朝鲜淫羊藿酸性多糖的理化特性及对HepG2细胞脂质堆积的改善作用＊

目的 对比研究朝鲜淫羊藿酸性多糖酯化还原前后的理化特性，并探讨其改善油酸诱导的肝癌HepG2细胞脂质堆积活性的差异。方法 采用高效凝胶渗透色谱法测定朝鲜淫羊藿酸性多糖（EFPA）的均一性和分子量，高效液相色谱法测定EFPA和酯化还原后朝鲜淫羊藿酸性多糖（EFPA-R）的单糖组成；采用油酸（OA）处理HepG2细胞诱导建立脂质蓄积模型，不同浓度EFPA与EFPA-R（10、30、100、300 μg·mL^-1）分别和OA共同作用于细胞24 h，采用CCK-8试剂盒测定细胞存活率，油红O染色观察细胞内脂滴蓄积情况，并采用试剂盒测定细胞内总胆固醇（TC）、甘油三酯（TG）含量。结果 EFPA为成分均一的多糖组分，分子量为125.8 kDa，由甘露糖、葡萄糖、半乳糖、葡萄糖醛酸和阿拉伯糖组成，摩尔比为1.7∶7.4∶1.4∶1.8∶1.0，葡萄糖占比最大，EFPA-R由甘露糖、葡萄糖、半乳糖和阿拉伯糖组成，摩尔比为0.8∶10.6∶2.1∶1.0；在10-300 μg·mL^-1范围内，EFPA和EFPA-R对HepG2细胞的抑制作用较弱，作为给药浓度；与空白组相比，模型组细胞中TC、TG含量显著升高（P < 0.01），细胞内红色脂滴显著增多，与模型组相比，EFPA可显著降低细胞中TC、TG含量（P < 0.01），明显减少细胞内红色脂滴（P < 0.05或P < 0.01），EFPA-R干预后细胞则无明显变化。结论 EFPA可明显改善HepG2细胞脂质堆积情况，且呈现剂量依赖性，而半乳糖醛酸（GalA）的存在可能是其抑制HepG2细胞脂质蓄积的关键因素。     

2011—2020年呼和浩特市流行性腮腺炎流行特征分析

目的 分析呼和浩特市2011—2020年流行性腮腺炎(流腮)流行病学特征,为制定有针对性防控措施提供参考依据。方法 对呼和浩特市2011—2020年流行性腮腺炎的发病数据进行监测分析,描述其分布及趋势。 呼和浩特市2011—2020年共报告流腮病例6 176例,年均发病率20.60/10万,其中2012年发病率最高为40.17/10万,2020年发病率最低为7.08/10万,2012年和2020年发病率差异有统计学意义(χ2=717.641,P<0.001),10年间流腮发病呈波动下降趋势。流腮发病具有明显季节性,11月至次年1月和4—7月出现2个发病高峰。年龄分布以儿童和青少年为主,占总病例数60.83%。流腮病例最多人群是学生,占总病例数的61.08%。 流腮暴发疫情主要发生在中小学校,应加强儿童入托、入学预防接种证查验和流腮疫苗查漏补种工作,必要时开展6～15岁学生为目标人群的应急接种。     

儿童期创伤应对方式自动思维与大学生抑郁特质间的关系

目的探讨儿童期创伤与抑郁特质之间的中介和调节机制,为开展相关研究和干预提供参考。方法2017-2018年采用整群抽样的方法,抽取湖南某高校大一年级2 786名学生完成儿童期创伤问卷、状态特质抑郁问卷、自动思维问卷和特质应对方式问卷的调查。结果儿童期创伤、自动思维和消极应对对抑郁特质起正向的预测作用,积极应对对抑郁特质起负向的预测作用(β值分别为0.12,0.43,0.14,-0.33,P值均<0.05)。自动思维中介儿童期创伤与抑郁特质之间的关系(Bootstrapping法的95%CI为0.03~0.06)。消极应对分别调节了儿童期创伤与自动思维和儿童期创伤与抑郁特质之间的关系(P值均<0.05)。结论儿童期创伤影响抑郁特质,其关系受到应对方式和自动思维的影响。     

探讨中西医护理改善老年高血压患者生活质量的效果

目的探讨中西医结合护理改善老年高血压患者心理状态及生活质量的效果。方法在医院2018年1月—2018年6月就诊的老年高血压患者中选出60例,按照随机数字法的原则,分成研究组(n=30)和参考组(n=30)。参考组采用单一西医护理,研究组在此之外联合中医护理,比较两组患者的临床疗效。结果入院时研究组与参考组SAS、SDS、SF-6评分差异不显著(P>0.05),护理后1月、3月与参考组相比,研究组SAS、SDS评分显著降低(P<0.05),SF-6评分显著升高(P<0.05);研究组患者对中西结合护理满意度为96.67%,参考组患者对西医护理满意度为73.33%,研究组满意度较参考组高,差异显著(P<0.05)。结论中西医结合护理能够有效改善老年高血压患者的心理状态,提高生活质量。     

学龄前儿童心理行为问题与家长健康素养的关系

目的探讨家长健康素养与学龄前儿童心理行为问题的关系,为学龄前儿童心理行为问题早期干预提供参考。方法 2018年5-7月采用分层整群随机抽样的方法,抽取深圳市光明区13所幼儿园4 120名学龄前儿童及其家长,使用长处与困难问卷(SDQ)家长版和深圳市居民健康素养问卷分别对学龄前儿童心理行为问题及家长健康素养水平进行调查;采用非条件Logistic回归分析家长健康素养与儿童心理行为问题的关系。结果学龄前儿童心理行为问题检出率为34.9%(1 436/4 120)。其中困难总分6.5%、情绪问题5.6%、品行问题8.9%、多动注意不能13.4%、同伴交往问题14.4%、亲社会行为9.2%。男童和女童困难总分、情绪问题、品行问题、多动注意不能、同伴交往问题、亲社会行为检出率分别为7.3%和5.5%,4.8%和6.5%,9.7%和7.8%,15.5%和10.7%,15.8%和12.7%,11.1%和6.7%,差异有统计学意义(χ^2值分别为5.626,5.506,4.212,19.846,7.650,23.863,P<0.05)。多因素Logistic回归分析显示,家长健康素养(OR=1.294)、健康知识(OR=1.293)、健康行为(OR=1.412)及健康技能(OR=1.193)水平低是学龄前儿童心理行为问题产生的危险因素(P<0.05)。结论学龄前儿童心理行为问题发生率较高,问题突出,家长健康素养水平低是学龄前儿童心理行为问题的重要影响因素。     

Organic and Inorganic Mercury in Biological Samples of Flouresecent Lamp Industries Workers and Health Risks

Objective The present study aims to investigate the concentrations of Hg and its aspects methyl mercury(Me-Hg) and inorganic mercury(I-Hg) in the biological samples(BSs) of fluorescent lamp industries workers(FLIWs).Methodology Different BSs including red blood cells(RBCs),plasma,urine,hair and nails were collected from the workers exposed to Hg and unexposed persons were selected as control group to measure both the T-Hg concentration as well as its species in different biological samples through quantitative analysis.Health data was collected through questionnaire survey.Results The mean concentrations of T-Hg(31.9 μg/L),Me-Hg(27.7 μg/L),and I-Hg(5.36 μg/L) in RBCs were found significantly(P 0.001) higher among the workers(n = 40) as compared to the control group(n = 40).Similarly the mean Hg concentrations in plasma,urine,hair and nails were also significantly higher among the workers than the control group.The statistical relation between Hg concentration and demographic characteristics observed that workers experience and fish consumption has increased the Hg concentration while age,weight and smoking found no significant effect on Hg concentration in the BSs.Conclusion The study observed that the workers were highly exposed to high concentration of Hg and they are at a high health risk.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.