Objective: To explore the network structure of eating behaviors with anxiety, depression and perceived stress in adolescents, so as to provide a basis for effective prevention and intervention of eating behavior problems and negative emotions in adolescents. Methods: Based on the Psychology and Behavior Investigation of Chinese Residents (2021) database, the study was conducted among 3 087 adolescents. Sakata Eating Behavior Scale Short From(EBS-SF) was used to investigate their eating behaviors. The Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder Scale-7 Item(GAD-7), and Perceived Stress Questionnaire-3 Item (PSQ-3) were used to evaluate their depression, anxiety and perceived stress. Network analysis method was applied to construct a network of eating behaviors and negative emotional symptoms among adolescents, so as to evaluate the centrality, bridge strength, stability and accuracy of each item. Results: The total scores of eating behaviors, depression,anxiety and stress perception in adolescents were 17.41±4.53,6.95±6.08,4.86±5.03,9.34±3.80,respectively. The symptom with the highest intensity and expected impact was "I am only satisfied when I buy more food than I need", with a node intensity and expected impact value of 4.37. The nodes Depression and Anxiety were the most closely connected(weight=0.87). There were no statistically significant differences in the network structure( M =0.13,0.11) and network connection strength(female and male:4.16,4.06, s =0.10;urban and rural areas:4.08,4.07, s =0.01) between different sexes and residents ( P >0.05). Conclusion The negative impact of comorbidities such as anxiety, depression, perceived stress and eating behaviors among adolescents can be reduced through targeted prevention and intervention of core symptoms and bridging symptoms.

Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer.Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer,who were randomly divided into a training dataset(60%)and a testing dataset(40%).Using univariate analysis,multiple logistic regression analysis,and LASSO regression analysis,we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer.We employed an integrated learning approach that combined GBM,XGBOOST,and MLP algorithms for optimization of the model performance through parameter adjustments.Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age,Gn initiation dose,number of assisted reproduction cycles,and number of embryos transferred.The variables included in the LASSO model selection included female age,FSH levels,duration and initial dose of Gn usage,number of assisted reproduction cycles,retrieved oocytes,embryos transferred,endometrial thickness on HCG day,and progesterone level on HCG day.The nomogram demonstrated an accuracy of 0.642(95%CI:0.605-0.679)in the training dataset and 0.652(95%CI:0.600-0.704)in the validation dataset.The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725(95%CI:0.680-0.770)in the training dataset and 0.718(95%CI:0.675-0.761)in the validation dataset.Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer.Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer,who were randomly divided into a training dataset(60%)and a testing dataset(40%).Using univariate analysis,multiple logistic regression analysis,and LASSO regression analysis,we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer.We employed an integrated learning approach that combined GBM,XGBOOST,and MLP algorithms for optimization of the model performance through parameter adjustments.Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age,Gn initiation dose,number of assisted reproduction cycles,and number of embryos transferred.The variables included in the LASSO model selection included female age,FSH levels,duration and initial dose of Gn usage,number of assisted reproduction cycles,retrieved oocytes,embryos transferred,endometrial thickness on HCG day,and progesterone level on HCG day.The nomogram demonstrated an accuracy of 0.642(95%CI:0.605-0.679)in the training dataset and 0.652(95%CI:0.600-0.704)in the validation dataset.The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725(95%CI:0.680-0.770)in the training dataset and 0.718(95%CI:0.675-0.761)in the validation dataset.Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

