A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

The Role and features of Student Peer Feedback in Interpreting Training——Preliminary Findings of a Survey of both trainers and students

The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.

Primary sclerosing cholangitis associated colitis: Characterization of clinical, histologic features, and their associations with liver transplantation

BACKGROUND Primary sclerosing cholangitis(PSC)associated inflammatory bowel disease(IBD)is a unique form of IBD(PSC-IBD)with distinct clinical and histologic features from ulcerative colitis(UC)and Crohn disease(CD).In patients with PSC and IBD,the severity of the two disease processes may depend on each other.AIM To study the histologic and clinical features of PSC patients with and without IBD.METHODS We assessed specimens from patients with UC(n=28),CD(n=10),PSC and UC(PSC-UC;n=26);PSC and CD(PSC-CD;n=6);and PSC and no IBD(PSC-no IBD;n=4)between years 1999-2013.PSC-IBD patients were matched to IBD patients without PSC by age and colitis duration.Clinical data including age,gender,age at IBD and PSC diagnoses,IBD duration,treatment,follow-up,orthotopic liver transplantation(OLT)were noted.RESULTS PSC-UC patients had more isolated right-sided disease(P=0.03),and less active inflammation in left colon,rectum(P=0.03 and P=0.0006),and overall(P=0.0005)compared to UC.They required less steroids(P=0.01)and fewer colectomies(P=0.03)than UC patients.The PSC-CD patients had more ileitis and less rectal involvement compared to PSC-UC and CD.No PSC-CD patients required OLT compared to 38%of PSC-UC(P=0.1).PSC-IBD(PSC-UC and PSCCD)patients with OLT had severe disease in the left colon and rectum(P=0.04).CONCLUSION PSC-UC represents a distinct form of IBD.The different disease phenotype in PSC-IBD patients with OLT may support liver-gut axis interaction,however warrants clinical attention and further research.

Determinants of internet addiction among medical students in Casablanca: a cross-sectional study

Objective:This study aimed to estimate the prevalence and determinants of Internet addiction among medical students at the Faculty of Medicine and Pharmacy of Casablanca,Morocco.Methods:This was a cross-sectional study conducted among students at the Faculty of Medicine and Pharmacy in Casablanca between October and March 2020.An online questionnaire was administered to students to collect data and internet addiction was assessed by the Young questionnaire.A score threshold≥50 was adopted to define addiction.Univariate and multivariate logistic regression analyses were used to identify factors associated with internet addiction.Results:Out of a total of 4093 FMPC students enrolled in the 2020-2021 academic year,506 agreed to participate in this study,including 303 females and 203 males.The mean addiction score assessed on the Young scale was(49.08±16.11).The prevalence of Internet addiction was 44.5%(225/506,95% CI:40% to 49%).Multiple regression analysis showed that being older than 20 years(OR=0.17,95% CI:0.40 to 0.64),being female(OR=1.70,95% CI:1.04 to 2.78),being in the dissertation year(6th year)(OR=5.17,95% CI:2.23 to 11.44),having a history of psychiatric consultation(OR=2.64,95% CI:1.34 to 5.21),having divorced parents(OR=2.64,95% CI:1.05 to 5.87),use of sleeping medication(OR=2.9,95% CI:1.05 to 3.70),sleep disorders(OR=2.06,95% CI:1.25 to 3.79),sleep deprivation(OR=2.26,95% CI:1.39 to 3.65),excessive daytime sleepiness(OR=5.39,95% CI:2.19 to 13.24),anxiety disorders(OR=1.47,95% CI:1.18 to 2.30),duration of internet connection(>4 h)(OR=11.43,95% CI:4.85 to 27.66),and having frequent conflicts with parents(OR=2.37,95% CI:1.49 to 3.79)and friends(OR=0.26,95% CI:0.11 to 0.65)were independently associated with internet addiction.Conclusion:The prevalence of Internet addiction among medical students in Casablanca remains high.Targeted action on the determinants would be of great value in prevention.

Drinking Behavior and Associated Factors among Middle School Students in Shanghai,China

The purpose of our study was to assess drinking status in middle school students and to understand the associated factors. The adjusted drinking rates were 50.9%, 39.8%, and 15.1% for lifetime, past-year, and current drinking, respectively.

