Objective To realize the utilization of antibacterial drugs in pediatric inpatients in Children's Hospital for clinical reference of rational use of drugs. Methods By a retrospective study, 180 cases in pediatric inpatients were randomly sampled in Children's Hospital from July to December 2012, and were analyzed in utility rate, antibiotics varieties, administrational frequency,single dose,combination use,prophylaxis time,courses of drug use,etc. Results The use of antibacterial drugs in pediatric inpatients was 110 cases (61.1%) and used in combination was 55 cases (50%) . Leading the list in terms of DDDs was cephalosporins,followed byβ-lactam and its enzyme inhibitor complex preparations. The improper medications frequency was 12 cases (10.9%), irrational single dose was 7 cases (6.4%), and irrational course of treatment was 9 cases (8.2%) . Conclusion The utilization of antibacterial drugs in Children's Hospital is basically rational, but there still exist some irrational drug uses,such as antibiotics varieties,no indication, irrational dosage,long duration and irrational combination. So, it is necessary to enhance the administration of antibiotics use and improve the level of clinical reasonable application of antibiotics.

The concept is cell of the theory. Starting from the concept, is the first and important step in building the modern basic theory of traditional Chinese medicine. Based on the early thought of Changes from phenomenon description to entity elucidation, we firstly introduce conceptual change in this paper and point out its basic meaning and role. To accurately apply the new concept, we have analyzed and demonstrated the concept and its expression object, the concept words' expression forms, as well as the concept explanatory power in details, and pointed out the problems because of ignoring these recognition in researching the concept of chinese medicine and its solutions. We have summarized the laws of the international scientific community realizing the conceptual change focus on the object, explore the unknown, propose new knowledge. and proposed the definition of conceptual change of the basic theory of traditional Chinese medicine. According to preliminary findings we have generalized the goal and standard to achieve the conceptual change of the basic theory of traditional Chinese medicine and gave examples of proof. As such we have draw an conclusion: the concepts of the basic theory of traditional Chinese medicine form the scientific concepts of the modern basic theory of traditional Chinese medicine through conceptual change, and thus guiding the theory, teaching and thinking reform of the Chinese medicine.

[Abstract ] Objective Stenosis is a common complication of Crohn′s disease (CD), different treatments for different cau-ses.The article aimed to investigate bowel stenosis by the application of MRI diffusion-weighted imaging(DWI) and explore its value of identifying CD. Methods From Jan 2014 to Jun 2014, 31 patients with histologically proven CD (18 males and 13 females;mean age:38.90 ±13.65 years) were recruited in this approved retrospective study .All patients underwent conventional 3.0T MRI and DWI sequences .According to the most serious stenosis part identified by MRI , DWI sequence examination was added and the apparent diffusion coefficient (ADC) of the lesion was measured.All patients would undergo colonoscopy in 24 hours.According to the endo-scopic manifestations and pathological results , the patients were divided into inflammatory group (n=21) and fibrotic group (n=10). We observed the difference of ADC between two groups and worked out the cutoff points . Results In the inflammatory group , the ADC value andthe mean ADC value of stenosis bowel wall were (1.01 ±1.83) ×103 mm2/s and (1.40 ±0.23) ×103 mm2/s, whereas (0.53 ±1.03) ×103 mm2/s and (0.80 ±0.16) ×103 mm2/s in the fibrotic group(P<0.05).The area under receiver operating characteristic curve was 0.981 (95%confidence interval 0.943-1.000), taking 1.11 ×103mm2/s as the cutoff point.The sensitivity of low ADC values in detecting inflammatory bowels was 90.5%, and the specificity of high ADC values in excluding inflammatory bowels was 100%. Conclusion Different pathological components limit the movement of water molecular at different degrees , therefore quantitative parameters can be acquired by measuring ADCs , which contributes to the identification and diagnosis of CD secondary bowel stenosis.

Objective Visceral pain in patients with inflammatory bowel disease (IBD) may be associated with the abnormal processing of pain in the central nervous system.The aim of the study is to investigate the characteristic changes of brain functions in the IBD patients using resting-state functional magnetic resonance imaging (rs-fMRI) with the amplitude of low-frequency fluctuation (ALFF) algorithm.Methods This study included 27 cases of IBD treated in our hospital from December 2015 to August of 2016 and 21 healthy volunteers as normal controls.We recorded the high-resolution structure imaging and rs-fMRI data, compared the brain activities of the two groups patients by ALFF analysis, and evaluated the correlation of the ALFF values with the clinical parameters of the IBD patients.Results Compared with the normal control group, the IBD patients showed significantly decreased ALFF values in the medial frontal gyrus, right putamen, right insula, left middle cingulate gyrus (MCC), and bilateral supplementary motor region (P<0.05), increased ALFF values in the middle frontal gyrus, left superior frontal gyrus, and medial prefrontal lobe region (P<0.05).The ALFF values in the inferior parietal lobule, precuneus and MCC of the IBD patients were correlated negatively with the blood sedimentation rate (r=-0.537,-0.588, and-0.588, P<0.05), disease course (P<0.05), and C-reactive protein (CRP) level (P<0.05), while that in the medial frontal gyrus positively with the CRP level (r=-0.623, P<0.001).Conclusion IBD patients have abnormal ALFF values in various brain regions, mainly in those involved in the processing of visceral pain and emotion.

