ObjectiveTo investigate the effects of Anmeidan （AMD） on neuroinflammation in the hippocampus of sleep-deprived rats. MethodsSD rats were randomly divided into four groups （n = 10 per group）： control group， model group， AMD group， and melatonin group. A sleep deprivation model was established using the modified multiple platform water environment method. The AMD group received AMD at a dose of 18.18 g·kg^-1·d^-1， the melatonin group received melatonin at 100 mg·kg^-1·d^-1， and the control and model groups were given an equal volume of pure water. All treatments were administered by gavage for four weeks. Spontaneous activity was assessed using an animal behavior video system. Serum levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal pyramidal neuron morphology was examined using hematoxylin-eosin （HE） staining， and ultrastructural changes of hippocampal neurons were observed via transmission electron microscopy. Immunofluorescence was used to detect the expression of brain-derived neurotrophic factor （BDNF） and nerve growth factor （NGF） in the hippocampus. Western blot analysis was performed to measure the expression of nuclear factor-κB （NF-κB）， phosphorylated NF-κB （p-NF-κB）， NOD-like receptor protein 3 （NLRP3）， and Caspase-1 proteins. ResultsCompared with the control group， the model group showed a significant increase in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell structural damage and decreased cell count， aggravated hippocampal ultrastructural damage， mitochondrial cristae disruption， and exacerbated vacuolization. The expression of p-NF-κB p65， NLRP3， and Caspase-1 proteins was upregulated， serum IL-1β， IL-6， and TNF-α levels were significantly elevated （P<0.01）， and the fluorescence intensity of BDNF and NGF proteins was significantly reduced （P<0.01）. Compared with the model group， the AMD group showed a significant reduction in activity duration and frequency （P<0.01）， increased hippocampal pyramidal cell count with reduced structural damage， alleviated hippocampal ultrastructural damage， significantly downregulated p-NF-κB p65， NLRP3， and Caspase-1 protein expression （P<0.01）， decreased serum IL-1β， IL-6， and TNF-α levels （P<0.01）， and significantly increased the fluorescence intensity of BDNF and NGF proteins （P<0.01）. ConclusionAnmeidan alleviates hippocampal neuronal damage in sleep-deprived rats， potentially by downregulating the NLRP3 signaling pathway， reducing inflammatory cytokine release， and increasing neurotrophic factor levels.

BackgroundSevere mental disorders represent a major public health concern due to the high disability rates and substantial disease burden， which has garnered significant national attention and prompted their inclusion in public health project management systems. However， credible evidence regarding the current status of disease management and factors influencing disease stabilization among patients with severe mental disorders in Luzhou City， Sichuan Province， remains limited. ObjectiveTo investigate the current management status of patients with severe mental disorders in Luzhou City， Sichuan Province， and to analyze influencing factors of disease stabilization among patients under standardized care， so as to provide evidence-based insights for developing targeted management strategies to optimize clinical interventions for this patient population. MethodsIn March 2023， data were extracted from the Sichuan Mental Health Service Comprehensive Management Platform for patients with severe mental disorders in Luzhou City who received management between December 2017 and December 2022. Information on mental health service utilization and clinical status changes was collected. Trend analysis was conducted to evaluate temporal changes in key management indicators for severe mental disorders in Luzhou City. Logistic regression analysis was employed to identify factors influencing the disease stabilization or fluctuation of these patients. ResultsThis study enrolled a total of 20 232 patients. In Luzhou City， the stabilization rate and standardized management rate of severe mental disorders were 94.89% and 79.36% in 2017， respectively， which increased to 95.33% and 96.92% by 2022. The regular medication adherence rate rose from 34.42% in 2018 to 86.81% in 2022. In 2022， the regular medication adherence rate was 71.80% for schizophrenia， 55.26% for paranoid psychosis， and 51.43% for schizoaffective disorder. Multivariate analysis identified the following protective factors for disease stabilization： age of 18~39 years （OR=0.613， 95% CI： 0.409~0.918）， age of 40~65 years （OR=0.615， 95% CI： 0.407~0.931）， urban residence （OR=0.587， 95% CI： 0.478~0.720）， and regular medication adherence （OR=0.826， 95% CI： 0.702~0.973）. Risk factors for disease fluctuation included poor （OR=1.712， 95% CI： 1.436~2.040）， non-inclusion in care-support programs （OR=1.928， 95% CI： 1.694~2.193）， non-participation in community rehabilitation （OR=2.255， 95% CI： 1.930~2.634）， and intermittent medication adherence （OR=3.893， 95% CI： 2.548~5.946）. ConclusionThe stability rate， standardized management rate， and regular medication adherence rate of patients with severe mental disorders in Luzhou City have shown a year-by-year increase. Age， household registration status， economic condition， medication compliance， and community-based rehabilitation were identified as influencing factors for disease fluctuation in these patients. ［Funded by Luzhou Science and Technology Plan Project （number， 2022-ZRK-186）］

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.