ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

Objective: This study aims to investigate may moesin deficiency resulted in neurodevelopmental abnormalities caused by negative impact on synaptic signaling ultimately leading to synaptic structure and plasticity. Methods: Behavioral assessments measured neurodevelopment (surface righting, negative geotaxis, cliff avoidance), anxiety (open field test, elevated plus maze test), and memory (passive avoidance test, Y-maze test) in moesin-knockout mice (KO) compared to wild-type mice (WT). Whole exome sequencing (WES) of brain (KO vs. WT) and analysis of synaptic proteins were performed to determine the disruption of signal pathways downstream of moesin. Risperidone, a therapeutic agent, was utilized to reverse the neurodevelopmental aberrance in moesin KO. Results: Moesin-KO pups exhibited decrease in the surface righting ability on postnatal day 7 (p<0.05) and increase in time spent in the closed arms (p<0.01), showing increased anxiety-like behavior. WES revealed mutations in pathway aberration in neuron projection, actin filament-based processes, and neuronal migration in KO. Decreased cell viability (p<0.001) and expression of soluble NSF adapter protein 25 (SNAP25) (p<0.001) and postsynaptic density protein 95 (PSD95) (p<0.01) was observed in days in vitro 7 neurons. Downregulation of synaptic proteins, and altered phosphorylation levels of Synapsin I, mammalian uncoordinated 18 (MUNC18), extracellular signal-regulated kinase (ERK), and cAMP response element-binding protein (CREB) was observed in KO cortex and hippocampus. Risperidone reversed the memory impairment in the passive avoidance test and the spontaneous alternation percentage in the Y maze test. Risperidone also restored the reduced expression of PSD95 (p<0.01) and the phosphorylation of Synapsin at Ser605 (p<0.05) and Ser549 (p<0.001) in the cortex of moesin-KO. Conclusion Moesin deficiency leads to neurodevelopmental delay and memory decline, which may be caused through altered regulation in synaptic proteins and function.

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.

Objective: This study aims to investigate may moesin deficiency resulted in neurodevelopmental abnormalities caused by negative impact on synaptic signaling ultimately leading to synaptic structure and plasticity. Methods: Behavioral assessments measured neurodevelopment (surface righting, negative geotaxis, cliff avoidance), anxiety (open field test, elevated plus maze test), and memory (passive avoidance test, Y-maze test) in moesin-knockout mice (KO) compared to wild-type mice (WT). Whole exome sequencing (WES) of brain (KO vs. WT) and analysis of synaptic proteins were performed to determine the disruption of signal pathways downstream of moesin. Risperidone, a therapeutic agent, was utilized to reverse the neurodevelopmental aberrance in moesin KO. Results: Moesin-KO pups exhibited decrease in the surface righting ability on postnatal day 7 (p<0.05) and increase in time spent in the closed arms (p<0.01), showing increased anxiety-like behavior. WES revealed mutations in pathway aberration in neuron projection, actin filament-based processes, and neuronal migration in KO. Decreased cell viability (p<0.001) and expression of soluble NSF adapter protein 25 (SNAP25) (p<0.001) and postsynaptic density protein 95 (PSD95) (p<0.01) was observed in days in vitro 7 neurons. Downregulation of synaptic proteins, and altered phosphorylation levels of Synapsin I, mammalian uncoordinated 18 (MUNC18), extracellular signal-regulated kinase (ERK), and cAMP response element-binding protein (CREB) was observed in KO cortex and hippocampus. Risperidone reversed the memory impairment in the passive avoidance test and the spontaneous alternation percentage in the Y maze test. Risperidone also restored the reduced expression of PSD95 (p<0.01) and the phosphorylation of Synapsin at Ser605 (p<0.05) and Ser549 (p<0.001) in the cortex of moesin-KO. Conclusion Moesin deficiency leads to neurodevelopmental delay and memory decline, which may be caused through altered regulation in synaptic proteins and function.