Objective:To explore the impact of nurses' social support and general self-efficacy on narrative nursing knowledge, attitudes, and practice, and their effect paths.Methods:This study was a cross-sectional survey. From October to December 2022, convenient sampling was used to select 343 nurses from three Class Ⅲ Grade A hospitals in Xi'an, Shaanxi Province as the research subject. Nurses were surveyed using the General Information Questionnaire, Knowledge-Attitude-Practice Survey of Clinical Nurses on Narrative Nursing, Social Support Rating Scale (SSRS) , and General Self-Efficacy Scale (GSES) . Pearson correlation analysis was used to explore the correlation between nurses' narrative nursing knowledge, attitudes and practice, social support, and general self-efficacy. Multiple linear regression analysis was used to analyze the influencing factors of nurses' narrative nursing knowledge, attitudes, and practice. The path analysis between social support, general self-efficacy, and narrative nursing knowledge, attitudes, and practice was conducted using the AMOS 22.0 software stepwise method and Bootstrap test. A total of 343 questionnaires were distributed, and 341 valid questionnaires were collected, with an effective response rate of 99.42%.Results:Among 341 nurses, the total score of Knowledge-Attitude-Practice Survey of Clinical Nurses on Narrative Nursing was (86.83±11.85) , the total score of SSRS was (40.15±7.62) , and the total score of GSES was (27.04±6.15) . The path analysis model showed that the three dimensions (objective support, subjective support, and support utilization) in SSRS had an impact on narrative nursing knowledge, attitudes, and practice, with effect value of 0.131, 0.052, and 0.316, respectively ( P<0.01) . Subjective support could indirectly affect narrative nursing knowledge, attitudes, and practice, while objective support could directly affect narrative nursing knowledge, attitudes, and practice. The support utilization could directly or indirectly affect narrative nursing knowledge, attitudes, and practice through the mediating effect of general self-efficacy. Conclusions:Nurses' social support has an impact on narrative nursing knowledge, attitudes, and practice through general self-efficacy. Managers and policy makers should enhance nurses' social support, enhance their general self-efficacy, increase the improvement of nurses' narrative nursing knowledge, attitudes and practice, and promote the implementation of humanistic care.

Objective:To study the role of myocardial work parameters in early identification of myocardial injury in neonatal asphyxia.Methods:From July 2020 to December 2021, neonates diagnosed with mild neonatal asphyxia admitted to the Department of Neonatology of our hospital within 24 h after birth were prospectively enrolled into the asphyxia group. Neonates without asphyxia during the same period were selected as the control group and matched with the asphyxia group for gender, gestational age and birth weight at a ratio of 1:1~1:2. The asphyxia group was subgrouped into preterm asphyxia group and term asphyxia group. All neonates received echocardiography within 24 h after birth. Multiple parameters were measured including M-mode, two-dimensional image, Doppler image, global longitudinal strain (GLS) and myocardial work parameters [global work index (GWI), global constructive work (GCW), global wasted work (GWW), global work efficiency (GWE)]. The level of serum N-terminal pro brain natriuretic peptide (NT-proBNP) was recorded in the asphyxia group. The data were compared between the asphyxia group and the control group. Correlations between myocardial work parameters and other parameters were analyzed.Results:A total of 33 cases were in the asphyxia group and 43 cases were in the control group. The preterm asphyxia group (18 cases) showed significantly lower GWI and GCW than the preterm control group (18 cases) [GWI: (702±153) mmHg vs. (879±205) mmHg, GCW: (1 016±221) mmHg vs. (1 200±271) mmHg] ( P<0.05). No differences existed in GLS, GWW and GWE. The term asphyxia group (15 cases) showed significantly lower GWW than the term control group (25 cases) [45.0 (30.0, 65.0) mmHg vs. 71.0 (35.5,85.5) mmHg] ( P<0.05). No differences existed in GLS, GWI, GCW and GWE. GWI was negatively correlated with serum NT-proBNP level ( r=-0.327, P<0.05). Conclusions:GWI and GCW may indicate myocardial injury in preterm neonates with mild asphyxia.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.