An Epistaxis Emergency Associated with Multiple Pollutants in Elementary Students

Emergencies of epistaxis in students caused by environmental pollution have rarely been reported to date. This study aimed to explore the cause of an emergency of epistaxis in elementary students by using a field epidemiological investigation. Twenty-two epistaxis cases from a single school with differences in gender, age, and classroom,were diagnosed within a period of 7 days. The air concentration of chromic acid mist （Cr6~） in the electroplating factory area, new campus, and residential area exceeded the limit of uncontrolled emissions. The emission of HCL and HzSO4was also observed. Formaldehyde levels in the classrooms exceeded the limits of indoor air quality. Abnormal nasal mucosa was significantly more frequent in the case group （93.3%） and control group 1 （of the same school） （66.7%） than in control group 2 （from a mountainous area with no industrial zone） （34.8%; P 〈 0.05 and P 〈 0.01, respectively）. On the basis of the pre-existing local nasal mucosal lesions, excessive chromic acid mist in the school＇s surrounding areas and formaldehyde in the classrooms were considered to have acutely irritated the nasal mucosa, causing epistaxis. Several lessons regarding factory site selection, eradication of chemical emissions, and indoor air quality in newly decorated classrooms, should be learned from this emergency.

Factors Associated with Voluntary Cosmetic Depigmentation in Schools in the Collines Department (Benin)

Skin depigmentation is a worrying practice that is gaining popularity, particularly among young girls. However, this practice poses health risks. It also reflects a negative view of black skin color. This was a cross-sectional study carried out between April and May 2023 which involved 1039 female students from schools and universities in the Collines department selected by stratified sampling. Data was collected during a face-to-face interview using a questionnaire providing information on the demographic, socio-cultural, and economic characteristics of the girls. The depigmentation products used were identified as well as the complications caused by the use of these products. Statistical analysis made it possible to calculate the frequencies and logistic regression made it possible to identify the factors associated with depigmentation. The prevalence of depigmentation among the girls surveyed was 78.2%. The main products used were soaps based on mercurial derivative and hydroquinone (21.6%) and lotions based on hydroquinone and corticosteroids (75.7%). The factors associated with the practice of depigmentation were the ethnicity of the respondents (OR = 2.52;95% CI = [0.47 - 13.33], p = 0.001);the average monthly income of the parents (OR = 3.26;95% CI = [1.71 - 6.09], p = 0.003);the opinion of the respondents on depigmentation (OR = 2.48;95% CI = [0.84 - 7.3], p < 0.001) and the value given to light skin versus black skin (OR = 3.41;95% CI = [2.32 - 5.01], p < 0.001). In conclusion, the prevalence of depigmentation among the girls surveyed is high. Consequently, reinforced awareness measures and stricter control of bleaching products are imperative to address this high prevalence of the phenomenon.

Influencing program satisfaction for undergraduate students in STEM areas: The impact of interdisciplinary features, disciplinary connectedness, faculty support, and examination difficulty

Ensuring program satisfaction for undergraduate students in the areas of science,technology,engineering and mathematics(STEM)matters in student retention and education quality improvement.This study explores how four rarely examined variables support from faculty members,interdisciplinary features of STEM program courses,disciplinary connectedness of STEM program core courses,and examination difficulty impact Chinese STEM undergraduates’program satisfaction.With data from 619 Chinese STEM undergraduates,structural equation modeling shows that course satisfaction partially mediates the impact of support from faculty members on program satisfaction,while fully mediating that of interdisciplinary features of STEM program courses and disciplinary connectedness of STEM program core courses on program satisfaction.Examination difficulty exerts no significant impact on program satisfaction neither directly nor indirectly.Support from faculty members impact course satisfaction significantly stronger for junior and senior students than for freshmen and sophomores,while interdisciplinary features of STEM program courses impact course satisfaction stronger for freshmen and sophomores than for juniors and seniors.The study ends with practical implications for the higher education reform in relevant areas.

Prevalence of asthenopia and its risk factors in Chinese college students

·AIM: To determine the prevalence of asthenopia and identify any associated risk factors in the college students in Xi’an, China. · METHODS: From April to September 2012, 1 500 students from five universities in Xi’an were selected according to a multi -stage stratified cluster sampling method. Data on demographic features, lifestyle or dietary habits, health status, living environment conditions, sleep and mental status, and asthenopia symptoms were collected through a self -administered validated questionnaire. Univariate logistic regression and multivariate logistic regression analysis modified by the factor analysis were performed to evaluate risk factors for asthenopia. ·RESULTS: Fifty-seven percent of the college students complained of asthenopia. Statistically significant risk factors for asthenopia in the univariate analysis included 13 variables. Multivariate analysis revealed a significant relationship between the use of computer and asthenopia (OR 1.21, 95% CI: 1.09 to 1.35). Good sleep and mental status (OR 0.86, 95% CI: 0.76 to 0.97), good living environment conditions (OR 0.67, 95% CI: 0.60 to 0.76), and high intake of green leafy vegetables (OR 0.89, 95% CI: 0.80 to 0.98) were found to be strong predictors of decreasing the occurrence of asthenopia complaints. ·CONCLUSION: Asthenopia symptom appeares to be common among college students; and it is strongly associated with computer use, psychosocial state, environment conditions and dietary habits, suggesting that additional studies are warranted to verify these risk factors and establish prevention guidelines, especially for college students. ·