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.

Objective: This study aims to investigate may moesin deficiency resulted in neurodevelopmental abnormalities caused by negative impact on synaptic signaling ultimately leading to synaptic structure and plasticity. Methods: Behavioral assessments measured neurodevelopment (surface righting, negative geotaxis, cliff avoidance), anxiety (open field test, elevated plus maze test), and memory (passive avoidance test, Y-maze test) in moesin-knockout mice (KO) compared to wild-type mice (WT). Whole exome sequencing (WES) of brain (KO vs. WT) and analysis of synaptic proteins were performed to determine the disruption of signal pathways downstream of moesin. Risperidone, a therapeutic agent, was utilized to reverse the neurodevelopmental aberrance in moesin KO. Results: Moesin-KO pups exhibited decrease in the surface righting ability on postnatal day 7 (p<0.05) and increase in time spent in the closed arms (p<0.01), showing increased anxiety-like behavior. WES revealed mutations in pathway aberration in neuron projection, actin filament-based processes, and neuronal migration in KO. Decreased cell viability (p<0.001) and expression of soluble NSF adapter protein 25 (SNAP25) (p<0.001) and postsynaptic density protein 95 (PSD95) (p<0.01) was observed in days in vitro 7 neurons. Downregulation of synaptic proteins, and altered phosphorylation levels of Synapsin I, mammalian uncoordinated 18 (MUNC18), extracellular signal-regulated kinase (ERK), and cAMP response element-binding protein (CREB) was observed in KO cortex and hippocampus. Risperidone reversed the memory impairment in the passive avoidance test and the spontaneous alternation percentage in the Y maze test. Risperidone also restored the reduced expression of PSD95 (p<0.01) and the phosphorylation of Synapsin at Ser605 (p<0.05) and Ser549 (p<0.001) in the cortex of moesin-KO. Conclusion Moesin deficiency leads to neurodevelopmental delay and memory decline, which may be caused through altered regulation in synaptic proteins and function.

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.

Objective: This study aims to investigate may moesin deficiency resulted in neurodevelopmental abnormalities caused by negative impact on synaptic signaling ultimately leading to synaptic structure and plasticity. Methods: Behavioral assessments measured neurodevelopment (surface righting, negative geotaxis, cliff avoidance), anxiety (open field test, elevated plus maze test), and memory (passive avoidance test, Y-maze test) in moesin-knockout mice (KO) compared to wild-type mice (WT). Whole exome sequencing (WES) of brain (KO vs. WT) and analysis of synaptic proteins were performed to determine the disruption of signal pathways downstream of moesin. Risperidone, a therapeutic agent, was utilized to reverse the neurodevelopmental aberrance in moesin KO. Results: Moesin-KO pups exhibited decrease in the surface righting ability on postnatal day 7 (p<0.05) and increase in time spent in the closed arms (p<0.01), showing increased anxiety-like behavior. WES revealed mutations in pathway aberration in neuron projection, actin filament-based processes, and neuronal migration in KO. Decreased cell viability (p<0.001) and expression of soluble NSF adapter protein 25 (SNAP25) (p<0.001) and postsynaptic density protein 95 (PSD95) (p<0.01) was observed in days in vitro 7 neurons. Downregulation of synaptic proteins, and altered phosphorylation levels of Synapsin I, mammalian uncoordinated 18 (MUNC18), extracellular signal-regulated kinase (ERK), and cAMP response element-binding protein (CREB) was observed in KO cortex and hippocampus. Risperidone reversed the memory impairment in the passive avoidance test and the spontaneous alternation percentage in the Y maze test. Risperidone also restored the reduced expression of PSD95 (p<0.01) and the phosphorylation of Synapsin at Ser605 (p<0.05) and Ser549 (p<0.001) in the cortex of moesin-KO. Conclusion Moesin deficiency leads to neurodevelopmental delay and memory decline, which may be caused through altered regulation in synaptic proteins and function.