Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To evaluate the safety and efficacy of ultrasound-guided percutaneous negative pressure suction and minimally invasive rotatory excision technique for the treatment of complex encapsulated lesions.Methods:A total of 48 patients(48 lesions) with complex encapsulated lesions who underwent ultrasound-guided percutaneous negative pressure suction and minimally invasive rotatory excision technique at Xi′an Chest Hospital from January to October 2023 were retrospectively enrolled, including 39 cases of encapsulated abscess, 7 cases of encapsulated effusion, and 2 cases of encapsulated haematoma; the distribution of the bacterial flora of the abscesses were as follows: 24 cases of tuberculous abscess, 14 cases of bacterial abscess, 1 case of bacterial combined bacterial-fungal abscess, and 7 cases of encapsulated effusion were tuberculous pleurisy, and the clinical data were analysed retrospectively. The maximum upper and lower diameters, right and left diameters, and anterior and posterior diameters of the lesions were measured by ultrasound before and after the operation. The patients′ various biochemical indicators (C-reactive protein, white blood cell count, neutrophil count, erythrocyte sedimentation rate) were detected. The intraoperative and postoperative complications, postoperative outcomes, and postoperative clinical symptoms were recorded.Results:Of the 48 patients, 39 were cured and discharged after negative pressure suction and rotatory excision technique, and 9 patients were cured and discharged after surgical incision and drainage of the lesions. The overall effective rate of negative pressure suction and rotatory excision treatment reached 81.25%, and the average number of days of tube placement was (11.81±7.22) days, and the average number of days of follow-up was (35.77±19.39) days. Compared with preoperative values, the upper and lower diameters, the left and right diameters, and the anterior and posterior diameters of the lesions were all reduced after operation [5.80 (4.95, 7.95)cm vs 8.00 (6.00, 11.82)cm, 4.00 (3.25, 5.00)cm vs 5.85 (4.52, 7.65)cm, 1.80 (1.00, 2.90)cm vs 3.40 (2.50, 6.15)cm, all P<0.01]; and postoperative C-reactive protein, white blood cell count and neutrophil count all decreased (all P<0.05). Before operation there were 31 cases of local swelling, 16 cases of pain, 12 cases of activity limitation, 12 cases of fever, 7 cases of chest tightness, and 6 cases of shortness of breath, and during postoperative follow-up, there were 4 cases of local swelling, 5 cases of pain, and 4 cases of activity limitation. The symptoms of fever, chest tightness, and shortness of breath all disappeared, and there was a statistically significant difference between preoperation and postoperation (all P<0.05). There were no adverse events or complications associated with the intraoperative and postoperative follow-up of negative pressure suction and rotatory excision treatment. Conclusions:Ultrasound-guided percutaneous negative pressure suction and invasive rotatory excision technique for the treatment of complex encapsulated lesions can significantly reduce lesion size, reduce inflammatory response and improve patient symptoms, which is a safe, effective and minimally invasive technique.

