In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

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: 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: 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: 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: 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 To study the microscopic structure and morphological characteristics of Zebrafish eyeball and retina at different developmental stages, and to lay a foundation for visual research model. Methods Select eight groups of zebrafish at different ages, with six fish in each group, 48 fish in total. Optical microscopy and transmission electron microscopy were used to observe the eyeball structure of Zebrafish at different developmental stages, and the thickness of retinal each layer was measured to analyze the temporal and spatial development pattern. The morphological characteristics of various cells in the retina and the way of nerve connection were observed from the microscopic and ultrastructural aspects, especially the structural differences between rod cells and cone cells. Results The retina of Zebrafish can be divided into ten layers including retinal pigment epithelial layer, rod cells and cone cells layer, outer limiting membrane, outer nuclear layer, outer plexiform layer, inner nuclear layer, inner plexiform layer, ganglion cell layer, nerve fiber layer, inner limiting membrane. Rod cells had a smaller nucleus and a higher electron density than cone cells. Photoreceptor terminals were neatly arranged in the outer plexiform layer, forming neural connections with horizontal cells and bipolar cells, and several synaptic ribbons are clearly visible within them. In Zebrafish retina, ganglion cell layer and inner plexiform layer are the earliest developed. With the growth and development of Zebrafish, the thickness of rod cells and cone cells layer and retinal pigment epithelial layer gradually increases, and the retinal structure was basically developed in about 10 weeks. Conclusion The retinal structure of Zebrafish is typical, with obvious stratification and highly differentiated nerve cells. There are abundant neural connections in the outer plexiform layer. The ocular development characteristics of Zebrafish are similar to those of most mammals.

AIM To identify the chemical components of Longmu Qingxin Mixture by UPLC-Q-TOF-MS and study its material basis for the treatment of attention deficit hyperactivity disorder.METHODS The sample was detected by mass spectrometry in positive and negative ion mode on a Waters CORTECS? UPLC? T3 chromatographic column.The data were analyzed with Peakview 1.2 software and matched with the Natural Products HR-MS/MS Spectral Library 1.0 database,and the components were identified in combination with literature reports.The material basis of Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder was analysed according to the identified components.RESULTS Forty chemical components were identified,including 11 flavonoids,6 monoterpene glycosides,4 triterpene saponins,3 phenolic acids,6 alkaloids etc.,which mainly derived from Radix Astragali,Radix Paeoniae Alba,Radix Scutellariae,licorice root,Ramulus Uncariae cum,etc.,baicalein,formononetin,astragaloside Ⅳ and rhynchophylline may be the material basis for the therapeutic effect of Longmu Qingxin Mixture.CONCLUSION UPLC-Q-TOF-MS can quickly identify the chemical components of Longmu Qingxin Mixture.Flavonoids,triterpene saponins and alkaloids may be the material basis for Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder,which can provide the basis for its material basis research,quality standard establishment and pharmacological study of the dismantled formula.

Objective: To explore the association between lifestyle and fat mass index (FMI) in different positions of children and adolescents aged 7-18, so as to provide a scientific basis for health promotion in children and adolescents. Methods: A total of 1 531 students aged 7-18 was selected by intentional sampling from 4 schools in Tongzhou District, Beijing from September to December in 2020 and August in 2022. Questionnaire survey was used to collect lifestyle including dietary behavior, moderate to vigorous physical activity, smoke and drink behaviors, sleep time and sleep quality. Dual energy Xray absorptiometry was employed to assess fat mass, and calculated total, android, trunk, hip, gynoid and leg fat mass index (FMI). The ttest and Chisquare test were used to compare the differences of different lifestyle. Logistic regression was used to analysis association between lifestyle and body composition in different positions. Results: Compared with healthy lifestyle, unhealthy lifestyle had higher risk for hightrunk FMI (OR=1.40, P<0.05). After adjusted for sex and age, unhealthy lifestyle had higher risk for hightotal FMI, highandroid FMI, hightrunk FMI (OR=1.37, 1.37, 1.50, P<0.05), compared with healthy lifestyle. Stratified analysis found the associations between unhealthy lifestyle and hightotal FMI, highandroid FMI, hightrunk FMI, and highthigh FMI were only significant in girls with 7-12 years old (OR=2.13, 2.46, 2.13, 2.13, P<0.05). Conclusions Unhealthy lifestyle is associated with hightotal FMI, highandroid FMI and hightrunk FMI. A healthy lifestyle should be maintained during puberty, especially before puberty, to help children and adolescents reduce body fat and promote a balanced distribution of body composition.

OBJECTIVE: To examine the therapeutic effect of Fangji Fuling Decoction (FFD) on sepsis through network pharmacological analysis combined with in vitro and in vivo experiments. METHODS: A sepsis mouse model was constructed through intraperitoneal injection of 20 mg/kg lipopolysaccharide (LPS). RAW264.7 cells were stimulated by 250 ng/mL LPS to establish an in vitro cell model. Network pharmacology analysis identified the key molecular pathway associated with FFD in sepsis. Through ectopic expression and depletion experiments, the effect of FFD on multiple organ damage in septic mice, as well as on cell proliferation and apoptosis in relation to the mitogen-activated protein kinase 14/Forkhead Box O 3A (MAPK14/FOXO3A) signaling pathway, was analyzed. RESULTS: FFD reduced organ damage and inflammation in LPS-induced septic mice and suppressed LPS-induced macrophage apoptosis and inflammation in vitro (P<0.05). Network pharmacology analysis showed that FFD could regulate the MAPK14/FOXO signaling pathway during sepsis. As confirmed by in vitro cell experiments, FFD inhibited the MAPK14 signaling pathway or FOXO3A expression to relieve LPS-induced macrophage apoptosis and inflammation (P<0.05). Furthermore, FFD inhibited the MAPK14/FOXO3A signaling pathway to inhibit LPS-induced macrophage apoptosis in the lung tissue of septic mice (P<0.05). CONCLUSION FFD could ameliorate the LPS-induced inflammatory response in septic mice by inhibiting the MAPK14/FOXO3A signaling pathway.
Mice ; Animals ; Mitogen-Activated Protein Kinase 14/metabolism* ; Wolfiporia ; Lipopolysaccharides/pharmacology* ; Sepsis/complications* ; Signal Transduction ; Inflammation/drug therapy* ; Oxygen Radioisotopes