Objective To explore the effect of a multicomponent exercise and cognitive stimulation program in elderly patients with mental disorders and sarcopenia,so as to provide references for reducing the risk of falls,preventing and improving sarcopenia,and enhancing cognitive function in patients.Methods The multi-component exercise and cognitive stimulation program was formulated through literature review and expert meeting.In this quasi-experimental study,76 elderly patients with mental disorders and sarcopenia who were hospitalized in a tertiary mental health center in Shanghai from January 2023 to February 2024 were selected as research subjects.They were randomly divided into an experimental group and a control group according to their hospitalization building number(38 cases in each group).The experimental group was treated with multicomponent exercise combined with cognitive stimulation program based on routine nursing,and the control group was treated with routine nursing.The risk of falls,skeletal muscle mass,muscle strength,physical function,cognitive function,and incidence of adverse events were compared between 2 groups after 12 weeks of intervention.Results A total of 75 patients with 37 in the control group and 38 in the experimental group completed the study.The TUG time,6M walking speed and the score of Short Physical Performance Bettery of the experimental group were all lower than those of the control group(P＜0.05),and the scores of skeletal muscle mass,muscle strength,calf circumference,physical function and cognitive function of the experimental group were all higher than those of the control group(P＜0.05).Neither group experienced any adverse events.Conclusion The application of this multicomponent exercise combined cognitive stimulation program developed for elderly patients with mental disorders and sarcopenia can effectively reduce the risk of falls,enhance the skeletal muscle mass and muscle strength and improve the cognitive function in elderly patients with mental disorders and sarcopenia.

Objective To explore the effect of a multicomponent exercise and cognitive stimulation program in elderly patients with mental disorders and sarcopenia,so as to provide references for reducing the risk of falls,preventing and improving sarcopenia,and enhancing cognitive function in patients.Methods The multi-component exercise and cognitive stimulation program was formulated through literature review and expert meeting.In this quasi-experimental study,76 elderly patients with mental disorders and sarcopenia who were hospitalized in a tertiary mental health center in Shanghai from January 2023 to February 2024 were selected as research subjects.They were randomly divided into an experimental group and a control group according to their hospitalization building number(38 cases in each group).The experimental group was treated with multicomponent exercise combined with cognitive stimulation program based on routine nursing,and the control group was treated with routine nursing.The risk of falls,skeletal muscle mass,muscle strength,physical function,cognitive function,and incidence of adverse events were compared between 2 groups after 12 weeks of intervention.Results A total of 75 patients with 37 in the control group and 38 in the experimental group completed the study.The TUG time,6M walking speed and the score of Short Physical Performance Bettery of the experimental group were all lower than those of the control group(P＜0.05),and the scores of skeletal muscle mass,muscle strength,calf circumference,physical function and cognitive function of the experimental group were all higher than those of the control group(P＜0.05).Neither group experienced any adverse events.Conclusion The application of this multicomponent exercise combined cognitive stimulation program developed for elderly patients with mental disorders and sarcopenia can effectively reduce the risk of falls,enhance the skeletal muscle mass and muscle strength and improve the cognitive function in elderly patients with mental disorders and sarcopenia.

Macrophage as a crucial component of innate immunity, plays an important role in inflammation and infection immunity. Notch signal pathway is a highly conserved pathway, which regulates cellular fate and participates in numerous pathological processes. At present, a lot of literature has confirmed the role of Notch signaling in regulating the differentiation, activation and metabolism of macrophage during inflammation and infection. This review focuses on how Notch signaling promotes macrophage pro-inflammatory and anti-infective immune function in different inflammatory and infectious diseases. In this regulation, Notch signaling interact with TLR signaling in macrophages or inflammatory-related cytokines including IL-6, IL-12, and TNF-α. Additionally, the potential application and challenges of Notch signaling as a therapeutic target against inflammation and infectious diseases are also discussed.
Humans ; Signal Transduction ; Macrophages ; Cytokines/metabolism* ; Inflammation/metabolism* ; Communicable Diseases ; Receptors, Notch/metabolism*

Objective: To investigate whether a deep learning-based model using unenhanced computed tomography (CT) at baseline could predict the malignancy of pulmonary nodules. Methods: A deep learning model was trained and applied for the discrimination of pulmonary nodule in Dr. Wise Lung Analyzer. This study retrospectively recruited 130 consecutive participants with pulmonary nodules detected on CT who undergoing biopsy or surgery from May 2009 to June 2017 in Jinling hospital. A total of 136 pulmonary nodules were included in this study, including 86 malignant nodules and 50 benign ones. All patients underwent CT scans 2 times at least, the first scan was defined as baseline and the last scan before the pathological results was defined as final scan. The ROC curve of deep learning model was plotted and the AUCs were calculated. Delong test was used to examine the difference of AUCs baseline and final scan. The nodules were further divided into subsolid nodule group (pure ground-glass nodule and part solid nodule) (n=87) and solid nodule group (n=49). The difference of AUCs at baseline and final scans was evaluated intra two groups. Results: The AUCs of the deep learning model at final and baseline scans were 0.876 and 0.819, respectively. There was no significant difference between them (P=0.075). The result indicated that the model could predict the consequences of pulmonary nodules well at baseline. In small nodules (longest diameter ≤10mm), the AUC at final scan (0.847) was better than it at baseline scan (0.734), but there was no significant difference between them (P=0.058). In solid nodule group, The AUC at final scan (0.932) was better than it at baseline scan (0.835), but there was no significant difference between them (P=0.066). In subsolid nodule group, the deep learning model exhibited consistent performance at final scan (AUC, 0.759) with the baseline scan (AUC, 0.728, P=0.580). Conclusions The deep learning model could predict the malignancy of pulmonary nodules including small ones at baseline, and the model exhibited consistent performance between baseline and final scans in subsolid nodules.