Objective:To effectively quantify and evaluate the quality of different deformation registration algorithms, in order to enhance the possibility of implementing deformation registration in clinical practice.Methods:The Jacobian determinant mean (JDM) is proposed based on the Jacobian determinant (JD) of displacement vector field (DVF), and the Jacobian determinant error (DJDE) is introduced by incorporating the JD of the inverse DVF. The optical flow method (OF-DIR) and fast demons method with elastic regularization (FD-DIR) were tested on nasopharyngeal and lung cancer datasets. Finally, JDM and DJDE with the Jacobian determinant negative percentage (JDNP), inverse consistency error (ICE) and normalized mean square error (NMSE) were used to evaluate the registration algorithms and compare the differences evaluation indicators in different tumor images and different algorithms, and the receiver operating curve (ROC) was analyzed in evaluation.Results:In lung cancer, OF-DIR outperformed FD-DIR in terms of JDM, NMSE, DJDE and ICE, and the difference was statistically significant( z = -2.24, -4.84, t = 4.01, 6.54, P<0.05). In nasopharyngeal carcinoma, DJDE, ICE and NMSE of OF-DIR were superior to FD-DIR, and the difference was statistically significant ( t = 4.46, -7.49, z = -2.22, P<0.05), but there was no significant difference in JDM ( P>0.05). In lung cancer and nasopharyngeal carcinoma, JDNP of OF-DIR was worse than that of FD-DIR, and the difference was statistically significant ( z = -4.29, -4.02, P<0.01). In addition, DJDE is more specific and sensitive on ROC curve (AUC=0.77), and has different performance result for tumor images at different sites. Conclusions:The JDM and DJDE evaluation metrics proposed are effective for deformation registration algorithms. OF-DIR is suitable for both lung cancer and nasopharyngeal carcinoma, while the influence of organ motion on the registration effect should be considered when using FD-DIR.

In 2020, the prevalence of abnormal spinal curvature among 54 079 students in Shandong Province was 1.54%. The multivariate logistic regression model analysis showed that, compared with those in primary school, economically underdeveloped areas, and non-residential schools, students in middle and high schools, economically average areas, and residential schools had a higher risk of abnormal spinal curvature, with OR (95%CI) values of 2.029 (1.662-2.476), 2.746 (2.208-3.416), 2.237 (1.740-2.875) and 2.057 (1.705-2.483), respectively. Compared with those in economically underdeveloped areas, who were underweight, who had seat adjustments≤1 time per academic year, and who had physical education classes≤1 per week, students in economically developed areas, who were normal weight, overweight, and obese, who had seat adjustments≥2 times per academic year, and who had physical education classes 2-3 or≥4 per week, had a lower risk of abnormal spinal curvature, with OR (95%CI) values of 0.690 (0.521-0.915), 0.722 (0.546-0.955), 0.535 (0.389-0.735), 0.383 (0.274-0.535), 0.835 (0.711-0.980), 0.561 (0.474-0.663) and 0.491 (0.315-0.766), respectively.
Humans ; Risk Factors ; Prevalence ; Spinal Curvatures ; Schools ; Students

In 2020, the prevalence of abnormal spinal curvature among 54 079 students in Shandong Province was 1.54%. The multivariate logistic regression model analysis showed that, compared with those in primary school, economically underdeveloped areas, and non-residential schools, students in middle and high schools, economically average areas, and residential schools had a higher risk of abnormal spinal curvature, with OR (95%CI) values of 2.029 (1.662-2.476), 2.746 (2.208-3.416), 2.237 (1.740-2.875) and 2.057 (1.705-2.483), respectively. Compared with those in economically underdeveloped areas, who were underweight, who had seat adjustments≤1 time per academic year, and who had physical education classes≤1 per week, students in economically developed areas, who were normal weight, overweight, and obese, who had seat adjustments≥2 times per academic year, and who had physical education classes 2-3 or≥4 per week, had a lower risk of abnormal spinal curvature, with OR (95%CI) values of 0.690 (0.521-0.915), 0.722 (0.546-0.955), 0.535 (0.389-0.735), 0.383 (0.274-0.535), 0.835 (0.711-0.980), 0.561 (0.474-0.663) and 0.491 (0.315-0.766), respectively.
Humans ; Risk Factors ; Prevalence ; Spinal Curvatures ; Schools ; Students

Objective: To analyze the clinical epidemiological characteristics including composition of pathogens , clinical characteristics, and disease prognosis acute bacterial meningitis (ABM) in Chinese children. Methods: A retrospective analysis was performed on the clinical and laboratory data of 1 610 children <15 years of age with ABM in 33 tertiary hospitals in China from January 2019 to December 2020. Patients were divided into different groups according to age,<28 days group, 28 days to <3 months group, 3 months to <1 year group, 1-<5 years of age group, 5-<15 years of age group; etiology confirmed group and clinically diagnosed group according to etiology diagnosis. Non-numeric variables were analyzed with the Chi-square test or Fisher's exact test, while non-normal distrituction numeric variables were compared with nonparametric test. Results: Among 1 610 children with ABM, 955 were male and 650 were female (5 cases were not provided with gender information), and the age of onset was 1.5 (0.5, 5.5) months. There were 588 cases age from <28 days, 462 cases age from 28 days to <3 months, 302 cases age from 3 months to <1 year of age group, 156 cases in the 1-<5 years of age and 101 cases in the 5-<15 years of age. The detection rates were 38.8% (95/245) and 31.5% (70/222) of Escherichia coli and 27.8% (68/245) and 35.1% (78/222) of Streptococcus agalactiae in infants younger than 28 days of age and 28 days to 3 months of age; the detection rates of Streptococcus pneumonia, Escherichia coli, and Streptococcus agalactiae were 34.3% (61/178), 14.0% (25/178) and 13.5% (24/178) in the 3 months of age to <1 year of age group; the dominant pathogens were Streptococcus pneumoniae and the detection rate were 67.9% (74/109) and 44.4% (16/36) in the 1-<5 years of age and 5-<15 years of age . There were 9.7% (19/195) strains of Escherichia coli producing ultra-broad-spectrum β-lactamases. The positive rates of cerebrospinal fluid (CSF) culture and blood culture were 32.2% (515/1 598) and 25.0% (400/1 598), while 38.2% (126/330)and 25.3% (21/83) in CSF metagenomics next generation sequencing and Streptococcus pneumoniae antigen detection. There were 4.3% (32/790) cases of which CSF white blood cell counts were normal in etiology confirmed group. Among 1 610 children with ABM, main intracranial imaging complications were subdural effusion and (or) empyema in 349 cases (21.7%), hydrocephalus in 233 cases (14.5%), brain abscess in 178 cases (11.1%), and other cerebrovascular diseases, including encephalomalacia, cerebral infarction, and encephalatrophy, in 174 cases (10.8%). Among the 166 cases (10.3%) with unfavorable outcome, 32 cases (2.0%) died among whom 24 cases died before 1 year of age, and 37 cases (2.3%) had recurrence among whom 25 cases had recurrence within 3 weeks. The incidences of subdural effusion and (or) empyema, brain abscess and ependymitis in the etiology confirmed group were significantly higher than those in the clinically diagnosed group (26.2% (207/790) vs. 17.3% (142/820), 13.0% (103/790) vs. 9.1% (75/820), 4.6% (36/790) vs. 2.7% (22/820), χ²=18.71, 6.20, 4.07, all P<0.05), but there was no significant difference in the unfavorable outcomes, mortility, and recurrence between these 2 groups (all P>0.05). Conclusions: The onset age of ABM in children is usually within 1 year of age, especially <3 months. The common pathogens in infants <3 months of age are Escherichia coli and Streptococcus agalactiae, and the dominant pathogen in infant ≥3 months is Streptococcus pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. ABM should not be excluded even if CSF white blood cell counts is within normal range. Standardized bacteriological examination should be paid more attention to increase the pathogenic detection rate. Non-culture CSF detection methods may facilitate the pathogenic diagnosis.
Adolescent ; Brain Abscess ; Child ; Child, Preschool ; Escherichia coli ; Female ; Humans ; Hydrocephalus ; Infant ; Infant, Newborn ; Male ; Meningitis, Bacterial/epidemiology* ; Retrospective Studies ; Streptococcus agalactiae ; Streptococcus pneumoniae ; Subdural Effusion ; beta-Lactamases

Objective: To investigate the prevalence, awareness, treatment and control status of dyslipidemia among females aged ≥35 years old across China. Methods: Participants were selected by stratified multistage random sampling method in the "Twelfth Five-Year Plan" National Science and Technology Support Project "Survey on the Prevalence of Important Cardiovascular Diseases and Key Technology Research in China" project. This study is a retrospective, cross-sectional study. A total of 17 418 females aged 35 years and over were included in the current study. The basic information such as age, medical history and menopause was collected by questionnaire. The blood lipid parameters were derived from clinical laboratory examinations. The prevalence of dyslipidemia and the rate of awareness, treatment, and control of dyslipidemia were analyzed in females aged 35 years and over. Results: The age of participants was (56.2±13.0) years old, and the prevalence of dyslipidemia was 33.1% (5 765/17 418). The prevalence rates of high total cholesterol, hypertriglyceridemia, low HDL-C and high LDL-C were 9.7% (1 695/17 418), 11.1% (1 925/17 418), 10.9% (1 889/17 418) and 7.3% (1 262/17 418), respectively. The prevalence of dyslipidemia increased with age and the prevalence of dyslipidemia in women who were not married, Han, menarche age>16 years, obesity, central obesity, alcohol consumption, diabetes, hypertension and family history of cardiovascular disease were higher than those without such characteristics (P<0.05). There were 10 432 (59.9%) menopausal females in this cohort and prevalence of dyslipidemia of these participants was 38.8% (4 048/10 432), which was higher than that of non-postmenopausal females (24.6%, 1 717/6 986) (P<0.05). The awareness rates, treatment rates and control rates of dyslipidemia were 33.9% (1 953/5 765), 15.1% (870/5 765) and 2.5% (143/5 765) respectively among females aged 35 years and over in China. Conclusion: The prevalence of dyslipidemia in Chinese females aged 35 years and over is high, and its awareness, treatment, and control rates need to be optimized.
Adult ; Aged ; Cardiovascular Diseases ; China/epidemiology* ; Cross-Sectional Studies ; Dyslipidemias/epidemiology* ; Female ; Humans ; Middle Aged ; Obesity/epidemiology* ; Prevalence ; Retrospective Studies ; Risk Factors

Objective To analyze the epidemiological characteristics of COVID-19 cases reported in Xianyang City from January to February 2020. Methods We retrospectively studied 17 COVID-19 patients diagnosed in Xianyang Central Hospital. The patients were characterized clinically and epidemiologically. Results The 17 patients included 10 male and 7 female, with an average age of(39.59±17.31)years. The median interval of time between onset and diagnosis was four days(1-10 days), whereas the median duration of COVID-19 was 16 days(3-23 days). Of the patients, six were mild, 10 were pneumonia, and one was severe. A total of 15 patients had fever as the onset, accompanied by fatigue, sore throat, sputum, vomit, muscle soreness; the other two patients were asymptomatic. There were no complications documented in all the patients. Patients had low levels of white blood cells and lymphocytes. Chest CT scan showed diverse diffuse ground-glass shadow. Eleven patients had travel history in Wuhan before the onset, four patients had contact with people who had travel history or residence history in Wuhan, and the other two patients did not report epidemiological exposure history. In addition, four of the 17 patients were clustered cases. Conclusion General population is susceptible to COVID-19. The majority of the confirmed cases have epidemiological exposure history. Routine examination, including white blood cell, lymphocyte count and CT scan may facilitate early diagnosis.

This study explores the construction and application of online platform for medical continuing education based on libraries and academic conferences. With the consent of the experts participating in the conference, the contents of the conferences are recorded and made into learning videos for continuing education. By constructing network servers, cloud services, and other platforms, the online continuing education can be realized, and such procedures as giving credits also can be completed. And combined with other professional continuing education platforms, a complete continuing education system can be constructed. We have initially designed out the construction mode of medical continuing education platform. The construction of medical continuing education platform has enhanced the class or position of the symposium organization institutions, facilitated the continuing education of professionals and technical personnel, with good social and economic effects, which is worthy of promotion.