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.

Glaucoma is a group of optic nerve disorders characterized by progressive optic nerve atrophy and visual field defects, which can lead to irreversible blindness. Early diagnosis of glaucoma is essential for preventing visual loss. However, due to the absence of obvious early symptoms, the diagnosis of glaucoma remains challenging. Visual electrophysiological examinations, an objective approach for evaluating visual function, have the potential to be used in the early diagnosis of glaucoma. This review integrates the latest publications to introduce visual electrophysiological examination techniques, including electroretinography(ERG)and visual evoked potential(VEP). It also explores the mechanisms underlying these techniques and their application value in the early diagnosis of glaucoma. In addition, this review summarizes the advantages, limitations, and applicable scenarios of different visual electrophysiological techniques. Finally, the review provides an outlook on the development prospects of visual electrophysiological techniques in the early diagnosis of glaucoma. The findings of this review can assist clinicians in selecting appropriate diagnostic methods, promote the innovation and development of early visual electrophysiological diagnostic techniques for glaucoma, and contribute to reducing the risk of blindness caused by glaucoma.

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.

This study explores the basic characteristics and potential functions of the terpene synthase(TPS) gene family members in Lonicera japonica. The L. japonica TPS(LjTPS) gene family was identified and functionally analyzed using bioinformatics methods. The results showed that a total of 70 members of the LjTPS gene family were identified in L. japonica, with protein lengths ranging from 130 to 1 437 amino acids. Most of these proteins were hydrophilic, and they were unevenly distributed across nine chromosomes. Phylogenetic analysis showed that the LjTPS gene family members were divided into six subfamilies, mainly consisting of members from the TPS-a, TPS-b, and TPS-e subfamilies. Promoter cis-acting element analysis showed that LjTPS members contained a large number of stress-responsive cis-acting elements. Aphid inoculation experiments showed that key enzyme genes in the MVA pathway for terpenoid backbone synthesis in L. japonica, such as HMGS, HMGR, MK, MPD, and the key enzyme gene in the DXP pathway, DXS, exhibited an initial increase followed by a decrease under aphid stress. The qRT-PCR analysis showed that the expression levels of the α-farnesene synthase genes LjTPS34 and LjTPS39 were down-regulated, while the expression levels of(E)-β-caryophyllene synthase genes LjTPS15 and LjTPS17 were up-regulated 12 h before aphid feeding, then began to decline. Farnesyl pyrophosphate synthase(FPS), which interacted with these genes, also displayed a pattern of increasing followed by decreasing expression. The expression of linalool synthase genes LjTPS12 and LjTPS33 was significantly up-regulated after 72 h of aphid feeding(P<0.000 1), reaching 24.39 and 22.64 times the initial expression, respectively. This pattern was in close alignment with the trend of linalool content in L. japonica. This study provides a theoretical foundation for future research on the interaction between L. japonica and pests, as well as on the functional roles of the LjTPS gene family.
Animals ; Aphids/physiology* ; Alkyl and Aryl Transferases/chemistry* ; Lonicera/parasitology* ; Phylogeny ; Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Multigene Family ; Terpenes/metabolism*

This study aims to explore the impact of host plants on the quality of Taxilli Herba and provide a theoretical basis for the quality control of Taxilli Herba. The components of Taxilli Herba from three different host plants(Morus alba, Salix babylonica, and Cinnamomum cassia) and its 3 hosts(mulberry branch, willow branch, and cinnamon branch) were detected by widely targeted metabolomics based on ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS). Principal component analysis(PCA), orthogonal partial least squares discriminant analysis(OPLS-DA), and Venn diagram were employed for analysis. A total of 717 metabolites were detected in Taxilli Herba from the three host plants and the branches of these host plants by UPLC-MS/MS. The results of PCA and OPLS-DA of Taxilli Herba from the three different host plants showed an obvious separation trend due to the different effects of host plants. The Venn diagram showed that there were 32, 8, and 26 characteristic metabolites in samples of Taxilli Herba from M. alba host, S. babylonica host, and C. cassia host, respectively. It was found by comparing the characteristic metabolites of Taxilli Herba and its hosts that each host transmits its characteristic components to Taxilli Herba, so that the Taxilli Herba contains the characteristic components of the host. The Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis showed that the differential metabolites of Taxilli Herba from the three hosts were mainly enriched in flavonoid biosynthesis, arginine and proline metabolism, and glycolysis/gluconeogenesis pathways. Furthermore, the differential metabolites enriching pathways of Taxilli Herba from the three hosts were different depending on the host. In a word, host plants have a significant impact on the metabolites of Taxilli Herba, and it may be an important factor for the quality of Taxilli Herba.
Metabolomics/methods* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid ; Tandem Mass Spectrometry ; Quality Control ; Salix/chemistry* ; Cinnamomum aromaticum/metabolism* ; Principal Component Analysis

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires