1.Localization Research of Medical Social Work Ethics
Chunjiao LIU ; Shuo ZHANG ; Yuxia DENG ; Binbin WU ; Yanrui LIU
Chinese Medical Ethics 2015;(4):608-610
Pass an overview about medical social work ethics and analyzed the reasons.Based on China′s na-tional conditions and the basis of the particularity of medical social work in China, puts forward the paths of the lo-calization of medical social work ethics, namely, improve the adaptability of western medical ethics of social work, realizes the Chinese traditional culture and western medical social work ethics, mutual accommodation, with the so-cialist core values at the head of the medical social work ethic construction in our country.
2.Regulation of intestinal microbiota by Roux-en-Y gastric bypass on patients with obesity or obesity combined with diabetes
Yiqiu WEI ; Jingshen ZHUANG ; Yanrui DENG ; Zhiyong DONG ; Cunchuan WANG ; Shiqi JIA
Chinese Journal of Digestive Surgery 2022;21(11):1452-1460
Objective:To investigate the regulation of intestinal microbiota by Roux-en-Y gastric bypass (RYGB) on patients with obesity or obesity combined with diabetes.Methods:The retrospective and descriptive study was conducted. The stool samples before and after surgery and clinical data of 20 patients with obesity, including 9 simple obesity cases and 11 obesity combined with diabetes cases, who underwent RYGB in the First Affiliated Hospital of Ji′nan University from July 2016 to August 2017 were collected. There were 11 males and 9 females, aged (33±11)years. Observation indicators: (1) changes in composition and structure of intestinal microflora; (2) changes of intestinal microflora in simple obesity patients after operation; (3) changes of intestinal microflora in obesity combined with diabetes patients after operation. Follow up was conducted using telephone interview or outpatient examinations to detect the body mass, the application of antimicrobial agent and the blood glucose control of patients. According to the unified training points, the stool samples were collected and stored into the DNA stabilizer, and then conducted to laboratory analysis within 45 hours. The follow up was up to November 2018. Measurement data with normal distribution were represented as Mean± SD, and independent-samples t test was used for inter-group comparison and paired-samples t test was used for intra-group comparison. Measurement data with skewed distribution were represented as M( Q1, Q3), and Wilcoxon signed rank test of two independent samples was used for inter-group comparison. Count data were described as absolute numbers, and the chi-square test, ANOSIM analysis, linear discriminant (LEfSe) analysis and the Metastats analysis were used for inter-group comparison. Results:(1) Changes in composition and structure of intestinal microflora. The Shannon index of α diversity of preoperative intestinal microflora in simple obesity patients and obesity combined with diabetes patients was 4.37±0.69 and 4.47±0.85, respectively, showing no significant difference between them ( t=0.28, P>0.05). Results of preoperative LEfSe analysis showed that there were differences in the bacterial abundance of Firmicutes and Bacteroidea between simple obesity patients and obesity combined with diabetes patients. The abundances of Parasutterella in simple obesity patients and obesity combined with diabetes patients was 0.000 113 0(0, 0.004 378 2) and 0.008 464 0(0.001 325 7, 0.034 983 1), respectively, showing a significant difference between them ( Z=2.12, P<0.05). Results of preoperative PCoA analysis showed that the contribution rates of principal component 1, principal component 2 and principal component 3 were 24.98%, 22.24% and 16.33% in simple obesity patients and obesity combined with diabetes patients and results of ANOSIM comparison showed that there was no significant difference in preoperative intestinal microflora between them ( r=?0.11, P>0.05). The Shannon index of α diversity of postoperative intestinal microflora in simple obesity patients and obesity combined with diabetes patients was 4.60±0.65 and 4.66±0.40, respectively, showing no significant difference between them ( t=0.24, P>0.05). Results of postoperative LEfSe analysis showed that there were differences in the bacterial abundance of Bacteroidea, Proteus and Firmicutes between simple obesity patients and obesity combined with diabetes patients. The abundances of Morganella and Coprococcus_2 in simple obesity patients and obesity combined with diabetes patients were 0.000 192 0(0.000 011 9,0.001 569 0), 0(0,0) and 0(0,0), 0.000 054 1(0,0.000 419 0), showing significant differences between them ( Z=2.70, 2.29, P<0.05). (2) Changes of intestinal microflora in simple obesity patients after operation. There were 10 genera of bacteria of intestinal bacteria changing after surgery, including 7 species of bacteria increasing in the Firmicutes and the Proteobacteria as Veillonella, Morganella, Granulicatella, Aeromonas, Streptococcus, Rothia and Megasphaera and the bacteria decreasing in the Firmicutes and the Actinobacteria as Ruminococcus_torques_group, Romboutsia and Erysipelo-trichaceae_UCG-003. Results of LEfSe analysis showed that the bacteria significantly enriched in simple obesity patients before surgery were Ruminococcus_torques_group, Romboutsia and Erysipelotri-chaceae_UCG-003, belonging to Firmicutes, and the bacteria significantly enriched in simple obesity patients after surgery were Rothia, Granulicatella, Enterococcus, Streptococcus, Megasphaera, Veillonella, A eromonas and Morganella, belonging to Actinobacteria, Firmicutes and Proteobacteria. (3) Changes of intestinal microflora in obesity combined with diabetes patients after operation. There were 16 bacteria of intestinal bacteria increasing after surgery, including Streptococcus, Veillonella, Haemophilus, Pluralibacter, Gemella, Lachnospiraceae_NC2004_group, Granulicatella,Aeromonas, uncultured_ bacterium_f_ Saccharimonadaceae, R uminiclostridium_9, Butyricicoccus, Fusobacterium, Anaerotruncus, Fusicateni-bacter, Klebsiella and E ubacterium_eligens_group, which belonged to the Firmicutes and the Proteo-bacteria. Results of LEfSe analysis showed that the bacteria significantly enriched in obesity combined with diabetes patients before surgery were Fusicatenibacter, Tyzzerella_3 and Butyricicoccus, belonging to the Firmicutes, and the bacteria significantly enriched in obesity combined with diabetes patients after surgery were Gemella, Granulicatella, Enterococcus, Streptococcus, Lachnospiraceae_NC2004_group, Eubacterium_eligens_group, Anaerotruncus, Ruminiclostridium_9, Anaeroglobus, Veillonella, Fusobacterium, uncultured_bacterium_f_Saccharimonadaceae, Aeromonas, Klebsiella, Pluralibacter, Proteus and Haemophilus, belonging to the Firmicutes and the Proteobacteria. Conclusions:RYGB can significantly increases the intestinal microflora abundance in simple obesity patients and obesity combined with diabetes patients. The two types of patients have specific changes in intestinal microflora at the genus level.
3. Investigation of dose-dependent association between bedtime routines and sleep outcomes in infants and toddlers
Fang YANG ; Qingmin LIN ; Guanghai WANG ; Yanrui JIANG ; Yuanjin SONG ; Shumei DONG ; Wanqi SUN ; Yujiao DENG ; Yan WANG ; Xiaojuan XU ; Qi ZHU ; Fan JIANG
Chinese Journal of Pediatrics 2017;55(6):439-444
Objective:
To investigate the current bedtime routine among Chinese children less than 3 years of age and explore its dose-dependent association with sleep duration and sleep quality.
Method:
Healthy full-term born children aged 0-35 months were selected by stratified cluster random sampling method from 8 provinces in China following the "Hospital of Province-City-County" sampling technical route during 2012-2013.Brief Infant Sleep Questionnaire(BISQ) was used to assess sleep conditions of these children.Children′s personal and family information was obtained by Shanghai Children′s Medical Center Socio-demographic Questionnaire.Both of these questionnaires were filled in by parents. The effects of bedtime routine on children′s sleep duration and quality were analyzed by multivariate analysis of variance.
Result:
The children′s average age was(12±10) months(