Objective To explore the feasibility of harmonic waves analysis for observing morphological brain network changes in patients with depressive disorder(DD).Methods Whole brain 3D high resolution T1WI of 55 DD patients(DD group)and 46 normal controls(NC group)were acquired.Six kinds of morphological features brain network were constructed with FreeSurfer tool,including the number of brain region vertices,surface area,gray matter volume,average cortical thickness,Gaussian curvature and fold index.Laplace operator was applied to obtain common harmonic wave.The harmonic power of different morphological features and the gray matter volume in different brain regions were compared between groups.Results No significant difference of total harmonic energy was found between groups.The specific harmonic wave energies were significantly different between groups,including the number of brain region vertices corresponding to the 2nd,6th,15th,44th and 57th harmonic waves,surface area corresponding to the 2nd,6th,16th and 57th harmonic waves,gray matter volume corresponding to the 2nd,12th,13th,15th and 57th harmonic waves,average cortical thickness corresponding to the 2nd,19th,35th,36th and 44th harmonic waves,Gaussian curvature corresponding to the 34th,40th,54th and 57th harmonic waves,as well as fold index corresponding to the 5th,16th,21st and 57th harmonic waves.Gray matter volumes of transverse temporal gyrus in left hemisphere in DD group were significantly larger than that in NC group(t=2.900,P=0.004).Conclusion Harmonic waves analysis was feasible for observing morphological brain network changes in DD patients.

Objective:To investigate the value of nomogram based on radiomics features of CT enterography (CTE) combined with clinical characteristics to predict secondary loss of response (SLOR) after infliximab (IFX) treatment in patients with Crohn′s disease (CD).Methods:This study was a case-control study. Clinical and imaging data of 155 patients with CD diagnosed at the First Affiliated Hospital of Anhui Medical University from March 2015 to July 2022 were retrospectively collected. The patients were divided into a training set ( n=108) and a testing set ( n=47) in the ratio of 7∶3 by stratified sampling method. All patients were treated according to the standardized protocol and were classified as SLOR (43 in the training set and 18 in the testing set) and non-SLOR (65 in the training set and 29 in the testing set) according to treatment outcome. Based on the data from the training group, independent clinical predictors of SLOR after IFX treatment were screened in the clinical data using univariate and multivariate logistic regression analysis to establish a clinical model. Intestinal phase images were selected to be outlined layer by layer along the margin of the lesion to obtain the volume of the region of interest to extract the radiomics features. The radiomics features were screened using univariate analysis and the minimum absolute shrinkage and selection operator to establish the radiomics model. Multivariate logistic regression analysis was used to build a combined clinical-radiomics model based on the screened clinical independent predictors and radiomics characters, then a nomogram was drawn. The predictive efficacy of the 3 models for SLOR after IFX treatment was assessed by receiver operating characteristic curves, and the area under the curve (AUC) was calculated. The decision curve analysis was applied to evaluate the clinical utility of the models. Results:Disease duration ( OR=1.983, 95% CI 1.966-2.000, P=0.046) and intestinal stenosis ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as the independent predictors of SLOR in the clinical data, and a clinical model was established. Totally 9 radiomics features were included in the radiomics model. The AUCs of clinical, radiomics, and combined models for predicting SLOR after IFX treatment in CD patients were 0.691 (95% CI 0.591-0.792), 0.896 (95% CI 0.836-0.955), and 0.910 (95% CI 0.855-0.965) in the training set, and 0.722 (95% CI 0.574-0.871), 0.866 (95% CI 0.764-0.968), and 0.889 (95% CI 0.796-0.982) in the testing set. Decision curve analysis in the testing set showed higher net clinical benefits for both the radiomics model and combined model than the clinical model, and combined model had higher net clinical benefits than the radiomics model over most threshold probability intervals. Conclusions:CTE-based radiomics model can effectively predict SLOR after IFX treatment in patients with CD, and a combined model by incorporating clinical characteristics of disease duration and intestinal stenosis can further improve the predictive efficacy.

Objective To discuss the value of clinical radiomic nomogram(CRN)and deep convolutional neural network(DCNN)in distinguishing atypical pulmonary hamartoma(APH)from atypical lung adenocarcinoma(ALA).Methods A total of 307 patients were retrospectively recruited from two institutions.Patients in institu-tion 1 were randomly divided into the training(n=184:APH=97,ALA=87)and internal validation sets(n=79:APH=41,ALA=38)in a ratio of 7∶3,and patients in institution 2 were assigned as the external validation set(n=44:APH=23,ALA=21).A CRN model and a DCNN model were established,respectively,and the performances of two models were compared by delong test and receiver operating characteristic(ROC)curves.A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification.Results The areas under the curve(AUCs)of DCNN model were higher than those of CRN model in the training,internal and external validation sets(0.983 vs 0.968,0.973 vs 0.953,and 0.942 vs 0.932,respectively),however,the differences were not statistically significant(p=0.23,0.31 and 0.34,respectively).With a radiologist-AI com-petition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS cat-egories in ALA than radiologists.Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA,with the former performing better.AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.