Clinic-pathological features of metabolic associated fatty liver disease with hepatitis B virus infection

BACKGROUND Metabolic associated fatty liver disease(MAFLD)is a novel concept proposed in 2020.AIM To compare the characteristics of MAFLD and MAFLD with hepatitis B virus(HBV)infection.METHODS Patients with histopathologically proven MAFLD from a single medical center were included.Patients were divided into MAFLD group(without HBV infection)and HBV-MAFLD group(with HBV infection).Propensity score matching was utilized to balance the baseline characteristics between two groups.RESULTS A total of 417 cases with MAFLD were included,359(86.1%)of whom were infected with HBV.There were significantly more males in the HBV-MAFLD group than in the MAFLD group(P<0.05).After propensity score matching,58 pairs were successfully matched with no significant differences found in gender,age,body mass index,lipid levels,liver enzymes,and the other metabolic associated comorbidities between the two groups(P>0.05).The rank sum test results showed that the degree of liver steatosis in the MAFLD group was more severe than that in the HBV-MAFLD group,while the degree of inflammation and fibrosis in the liver was less severe(P<0.05).In multivariate analysis,HBV infection was associated with significantly lower grade of hepatic steatosis[odds ratio(OR)=0.088,95%confidence interval(CI):0.027-0.291]but higher inflammation level(OR=4.059,95%CI:1.403-11.742)and fibrosis level(OR=3.016,95%CI:1.087-8.370)after adjusting for age,gender,and other metabolic parameters.CONCLUSION HBV infection is associated with similar metabolic risks,lower steatosis grade,higher inflammation,and fibrosis grade in MAFLD patients.

An Aridity Trend in China and Its Abrupt Feature in Association with the Global Warming

A distinct aridity trend in China in last 100 years is presented by applying a linear fitting to both the climate records and the hydrological records, which is supported by evidence of environmental changes and seems to be associated with a global warming trend during this period.The Mann Kendall Rank statistic test reveals a very interesting feature that the climate of China entered into a dry regime abruptly in about 1920's, which synchronized with the rapid warming of the global temperature at almost the same time.According to an analysis of the meridional profile of observed global zonal mean precipitation anomalies during the peak period of global wanning (1930-1940), the drought occurred in whole middle latitude zone (25°N-55°N) of the Northern Hemisphere, where the most part of China is located in. Although this pattern is in good agreement with the latitude distribution of the difference of zonal mean rates of precipitation between 4 × CO2 and 1 × CO2 simulated by climate model (Manabe and Wetherald, 1983), more studies are required to understand the linkage between the aridity trend in China and the greenhouse effect.The EOF analysis of the Northern Hemisphere sea level pressure for the season of June to August shows an abrupt change of the time coefficient of its first eigenvector from positive to negative in mid-1920's, indicating an enhancement of the subtropical high over Southeast Asia and the western Pacific after that time. This is an atmospheric circulation pattern that is favorable to the development of dry climate in China.

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.

On Developing Students' Communicative Competence in English Classes of Higher Vocational Colleges

This article introduces the theory of communicative competence and explores some possible teaching strategies for developing the students' communicative competence according to the students' features in higher vocational colleges.

The Effect of Training Package Development for Secondary School Students in Thailand Toward ASEAN Community

The purposes of this research were： （1） to create a training package to prepare secondary school students in northeastern of Thailand toward ASEAN （Association of Southeast Asian Nations） Community; （2） to compare the knowledge of secondary school students toward ASEAN before and after use training package; and （3） the expectations of the students in the northeast of their own preparation for the ASEAN community. Experimental research was used in this research. The subject was including 2,000 students who were randomly divided into groups of seven provinces. The statistics used in data analysis were percentage, average, standard deviation, and T-test. The research results showed that： （1） a training package to prepare secondary school students in northeastern of Thailand toward ASEAN Community, the effectiveness index （E.I.） was .57, according to the established criteria; （2） a comparison of pretest and post-test results found the use of cognitive training may vary, statistically significant at the .05 level; （3） an expectation of the secondary school students in the preparation of role into ASEAN Community found that the students who participated in a concept reflect that the knowledge and attitudes to prepare themselves for the ASEAN community.

