Skin wound healing is a complex and tightly regulated process. The frequent occurrence and reoccurrence of acute and chronic wounds cause significant skin damage to patients and impose socioeconomic burdens. Therefore, there is an urgent requirement to promote interdisciplinary development in the fields of material science and medicine to investigate novel mechanisms for wound healing. Cerium oxide nanoparticles (CeO₂ NPs) are a type of nanomaterials that possess distinct properties and have broad application prospects. They are recognized for their capabilities in enhancing wound closure, minimizing scarring, mitigating inflammation, and exerting antibacterial effects, which has led to their prominence in wound care research. In this paper, the distinctive physicochemical properties of CeO₂ NPs and their most recent synthesis approaches are discussed. It further investigates the therapeutic mechanisms of CeO₂ NPs in the process of wound healing. Following that, this review critically examines previous studies focusing on the effects of CeO₂ NPs on wound healing. Finally, it suggests the potential application of cerium oxide as an innovative nanomaterial in diverse fields and discusses its prospects for future advancements.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-β) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life.

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization.Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm.Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97–0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model.Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients’ clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

Objective: The aim of this study was to observe the effects of different frequencies of electroacupuncture (EA) on expression of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in the hippocampal CA3 region of cerebral ischemic rats. Methods: Middle cerebral artery occlusion was successfully introduced to simulate cerebral infarction in 48 healthy male Sprague–Dawley rats. Totally, 48 rats were randomly divided into three groups: the control group, the EA 5-Hz group, and the EA 50-Hz group, 16 in each group. The rats in the control group were placed in an ordinary cage and fed normally without any treatment. EA treatment was given at a certain frequency 24 hours after surgery in the EA 5-Hz group and EA 50-Hz group. Morris water maze test was used to measure the learning and memory abilities of rats in each group after 14-day treatment with EA. Immunohistochemical staining was used to detect the expression of BDNF- and NGF-positive cells in the ischemic hippocampal CA3 area of the rats. Results: The EA 5-Hz group and EA 50-Hz group had significantly improved learning and memory ability compared with the control group according to Morris water maze findings (P<0.05). The number of BDNF- and NGF-positive cells in the hippocampal CA3 region of the ischemic hemisphere in the 5-Hz group and 50-Hz EA group was significantly more than that in the control group after EA treatment (P<0.05), and the difference in the 50-Hz group was more obvious than that in the 5-Hz group. The difference was statistically significant (P<0.05). Conclusion: EA stimulation of “Baihui” and “Dazhui” can promote the expression of BDNF and NGF in the hippocampal CA3 region of cerebral ischemic rats, and the 50-Hz EA stimulation effect is more obvious.

Background: A global public health problem, frailty is closely associated with poor prognosis after percutaneous coronary intervention (PCI) in older patients with acute myocardial infarction (AMI). Although exercise intervention is the most commonly used method to reverse and alleviate frailty, its application is restricted in patients with acute myocardial infarction following PCI due to cardiovascular instability and autonomic imbalance. Consequently, there is a need for a new practical intervention to address frailty syndrome in these patients. Purpose: This study aimed to investigate the effect of neuromuscular electrical stimulation in frail older AMI patients post-PCI. Patients and Methods: A single-blind, randomized controlled trial was carried out in the Department of Cardiovascular Medicine from March to October 2023. A total of 100 eligible participants were randomly divided into two groups: experimental (n = 50) and control (n = 50) groups, respectively. Both groups received usual care. The experimental group underwent neuromuscular electrical stimulation (NMES) on bilateral quadriceps and gastrocnemius muscles for 30 minutes daily from day 1 to day 7 after surgery. The primary outcomes measured included the frailty score, lower limb muscle strength, and lower limb muscle quality. Secondary outcomes included the activities of daily living score, inflammatory markers, and length of hospital stay. All participants were included in an intention-to-treat analysis after the study ended. Results: The frailty scores of the two groups exhibited a gradual decrease over time, and the scores of the experimental group were lower than those of the control group at 4 and 7 days after surgery (P< 0.001). Concurrently, the lower limb muscle strength showed an increasing trend over the time in the experimental group and a decreasing trend in the control group, and the scores of the experimental group surpassed those of the control group (p< 0.001). Moreover, a statistical difference was observed in the lower limb muscle mass across the groups after 7 days postoperatively compared with baseline on both sides (p< 0.05). Conclusion: Neuromuscular electrical stimulation has the potential to enhance lower limb function and alleviate frailty in elderly patients with acute myocardial infarction after PCI. These findings introduce a novel intervention approach for frailty management in the elderly population.