Objective To construct a radiomics nomogram combining clinical and a radiomics signature for distinguishing type Ⅱpapillary renal cell carcinoma(pRCC)from atypical clear cell renal cell carcinoma(ccRCC).Methods Clinical and CT data of patients with pathologically confirmed type Ⅱ pRCC(62 cases)and atypical ccRCC(56 cases)were analyzed.A random sample was divided into a training set(82 cases)and a test set(36 cases)in a ratio of 7∶3.Clinical factors were screened to construct clinical factor models.A total of 1 595 radiomics features of tumors were extracted from the corticomedullary phase CT images and based on the most effective features to construct a radiomics signature and calculate the radiomics score(Rad-score).A radiomics nomogram was constructed by combining the Rad-score and independent clinical factors.Receiver operating characteristic(ROC)curve was used to assess the clini-cal usefulness of the models.Decision curve analysis(DCA)was used to assess the difference between the models.Results The radiomics signature showed good discrimination in training set area under the curve(AUC)0.894[95％confidence interval(CI)0.834-0.947]and test set AUC 0.879(95％CI 0.774-0.963).The AUC of the clinical factors model in training set and test set were 0.725(95％CI 0.646-0.804)and 0.698(95％CI 0.567-0.819).The AUC of the radiomics nomogram in training set and test set were 0.901(95％CI 0.840-0.953)and 0.901(95％CI 0.809-0.975).DCA demonstrated the radiomics nomogram outmatched the clinical factors model and radiomics signature in the aspects of clinical usefulness.Conclusion Radiomics nomogram based on enhanced CT can provide good prediction of type Ⅱ pRCC and atypical ccRCC preoperatively,improve the diagnostic accuracy and provide guidance for future clinical treatment.

Objective:To investigate the stability and feasibility of improved silent MRA technique based on hybrid-arterial spin labeling(ASL) for imaging intracranial arterial stenosis.Methods:From September 2019 to May 2020, totally 35 patients with suspected intracranial vascular stenosis in Department of Neurology of Northern Jiangsu People′s Hospital were enrolled in this study. Silent MRA and improved silent MRA based on hybrid-ASL technique were performed respectively. The acquisition noise (noise measurement and subjective score) of two kinds of MRA examination were evaluated respectively. Two neuroradiologists performed image quality scoring and signal-to-noise ratio (SNR) measurement of intracranial arteries (including internal carotid artery, vertebrobasilar artery, anterior cerebral artery, middle cerebral artery, and posterior cerebral artery) in the two kinds of MRA images using a double-blind, completely randomized method. Independent sample t-test was used to compare the image quality and SNR of two kinds of MRA images in each segment. Two experts assessed the degree of stenosis at the site of confirmed intracranial artery stenosis. Kappa test was used to assess interobserver and intermodel agreement. Results:There was no significant difference in acquisition noise between improved silent MRA and silent MRA ( P>0.05). In all five segments measured, the image quality scores of internal carotid artery [(4.40±0.49)scores], anterior cerebral artery[(4.30±0.33)scores] and middle cerebral artery [(4.46±0.34)scores] in improved silent MRA were higher than those in silent MRA images [(4.02±0.43)scores, (4.02±0.31)scores, (4.02±0.31)scores; t=2.825, 2.877, 1.683, all P<0.05)]. The SNR of internal carotid artery (9.11±1.23) and middle cerebral artery (8.77±1.87) in improved silent MRA images was higher than that in silent MRA images (7.83±1.33, 8.06±2.67, respectively; t=11.154, 3.268, both P<0.05). A total of 24 patients (38 lesions) with intracranial vascular stenosis were diagnosed by CTA. Improved silent MRA (Kappa=0.89, 95%CI 0.82-0.95) and silent MRA (Kappa=0.85, 95%CI 0.77-0.92) were highly consistent among observers in evaluating the degree of cerebrovascular stenosis.The results of improved silent MRA were highly consistent with those of CTA (Kappa=0.92, 95%CI 0.87-0.98), and those of silent MRA were highly consistent with those of CTA (Kappa=0.85, 95%CI 0.77-0.92). Conclusions:The improved silent MRA is feasible to improve the imaging quality and signal uniformity through efficient marking based on keeping the low noise features. In the diagnosis of intracranial stenosis and occlusive disease, the stability of improved silent MRA imaging improves the diagnostic efficiency of stenosis to a certain extent.