Association of Elevated Yes-Associated Protein Expression with Gastric Cancer and Its Clinicopathological Features: A Meta-Analysis

Objectives: To evaluate the difference of YAP-positive expression between GC and adjacent tissues, as well as the association of elevated YAP expression with clinicopathological features of GC. Methods: PubMed, Embase, Web of Science databases and the Chinese National Knowledge Infrastructure (CNKI) were searched from inception up to December 2018. The pooled ORs and corresponding 95% CIs were used to assess the strength of association. The heterogeneity among eligible studies was evaluated by the Q-test and I2 values. The sensitivity analysis was performed by sequential omission of individual studies. Moreover, Begg’s test and Egger’s test were used to evaluate publication bias. Results: A total of 2229 patients from 16 studies were included in this meta-analysis. The results showed that positive YAP expression was closely correlated with GC but not adjacent non-tumor tissue (OR = 8.08, 95% CI = 4.41 - 14.80). Additionally, YAP overexpression was found to be associated with more advanced TNM stage (OR = 2.68, 95% CI = 1.61 - 4.48), deeper invasion depth (OR = 2.05, 95% CI = 1.32 - 3.19), and lymph node metastasis (OR = 1.95, 95% CI = 1.29 - 2.96). No significant correlation was observed between YAP overexpression and degree of differentiation (OR = 1.17, 95% CI = 0.63 - 2.16), as well as gender of patients (OR = 1.12, 95% CI = 0.91 - 1.37) or tumor size (OR = 1.11, 95% CI = 0.82 - 1.49) of gastric cancer. Conclusions: This meta-analysis demonstrated that YAP might be a promising diagnostic marker and even a therapeutic target for gastric cancer.

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.

基于校园行为特征的学生就业画像

基于校园行为数据分析学生就业与校园行为之间的关联性是大数据背景下高校就业管理与指导工作的重点。文章以高校本科学生为研究对象,对学生基本信息和校园行为数据进行特征提取和学生标签体系的构建,通过关联规则挖掘学生就业与校园行为之间的关联,探析不同毕业去向学生的行为特征。研究发现:国内读研类学生有4种典型学生群体画像,出国出境类大学生有3种典型学生群体画像,直接就业类大学生有4种典型学生群体画像,不同的学生群体在行为规律性、消费水平和学业水平等方面存在显著区别。文章建议:提供大学生职业生涯规划指导,加强对学生心理状况的关注,开展同学结对帮扶活动。

抗MDA5抗体阳性幼年皮肌炎临床特点及治疗转归分析

目的分析抗黑色素瘤分化相关基因5(MDA5)抗体阳性幼年皮肌炎(JDM)的临床特点及治疗转归。方法收集首都医科大学附属北京儿童医院风湿科2015年6月至2022年2月收治的487例JDM患儿的临床资料,其中41例(8.42%)为抗MDA5抗体阳性者。对其临床特点、疾病评估、治疗药物及疾病转归进行总结分析。结果41例抗MDA5抗体阳性JDM患儿中,最常见的临床表现是皮肤和黏膜异常,其次为肌无力,发热和关节炎也较为常见,部分患儿出现呼吸系统症状和皮肤溃疡。皮肤评估工具(CAT)评分以3~5分占大多数。儿童肌炎评估量表(CMAS)评分为(33.88±9.57)分。间质性肺疾病(ILD)的发病率为87.80%,12.20%的患儿发展为快速进展型ILD(RP-ILD)。5例(12.20%)在病程中出现皮下钙化。90.24%的抗MDA5抗体阳性JDM患儿有高危病征。静脉注射甲基泼尼松龙(IVMP)(63.41%)、静脉注射免疫球蛋白(IVIG)(68.29%)和口服环孢素A(65.85%)是治疗的主要方法。9例(21.95%)患儿口服托法替布。75.61%的患儿表现为单一病程,19.51%的患儿为复发性病程,4.88%的患儿为持续的活动性病程。1年、2年、3年和4年的完全临床应答率分别为65.79%(25/38)、65.38%(17/26)、63.64%(14/22)和70.59%(12/17)。5年完全临床缓解率为14.29%(2/14),2年疾病复发率为19.23%(5/26)。在2年的随访中,95.00%(19/20)的患儿肺部病变完全吸收。随访3个月、6个月、9个月和12个月时,泼尼松的中位剂量分别为1.40 mg/kg、1.00 mg/kg、0.71 mg/kg和0.60 mg/kg,糖皮质激素停用的中位时间为69个月。截至2022年2月,41例患儿中有15例(36.59%)停用糖皮质激素,9例(21.95%)停用所有药物。结论抗MDA5抗体阳性JDM是一种特殊类型的JDM,主要表现为特征性皮疹、肌无力、发热和关节炎,ILD是主要危害。该病完全临床缓解率仍然较低,复发率相对较高。

基于服务漯河市地方文化传播的师范生专业社团建设路径探索

高校肩负着地方文化传播和发展的使命,基于服务漯河市地方特色文化传播目的,结合漯河市地方文化优势,以漯河职业技术学院师范生专业社团为例,分析师范生专业社团在服务地方文化传播方面的不足,提出推动高职院校师范生专业社团服务地方文化传播建设的策略。