone defect model showed MCPC significantly increases the bone volume fraction, bone density, new bone formation, and proportion of mature bone in the defect area compared to CPC. Additionally, MCPC group exhibited significantly higher levels of osteogenesis and angiogenesis markers compared to CPC group, with no inflammation or necrosis observed in the hearts, livers, or kidneys, indicating its good biocompatibility. In conclusion, MCPC participates in the repair of bone defects in the complex post-fracture microenvironment through interactions among macrophages, DRG neurons, and osteoblasts. This demonstrates its significant potential for clinical application in bone defect repair.

The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections.

In this paper, we study the dynamic behavior of a stochastic tungiasis model for public health education. First, the existence and uniqueness of global positive solution of stochastic models are proved. Secondly, by constructing Lyapunov function and using Ito^ formula, sufficient conditions for disease extinction and persistence in the stochastic model are proved. Thirdly, under the condition of disease persistence, the existence and uniqueness of an ergodic stationary distribution of the model is obtained. Finally, the importance of public health education in preventing the spread of tungiasis is illustrated through the combination of theoretical results and numerical simulation.

Coronary artery aneurysms (CAAs) have been reported to associate with an increased risk for thrombosis. Distinct to the brain aneurysm, which can cause a rupture, CAA’s threat is more about its potential to induce thrombosis, leading to myocardial infarction. Case reports suggest that thrombosis risk varied with the different CAA diameters and hemodynamics effects (usually wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT)) may relate to the thrombosis risk. However, currently, due to the rareness of the disease, there is limited knowledge of the hemodynamics effects of CAA. The aim of the study was to estimate the relationship between hemodynamic effects and different diameters of CAAs. Computational fluid dynamics (CFD) provides a noninvasive means of hemodynamic research. Four three-dimensional models were constructed, representing coronary arteries with a normal diameter (1x) and CAAs with diameters two (2x), three (3x), and five times (5x) that of the normal diameter. A lumped parameter model (LPM) which can capture the feature of coronary blood flow supplied the boundary conditions. WSS in the aneurysm decreased 97.7% apparently from 3.51 Pa (1x) to 0.08 Pa (5x). OSI and RRT in the aneurysm were increased apparently by two orders of magnitude from 0.01 (1x) to 0.30 (5x), and from 0.38 Pa−1 (1x) to 51.59 Pa−1 (5x), separately. Changes in the local volume of the CAA resulted in dramatic changes in local hemodynamic parameters. The findings demonstrated that thrombosis risk increased with increasing diameter and was strongly exacerbated at larger diameters of CAA. The 2x model exhibited the lowest thrombosis risk among the models, suggesting the low-damage (medication) treatment may work. High-damage (surgery) treatment may need to be considered when CAA diameter is 3 times or higher. This diameter classification method may be a good example for constructing a more complex hemodynamic-based risk stratification method and could support clinical decision-making in the assessment of CAA.

In the existing patency prediction model of coronary artery bypass grafting (CABG), the characteristics are based on graft flow, but no researchers selected hemodynamic factors as the characteristics. The purpose of this paper is to study whether the introduction of hemodynamic factors will affect the performance of the prediction model. Transit time flow-meter (TTFM) waveforms and 1-year postoperative patency results were obtained from 50 internal mammary arterial grafts (LIMA) and 82 saphenous venous grafts (SVG) in 60 patients. Taking TTFM waveforms as the boundary conditions, the CABG ideal models were constructed to obtain hemodynamic factors in grafts. Based on clinical characteristics and combination of clinical and hemodynamic characteristics, patency prediction models based on support vector machine (SVM) were constructed respectively. For LIMA, after the introduction of hemodynamic factors, the accuracy, sensitivity and specificity of the prediction model increased from 70.35%, 50% and 74.17% to 78.02%, 70% and 78.89%, respectively. For SVG, the accuracy, sensitivity and specificity of the prediction model increased from 63.24%, 40% and 76.91% to 74.41%, 60.1% and 82.73%, respectively. The performance of the prediction model can be improved by introducing hemodynamic factors into the characteristics of the model. The accuracy, sensitivity and specificity of the prediction results are higher with the addition of hemodynamic characteristics.