Pressure ulcers (PUs) pose a significant challenge in the care of bedridden patients, to which automated tilt nursing beds have emerged as a promising solution. However, the lack of effective models to elucidate the mechanical responses of deep tissue during assisted repositioning and identify the optimal tilt angle has hindered the implementation of effective automatic assisted repositioning systems for long-term care patients. Therefore, this study developed a novel computational model that integrates the buttocks with a support mattress to simulate automatic assisted repositioning, thereby analyzing deep tissue responses and optimizing tilt angles for effective load offloading. Inverse modeling was used to reconstruct the 3D shape of the buttocks, nodal equivalence techniques were employed to simplify the mesh and accurately represent internal tissue contacts, and soft tissue parameters were optimized using Response Surface Methodology (RSM). Finally, finite element (FE) analysis was conducted to evaluate the biomechanical responses and optimize the repositioning strategies. Model validation demonstrated a deformation error of 6.93 +/- 7.41 mm (mean +/- standard deviation) and interface pressure differences within 22.4%, demonstrating the efficacy and bio-fidelity of the system. Repositioning simulations at angles from 0 degrees to 30 degrees showed a 20% reduction in total soft tissue strain, with peak equivalent stress decreasing by 22.27% at the mattress- to-buttock interface and by 20.43% at the muscle-to-adipose tissue interface. These simulations suggest that a 30 degrees turning angle is beneficial for alleviating pressure concentration, which may inspire the design and optimization of automatic assisted repositioning strategies in rehabilitation practices.

In the formulation of strategies for walking rehabilitation, achieving precise identification of the current state and making rational predictions about the future state are crucial but often unrealized. To tackle this challenge, our study introduces a unified framework that integrates a novel 3D walking motion capture method using multi-source image fusion and a walking rehabilitation simulation approach based on multi-agent reinforcement learning. We found that, (i) the proposal achieved an accurate 3D walking motion capture and outperforms other advanced methods. Experimental evidence indicates that, compared to similar visual skeleton tracking methods, the proposed approach yields results with higher Pearson correlation (r=0.93), intra-class correlation coefficient (ICC(2,1)=0.91), and narrower confidence intervals ([0.90,0.95] for r, [0.88,0.94] for ICC(2,1)) when compared to standard results. The outcomes of the proposed approach also exhibit commendable correlation and concurrence with those obtained through the IMU-based skeleton tracking method in the assessment of gait parameters ([0.85,0.89] for r, [0.75,0.81] for ICC(2,1)); (ii) multi-agent reinforcement learning has the potential to be used to solve the simulation task of gait rehabilitation. In mimicry experiment, our proposed simulation method for gait rehabilitation not only enables the intelligent agent to converge from the initial state to the target state, but also observes evolutionary patterns similar to those observed in clinical practice through motor state resolution. This study offers valuable contributions to walking rehabilitation, enabling precise assessment and simulation-based interventions, with potential implications for clinical practice and patient outcomes. © 2024 Elsevier B.V.

Background: Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results: In all three planes, there are significant differences (P © 2023, The Author(s).

Background: Owing to the outbreak of the Omicron variant of SARS-CoV-2 in Shanghai, China, partitioned dynamic closure and control management plans were implemented on March 28, 2022. This created huge emergency pressure on Shanghai's medical and healthcare systems. However, the perceptions of job demands of healthcare workers (HCWs) and classification of frontline HCWs in mobile cabin hospitals are unknown.Methods: In this study, we investigated the job demands of 1223 frontline HCWs working in mobile cabin hospitals during the COVID-19 pandemic April 2022 to May 2022. We performed latent class analysis to identify classification features of job demands. A binary multivariate logistic regression model was used to explore the influencing factors of latent class.Results: The total mean job demand score was 132.26 (SD = 9.53), indicating a high level of job demand. A two class model provided the best fit. The two classes were titled "middle-demand group" (17.66%) and "high -demand group" (82.34%). A regression analysis suggested that female HCWs, HCWs satisfied with the doctor/ nurse-patient relationship, HCWs who believed that the risk of working in mobile cabin hospitals was high, and HCWs without physical discomfort during the pandemic were more likely to be in the "high-demand group".Conclusion: Characteristics of the "high-demand group" subtype suggest that attention should be paid to the physical condition of frontline HCWs and the job demands of female HCWs. Managers should strengthen the training of HCWs in terms of their communication skills as well as their knowledge and technical skills to aid epidemic prevention and control.