Objective To investigate the clinical curative effect of oral and acupoint application of traditional chinese medicine combined with Azithromycin for the children with Mycoplasma Pneumonia. Methods A total of 160 children with mycoplasma pneumonia were divided into two groups according to digital random table method, with 80 cases in each group. The patients of the two groups were given conventional treatment to relieve cough and reduce phlegm; On the basic treatment of conventional treatment, the control group were treated with azithromycin, wile the treatment group were treated with oral traditional chinese medicine Tinghuang runfei decotion and acupointion application of Tinglizi, Baijiezi, Shengnanxing, Dahuang, and Bingpian. The treatment of both groups last 3 weeks. The time of defervescence, the disappearance time of cough and pulmonaryrales of the two groups were compared, and the serum levels of inter leukin 8 (IL-8), tumor necrosis factor-a (TNF-a), thrombin regulatory proteins (TM), D-dimer were compared before and after treatment. The total effect rate and the incidence of adverse events were compared.Results The defervesce time (3.79 ± 1.68 dvs. 4.88 ± 1.61 d,t=3.846), disappearance time of cough (8.21 ± 2.42 dvs. 10.35 ± 2.60 d,t=5.389) and disappearance time of pulmonary rales (6.21 ± 1.89 dvs. 7.78 ± 2.08 d,t=4.997) in the treatment group were significantly less than those in the control group (P<0.01). The levels of IL-8 (9.98 ± 4.26 ng/Lvs.14.18 ± 4.82 ng/L, t=5.840), TNF-a (20.78 ± 5.93ng/Lvs. 26.07 ± 6.42 ng/L,t=5.414), TM (9.63 ±  2.88μg/Lvs. 13.08 ± 3.37μg/L,t=6.961), D-dimer levels (0.09 ± 0.04 ng/Lvs. 0.15 ± 0.06 ng/L,t=7.442) the treatment group were significantly better than those in the control group (P<0.01). The total effect rate in the treatment gruop was significantly higher than that of the control group (97.5%vs. 88.8%;χ2=4.783,P=0.029), but there was no significant difference in the incidence of adverse events between the two groups (16.3%vs. 10.0%;χ2=1.370,P=0.272).Conclusions Oral and acupointion of traditional chinese medicine Tinghuang runfei decotion combined with azithromycin could relieve the symptoms, improve the clincal effect and show its safety for the children with mycoplasma pneumonia.

Purpose To observe the functional expression of calcium sensing receptor ( CaSR) in human gastric cancer SGC-7901 cell line, the effect of CaSR on intracellular calcium, cell proliferation and migration of SCG-7901. Methods The expression and distribu-tion of CaSR were detected by Western blotting and immunofluorescence observation in SGC-7901. The intracellular concentration of free calcium ( [ Ca2+] i ) was determined by confocal laser scanning microscopy. MTT, flow cytometry and scratch test were used to an-alyze the impact of CaSR the proliferation and the migration capabilities of SGC-7901 cell. Results CaSR protein was expressed in SGC-7901. Extracellular calcium or calindol significantly increased the expression of [Ca2+]i, CaSR and E-cadherin;In addition, the migration capabilities were decreased. Conclusion CaSR is expressed in SGC-7901. The activation of CaSR induces the expression of E-cadherin, and decreases migration ability.