ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

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.

Compounds isolated from Epimedium include the total flavonoids of Epimedium , icariin, and its metabolites (icaritin, icariside I, and icariside II), which have similar molecular structures. Modern pharmacological research and clinical practice have proved that Epimedium and its active components have a wide range of pharmacological effects, especially in improving sexual function, hormone regulation, anti-osteoporosis, immune function regulation, anti-oxidation, and anti-tumor activity. To date, we still need a comprehensive source of knowledge about the pharmacological effects of Epimedium and its bioactive compounds on the male reproductive system. However, their actions in other tissues have been reviewed in recent years. This review critically focuses on the Epimedium , its bioactive compounds, and the biochemical and molecular mechanisms that modulate vital pathways associated with the male reproductive system. Such intrinsic knowledge will significantly further studies on the Epimedium and its bioactive compounds that protect the male reproductive system and provide some guidances for clinical treatment of related male reproductive disorders.
Male ; Epimedium/chemistry* ; Humans ; Genitalia, Male/drug effects* ; Flavonoids/therapeutic use* ; Animals

OBJECTIVES: To study the clinical and imaging features of children with influenza-associated encephalopathy (IAE), and to investigate the influencing factors for prognosis. METHODS: A retrospective analysis was conducted on the medical data (clinical data, laboratory examinations, imaging data, and prognosis) of 23 children with IAE who were diagnosed and treated in Children's Hospital of Nanjing Medical University from May 2022 to April 2023. RESULTS: Among the 23 patients, 18 (78%) had influenza A and 5 (22%) had influenza B. All patients had fever and encephalopathy, and 20 patients (87%) had seizures, while 11 patients (48%) had persistent convulsions. There were 10 patients (43%) with an increase in alanine aminotransferase, 14 (61%) with an increase in aspartate aminotransferase, and 18 (78%) with an increase in lactate dehydrogenase. Abnormal imaging findings were observed in 20 patients (87%), among whom 10 (43%) had acute necrotizing encephalopathy. All 23 patients received peramivir or oseltamivir. Of all patients, 12 (52%) achieved complete recovery, 5 (22%) had varying degrees of neurological dysfunction, and 6 (26%) died. Compared with the good prognosis group, the poor prognosis group had significantly higher levels of alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase (P<0.05). CONCLUSIONS Fever and convulsions are the most common symptoms of children with IAE, and acute necrotizing encephalopathy is the most common clinical imaging syndrome. Increases in alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase have a certain value in predicting poor prognosis.
Humans ; Influenza, Human/complications* ; Male ; Prognosis ; Female ; Child, Preschool ; Retrospective Studies ; Infant ; Child ; Brain Diseases/etiology*

OBJECTIVE: To investigate the effect of TBL1XR1 mutation on cell biological characteristics of diffuse large B-cell lymphoma (DLBCL). METHODS: The TBL1XR1 overexpression vector was constructed and DNA sequencing was performed to determine the mutation status. The effect of TBL1XR1 mutation on apoptosis of DLBCL cell line was detected by flow cytometry and TUNEL fluorescence assay; CCK-8 assay was used to detect the effect of TBL1XR1 mutation on cell proliferation; Transwell assay was used to detect the effect of TBL1XR1 mutation on cell migration and invasion; Western blot was used to detect the effect of TBL1XR1 mutation on the expression level of epithelial-mesenchymal transition (EMT) related proteins. RESULTS: The TBL1XR1 overexpression plasmid was successfully constructed. The in vitro experimental results showed that TBL1XR1 mutation had no significant effect on apoptosis of DLBCL cells. Compared with the control group, TBL1XR1 mutation enhanced cell proliferation, migration and invasion of DLBCL cells. TBL1XR1 gene mutation significantly increased the expression of N-cadherin protein, while the expression of E-cadherin protein decreased. CONCLUSION TBL1XR1 mutation plays a role in promoting tumor cell proliferation, migration and invasion in DLBCL. TBL1XR1 could be considered as a potential target for DLBCL therapy in future research.
Humans ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Proliferation ; Mutation ; Receptors, Cytoplasmic and Nuclear/genetics* ; Apoptosis ; Cell Line, Tumor ; Epithelial-Mesenchymal Transition ; Cell Movement ; Repressor Proteins/genetics* ; Nuclear Proteins/genetics* ; Cadherins/metabolism*

OBJECTIVE: To investigate the effects of histone deacetylase (HDAC) levels on the proliferation and apoptosis of Burkitt lymphoma cells, and the changes in related signaling molecules in the PI3K/AKT/mTOR signaling pathway, so as to explore the pathogenesis of Burkitt lymphoma. METHODS: HDAC levels in Burkitt lymphoma were detected by RT-PCR and Western blot. CA46 and RAJI cells were treated with the HDAC selective inhibitor VPA. CCK8 assay was used to detect the proliferation ability of cells. Western Blot was used to measure the expression of apoptosis-related proteins, PI3K/AKT/mTOR signaling pathway proteins and their phosphorylation levels. RESULTS: The expression levels of classⅠ HDAC in Burkitt lymphoma were higher than those in normal cells, and the HDAC1 inhibitor VPA could inhibit the proliferation of CA46 and RAJI cells. VPA decreased HDAC expression in CA46 and RAJI cells, inhibited the phosphorylation of PI3K/AKT/mTOR pathway molecules AKT and p70S6K, increased the expression of apoptotic proteins Cleaved Caspase-3, Cleaved Caspase-8, Cleaved Caspase-9 and Bax, and decreased the expression of anti-apoptotic proteins Bcl-2 and PARP. CONCLUSION Inhibition of HDAC activity can Attenuate the proliferation of Burkitt lymphoma cells and induce apoptosis by inhibiting the PI3K/AKT/mTOR signaling pathway activity.
Humans ; Burkitt Lymphoma/pathology* ; Apoptosis ; Cell Proliferation ; Signal Transduction ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Cell Line, Tumor ; Histone Deacetylases/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Histone Deacetylase Inhibitors/pharmacology* ; Phosphorylation