ObjectiveTo systematically evaluate relationship between expression of autophagy-related protein Beclin-1 in gastric cancer and its clinicopathologic features and its clinical significances.MethodsThe researches on the expression and significance of Beclin-1 protein in the gastric tumor tissues published from the database establishment to June 1, 2018 in the Cochrane Library, Springer Link, Web of Science, Embase, PubMed, CNKI, Wanfang, VIP, and other databases were searched. Two researchers independently screened and evaluated the literatures, extracted the relevant data, and conducted the meta-analysis using the Review Manager 5.3 and Stata 15.0 software.ResultsFinally, 10 articles were included, and there were 1 402 patients with gastric cancer. The meta-analysis showed that the positive rate of Beclin1 protein expression in the gastric cancer tissues was significantly lower than that in the non-gastric cancer tissues [OR=0.30, 95% CI (0.13, 0.72), P=0.007], which in the patients with TNM stage Ⅲ/Ⅳ and distant metastatic gastric cancer were significantly lower than those in the patients with stage Ⅰ/Ⅱ [OR=1.82, 95% CI (1.03, 3.20), P=0.04] and without distant metastasis [OR=0.36, 95% CI (0.20, 0.63), P=0.000 4], which were not associated with the gender, age, tumor size, lymph node metastasis, serosa invasion, and tumor differentiation degree of gastric cancer patients (P>0.05). For the studies of existed heterogeneity, further the subgroup analysis showed that the positive expression rate of Beclin-1 protein in the gastric cancer tissues was significantly lower than that in the non-gastric cancer tissues [OR=0.19, 95% CI (0.13, 0.29), P<0.000 01], which in the patients with lymph node metastasis, invasion of serosa, and poorly differentiated gastric cancer were significantly lower than those in the non-lymph node metastasis [OR=0.35, 95% CI (0.22, 0.57), P<0.000 1], non-invasion of serosa [OR=0.56, 95% CI (0.33, 0.94), P=0.03], and moderately/highly differentiated gastric cancer tissues [OR=0.29, 95% CI (0.20, 0.43), P<0.000 01].ConclusionsLow expression of Beclin-1 in gastric cancer tissues is related to stage and distant metastasis of gastric cancer. It is suggested that it might not only be an important cause of gastric cancer, but also play a regulatory role in progress of gastric cancer.
Objective To explore the role of using a workshop combined with diversified teaching model in undergraduate general practice. Methods Undergraduate students who enrolled in the Class 2022 general medicine course of Shanghai Jiao Tong University School of Medicine were selected between February and June 2023. Two classes were selected from undergraduate students who enrolled in the Class 2022 general medicine course using a simple random sampling method. One class was selected as the control group (using the workshop teaching mode), while the other class was selected as the experimental group (using the workshop combined with diversified teaching mode). The evaluation of teaching effect, teaching satisfaction, career intention and final scores of the control group and the experimental group were compared. Results A total of 120 students were included, with 60 students in each group. There was no statistically significant difference in the gender, age, and previous general medicine course exam scores between the two groups (P>0.05). The evaluation of teaching effect by the experimental group was higher than that of the control group (P<0.05). The teaching satisfaction scores of the experimental group on teaching atmosphere (85.16±9.44 vs. 81.65±8.15), teaching mode (86.30±9.12 vs. 79.27±9.33), and teaching management (84.20±7.05 vs. 80.10±7.15) were higher than those of the control group (P<0.05). The experimental group had a higher career intention than the control group (45 vs. 32 people; χ2=6.125, P=0.013). The theoretical course scores (71.77±7.10 vs. 66.14±7.45), internship scores (10.32±3.34 vs. 8.58±2.56), and total scores (82.09±9.36 vs. 74.58±8.45) of the experimental group were higher than those of the control group (P<0.05). Conclusion The application of workshop combined with diversified teaching mode in undergraduate general medicine course teaching can improve students’ evaluation of teaching effect, teaching satisfaction and final scores, as well as change students’ career intention.
Transcranial magneto-acoustical stimulation (TMAS), utilizing focused ultrasound and a magnetostatic field to generate an electric current in tissue fluid to regulate the activities of neurons, has high spatial resolution and penetration depth. The neuronal spike-frequency adaptation plays an important role in the treatment of neural information. In this paper, we study the effects of ultrasonic intensity, magnetostatic field intensity and ultrasonic frequency on the neuronal spike-frequency adaptation based on the Ermentrout neuron model. The simulation results show that, the peak time interval becomes smaller, the interspike interval becomes shorter and the time of the firing of the neuron is shortened with the increasing of the magnetostatic field intensity. With the increase of the adaptive variables, the initial spike-frequency is shifted to the right with the magnetostatic field intensity, and the spike-frequency is linearly related to the increase of the magnetostatic field intensity in steady state. The simulation effect with change of the ultrasonic intensity is consistent with the change of magnetostatic field intensity. The change of the ultrasonic frequency has no effect on the neuronal spike-frequency adaptation. Under the different adaptive variables, with the increase of the adaptive variables, the initial spike-frequency amplitude decreased with the increasing of the ultrasonic frequency, and the spike-frequency is linearly related to the increase of the ultrasonic frequency in steady state. These results of the study can help us to reveal the mechanism of transcranial magneto-acoustical stimulation on the neuronal spike-frequency adaptation, and provide a theoretical basis for its application in the treatment of neurological disorders.
Autism spectrum disorders (ASD) is a complex developmental disorder characterized by impairments in social communication and stereotyped behaviors. Electroencephalograph (EEG), which can measure neurological changes associated with cortical synaptic activity, has been proven to be a powerful tool for detecting neurological disorders. The main goal of this study is to explore the effects of repetitive transcranial magnetic stimulation (rTMS) on behavioral response and EEG. We enrolled 32 autistic children, rTMS group (n = 16) and control group (n = 16) and calculated the relative power of the δ, θ, α, β rhythms in each brain area by fast Fourier transform and Welch’s method. We also compared Autism Behavior Checklist (ABC) scores of the patients before and after rTMS. The results showed a significant decrease in the relative power of the δ band on right temporal region and parietal region and also a decreased coherence on frontal region after rTMS intervention. The study proves that rTMS could have positive effects on behavior of attention, execution ability, and language ability of children and could reduce their stereotyped behavior and radical behavior.
Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.
The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's t-test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of features, with which the SVM model achieved best performance. The results showed that the complexity of EEGs in children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six features were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.