Objectives: A large body of literature addresses experiences of spouse and adult-children caregiver of individuals with dementia (IWDs) but has not examined the role and strength of social networks in associations between spouses and adult-children caregivers' experience. Based on the stress process model, we aimed to explore the strength levels of social networks and their association with spouses/ adult-children caregivers for IWDs.Design: A cross-sectional study. Setting and Participants: A questionnaire-based survey was conducted with a total of 146 family caregivers of IWDs (78 adult-child, and 68 spouses) in China.Methods: Data collection comprised 4 sections: (1) care-related stressors (dementia stage, neuropsychiatric symptoms); (2) caregiver context; (3) social network, using the Lubben Social Network Scale; and (4) caregiving experience, using the short-form Zarit Burden Interview and 9-item Positive Aspects of Caregiving Scale. Linear regression, mediation model analysis, and interactive analysis were performed to explore the mechanisms of associations between variables.Results: Spouses had weaker social network strength (beta = -0.294, P =.001) and reported greater positive aspects of caregiving ( beta = 0.234, P =.003) than adult-children caregivers; no significant difference was found between them for caregiver burden. Mediation analysis suggests that associations between caregiver type and caregiver burden are indirect-only mediation effects of social networks ( beta = 0.140, 95% CI = 0.066-0.228). The social network strength suppressed the association between caregiver type and positive aspects of caregiving. The caregiver type/social network interaction statistically significantly (P =.025) affected the "positive aspects": a stronger social network was associated with more positive aspects of caregiving among the spouse subgroup (beta = 0.341, P =.003).Conclusions and Implications: Social networks mediate responses to caregiving experiences among different care provider types and are vital intervention targets, especially for spousal caregivers. Our results can serve as references for identifying caregivers for clinical intervention.(c) 2023 AMDA - The Society for Post-Acute and Long-Term Care Medicine.

Background: Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.Objective: We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults.Methods: This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors' direct observation. A total of 15 candidate predictors (older adults' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naive Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP).Results: A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube.Conclusions: The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.

Spatiotemporal Immune disorder is a key factor leading to the failure of bone tissue healing. It is of vital importance to accurately suppress excessive peak immune response within 24-48 h of the injury and so regulate the spatiotemporal osteoimmune disturbance of bones. In this study, Ultrasound Controlled "Explosive" (UCE) hydrogels were prepared from gelatin-hyaluronic acid methacrylate hydrogels loaded with resveratrol nanobubbles produced by double emulsification through a condensation reaction. Such materials innovatively enable ultrasound-controlled RES release for precise regulation of spatiotemporal osteoimmune disorders. Under an ultrasonic power level of 1.5 W/cm2, the rate of effectively released RES through the blast of UCE hydrogels reached 38.14 %. And compared with the control group, the in vivo inhibition of inflammation and osteogenesis effects of UCE hydrogels were more effective, respectively. As suggested by the results, the excessive local inflammatory response was inhibited by the release of resveratrol, the temporospatial disorder of bone immune was precisely regulated, and as a result, the process of bone repair was accelerated. Altogether, this study confirms that the newly created UCE Hydrogels effectively promote bone repair by intervening peak inflammation during the early phase of fracture healing. © 2023

Tissue repair is a cascade of sequential biological events initiated to repair damages inflicted by trauma, pathogenic bacteria, or other injury-causing factors, with the aim of restoring tissue integrity. Smart composite biomaterials are able to react to biological signals released after tissue damages and make adjustments that suit the dynamic and complex internal environment based on external information, thus maintaining the homeostasis of the internal environment in the human body. Ultrasound is considered as an excellent "remote control" and "trigger" for smart composite biomaterials due to its basic technical characteristic effects that primarily include cavitation, microstreaming, scattering, and acoustic radiation force. Here, we presented the first comprehensive and systematic review of the research progress of ultrasound-responsive smart composite biomaterials in tissue repair. In this review, firstly, we elaborated on the definition and mechanism of ultrasound, followed by a brief analysis of the principles of the response of biomaterials to ultrasound. Then, to systematically review the subject, we divide ultrasound-responsive smart composite biomaterials into following categories: anti-infective, skin repair, osteogenic, gene transfection, thrombolytic, neuromuscular, and imaging. Finally, there is a brief summary of ultrasound-responsive smart composite biomaterials, and an outlook based on characteristics of physiological repair of tissues in each system. © 2023 Elsevier Ltd

IntroductionEye-tracking technology provides a reliable and cost-effective approach to characterize mental representation according to specific patterns. Mental rotation tasks, referring to the mental representation and transformation of visual information, have been widely used to examine visuospatial ability. In these tasks, participants visually perceive three-dimensional (3D) objects and mentally rotate them until they identify whether the paired objects are identical or mirrored. In most studies, 3D objects are presented using two-dimensional (2D) images on a computer screen. Currently, visual neuroscience tends to investigate visual behavior responding to naturalistic stimuli rather than image stimuli. Virtual reality (VR) is an emerging technology used to provide naturalistic stimuli, allowing the investigation of behavioral features in an immersive environment similar to the real world. However, mental rotation tasks using 3D objects in immersive VR have been rarely reported. MethodsHere, we designed a VR mental rotation task using 3D stimuli presented in a head-mounted display (HMD). An eye tracker incorporated into the HMD was used to examine eye movement characteristics during the task synchronically. The stimuli were virtual paired objects oriented at specific angular disparities (0, 60, 120, and 180 degrees). We recruited thirty-three participants who were required to determine whether the paired 3D objects were identical or mirrored. ResultsBehavioral results demonstrated that the response times when comparing mirrored objects were longer than identical objects. Eye-movement results showed that the percent fixation time, the number of within-object fixations, and the number of saccades for the mirrored objects were significantly lower than that for the identical objects, providing further explanations for the behavioral results. DiscussionIn the present work, we examined behavioral and eye movement characteristics during a VR mental rotation task using 3D stimuli. Significant differences were observed in response times and eye movement metrics between identical and mirrored objects. The eye movement data provided further explanation for the behavioral results in the VR mental rotation task.

Background The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. Results The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. Conclusions Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.