ObjectiveTo observe the expression of Caspase-3 mRNA in prefrontal cortex and hippocampus of chronic stress-induced depression rats,and to detect the machnisms of antidepression by electro-acupuncture.MethodsSprague-Dawley rats were randomly divided into four groups:control group,model group,model +electro-acupuncture group and model + paroxetine group,12 rats in each group.Open-field test was used to observe the changes of movements,and real-time fluorescence quantitative PCR (RT-PCR) method was used to detect Caspase-3 mRNA levels in prefrontal cortex and hippocampus.Results ①Open-field test:after stress,compared with control group rats,the model group rats' crossing numbers (29 ± 7),rearing times (6 ± 2) were apparently less than those of control group( (66 ± 13),( 10 ±2) ; P＜0.05,P＞0.05).In comparison with model group,the crossing times and rearing times being increasing in degree in electro-acupuncture group( (61 ±9),( 13 ±1 ) ) and paroxetine group( (39 ± 10),(8 ± 1 ),P＜0.01,P＞0.05).② Compared with the control group,Caspase-3 mRNA in prefrontal cortex and hippocampus significantly increased in model group(P ＜0.05 ) ;and compared with model group,Caspase-3 mRNA in prefrontal cortex in electro-acupuncture group and paroxetine group significantly decreased(P ＜ 0.05 ),and both expression of Caspase-3 mRNA in hippocampus in electro-acupuncture group and expression of Caspase-3 mRNA in hippocampus in paroxetine group decreased (P ＞ 0.05 ).ConclusionChronic stress can increase the expression of Caspase-3 mRNA in prefrontal cortex and hippocampus of chronic stress-induced depression rats,while electro-acupuncture can decrease the expression of caspase-3 mRNA,which may be an important way to anti-depression by electro-acupuncture.

Objective To improve the understanding of the characteristics of obstructive sleep apnea-hypopnea syndrome (OSAHS) in the elderly patients, and to improve the diagnosis and treatment level. Methods Monitoring results of polysomnography (PSG) from 110 elderly OSAHS patients were analyzed retrospectively. The general conditions, sleep architecture, apnea and hypopnea events, oxygen reduction as well as possible correlations between various indicators were analyzed using SPSS18.0 statistical software. Results The median rapid eye movement (REM) and non-REM (NREM) sleep time of elderly patients with OSAHS accounted for 2. 17% and 76.73%,respectively. The median arousal index was 45.6 times/h. The longest time of sleep apnea was (51.94±22.06) s, the median of average sleep apnea time was 22.50 s, the longest time of hypopnea was (47.06±12.52) s and the average hypopnca time was (21.50±4.63) s. The median respiratory disturbance index (RDI) of all patients was 21.50, the patients with RDI between 5 and 20 accounted for 46.40%, with RDI between 20 and 40 accounted for 31.80% and with RDI over 40 accounted for 21.8%. The average oxygen saturation accounted for (93.45% ± 2.81%), the lowest oxygen saturation accounted for (76.3%± 10. 5%) and the median oxygen desaturation index was 31.6;times/h. BMI was negatively correlated with lowest oxygen saturation (r=-0. 378, P＜0.01) and average oxygen saturation ( r = - 0. 355, P ＜ 0. 01 ), while was positively correlated with oxygen desaturation index (r=0. 338, P＜0. 01 ). The lowest oxygen saturation was negatively correlated with the longest time of obstructive apnea (r= -0. 47, P＜0. 01 ), the average time of obstructive apnea (r=-0.316, P＜0.01), the longest time of hypopnea (r=-0.293, P＜0.01) and the average time of hypopnea (r=-0. 277, P＜0.01). The median time intervals of oxygen desaturation during supine, left side and right side position were 2.36 min, 11.54 min and 12.45 min,respectively. The median time intervals of oxygen desaturation during left side and right side position were both longer than that of supine position (Z= -6.12 and -7. 10 respectively, both P＜0.01).Conclusions Elderly patients with OSAHS manifest obvious disorder of sleep structural and sleep fragmentation. According to RDI, the majority of the patients are classified as mild to moderate in severity. However, elderly patients with OSAHS are severe regarding to hypoxia relatively. The severity of hypoxia is related with BMI and the lasting time of sleep-disordered breathing events, and hypoxia are less severe when sleeping on left side or on right side.

Objective To investigate the possible correlation of the clinical parameters, such as age, obesity, Epworth sleepiness score (ESS), with the severity of snoring and obstructive sleep apnea hypopnea syndrome (OSAHS). Methods One hundred and forty-eight patients with snoring during sleep admitted from May to Jul. 2008 were asked to answer the questions from a questionnaire concerning snoring, daytime sleepiness, and habits such as smoking and drinking, etc. All patients underwent at least a polysomnography (PSG) and the physical examination included height, weight, and body mass index (BMI). Age, BMI, the lowest SaO_2(%), ESS score, the biggest reduction of oxygen (%), a total suspension of time, the average correlation between respiratory disorder index (RDI) applied computing Pearson correlation test. Simple snoring and OSAHS group of mild, moderate and severe inter-group comparison analysis using generalized linear models. Results The prevalence of OSAHS was increased with age, higher in males than in females. A statistically significant correlation between ESS, BMI, the lowest SaO_2 with the RDI was detected. The difference of ESS, the lowest SaO_2 and the BMI was significant between the different serious patients (P<0.05). Conclusions OSAHS has a high morbidity rate in outpatients with snoring. Age and obesity are liability factors of OSAHS. BMI, the lowest SaO_2, ESS and RDI have well correlationship, which can be used to assess the pathogenetic condition, even make a primary diagnosis.

Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

OBJECTIVES: The automatic delineation of organs at risk (OARs) can help doctors make radiotherapy plans efficiently and accurately, and effectively improve the accuracy of radiotherapy and the therapeutic effect. Therefore, this study aims to propose an automatic delineation method for OARs in cervical cancer scenarios of both after-loading and external irradiation. At the same time, the similarity of OARs structure between different scenes is used to improve the segmentation accuracy of OARs in difficult segmentations. METHODS: Our ensemble model adopted the strategy of ensemble learning. The model obtained from the pre-training based on the after-loading and external irradiation was introduced into the integrated model as a feature extraction module. The data in different scenes were trained alternately, and the personalized features of the OARs within the model and the common features of the OARs between scenes were introduced. Computer tomography (CT) images for 84 cases of after-loading and 46 cases of external irradiation were collected as the train data set. Five-fold cross-validation was adopted to split training sets and test sets. The five-fold average dice similarity coefficient (DSC) served as the figure-of-merit in evaluating the segmentation model. RESULTS: The DSCs of the OARs (the rectum and bladder in the after-loading images and the bladder in the external irradiation images) were higher than 0.7. Compared with using an independent residual U-net (convolutional networks for biomedical image segmentation) model [residual U-net (Res-Unet)] delineate OARs, the proposed model can effectively improve the segmentation performance of difficult OARs (the sigmoid in the after-loading CT images and the rectum in the external irradiation images), and the DSCs were increased by more than 3%. CONCLUSIONS Comparing to the dedicated models, our ensemble model achieves the comparable result in segmentation of OARs for different treatment options in cervical cancer radiotherapy, which may be shorten time for doctors to sketch OARs and improve doctor's work efficiency.
Female ; Humans ; Image Processing, Computer-Assisted/methods* ; Machine Learning ; Organs at Risk/radiation effects* ; Radiotherapy Planning, Computer-Assisted/methods* ; Uterine Cervical Neoplasms/radiotherapy*