The relationships among monocyte subsets,miRNAs and inflammatory cytokines in patients with acute myocardial infarction

Background

Acute myocardial infarction (AMI) causes irreversible myocardial damage and release of inflammatory mediators, including cytokines, chemokines and miRNAs. We aimed to investigate changes in the levels of cytokines (IL-6, TNF-α and IL-10), miRNAs profiles (miR-146 and miR-155) and distribution of different monocyte subsets (CD14++CD16-, CD14++CD16+, CD14+CD16++) in the acute and post-healing phases of AMI.

2016至2018年某医院真菌血流感染者流行病学特征及耐药性分析

目的分析真菌性血流感染的病原菌分布以及耐药特征，为真菌血流感染的早期合理用药提供理论依据。 方法回顾性分析武汉大学人民医院2016年1月至2018年12月收治的真菌性血流感染者的菌群、科室分布以及耐药性。 结果入组192例真菌血流感染者的血培养样本中共分离192株真菌，其中白色念珠菌检出率为31.77%（61/192），其次热带念珠菌检出率为18.75%（36/192）；重症医学科检出率最高为33.85%（65/192）。所有菌株均对两性霉素B敏感，对其他抗菌药物耐药率分别为5-氟胞嘧啶4.49%（9/192）、伊曲康唑5.73%（11/192）、氟康唑10.94%（21/192）和伏立康唑11.46%（22/192）；除两性霉素B外，2016至2018年真菌对其他抗菌药物的耐药率均逐年上升，其中2018年所分离192株光滑念珠菌对伊曲康唑耐药菌率达46.7%。 结论真菌血流感染病原菌以念珠菌属为主，对目前抗真菌药物具有较高敏感性，但耐药率逐年上升，加强监测血培养病原菌变化及耐药趋势对指导临床用药至关重要。     

SI和DBP对女性产后出血的风险预测效果

目的考察休克指数(shock index,SI)和舒张压(diastolic blood pressure,DBP)对女性产后出血的风险预测效果。方法选取2016年1月～2018年12月在我院行产检并分娩的320例产后出血孕妇作为研究组,另外选取同期进行正常分娩的健康孕妇240例作为对照组,比较两组患者的一般临床资料、血红蛋白(hemoglobin,Hb)、舒张压(diastolic blood pressure,DBP)、收缩压(systolic blood pressure,SBP)、心率(heart rate,HR)及休克指数(shock index,SI),并采用Ordinal逻辑回归分析各参数对产后出血的风险预警。结果两组患者在年龄、孕周、BMI及新生儿体重相比,差异无统计学意义(P0.05),而孕次相比,差异具有统计学意义(P0.05);与对照组产后24 h相比,研究组产后24 h患者的DBP、SBP及Hb均显著降低,而HR和SI显著升高(P0.05);与对照组产前相比,产后24 h患者的SBP、HR及SI显著降低(P0.05);与研究组产前相比,产后24 h患者的DBP、SBP、HR及Hb均显著降低,而SI显著升高,差异具有统计学意义(P0.05);Ordinal逻辑回归分析结果显示,休克指数和舒张压对产后出血具有预警作用(P0.05)。结论休克指数和舒张压可作为评估女性产后出血的风险预测指标,临床应密切监护。     

Ultrasound-Assist Extrusion Methods for the Fabrication of Polymer Nanocomposites Based on Polypropylene/Multi-Wall Carbon Nanotubes

Isotactic polypropylenes (iPP) with different melt flow indexes (MFI) were used to fabricate nanocomposites (NCs) with 10 wt % loadings of multi-wall carbon nanotubes (MWCNTs) using ultrasound-assisted extrusion methods to determine their effect on the morphology, melt flow, and electrical properties of the NCs. Three different types of iPPs were used with MFIs of 2.5, 34 and 1200 g/10 min. Four different NC fabrication methods based on melt extrusion were used. In the first method melt extrusion fabrication without ultrasound assistance was used. In the second and third methods, an ultrasound probe attached to a hot chamber located at the exit of the die was used to subject the sample to fixed frequency and variable frequency, respectively. The fourth method is similar to the first method, with the difference being that the carbon nanotubes were treated in a fluidized air-bed with an ultrasound probe before being used in the fabrication of the NCs with no ultrasound assistance during extrusion. The samples were characterized by MFI, Optical microscopy (OM), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), electrical surface resistivity, and electric charge. MFI decreases in all cases with addition of MWCNTs with the largest decrease observed for samples with the highest MFI. The surface resistivity, which ranged from 10¹³ to 10⁵ Ω/sq, and electric charge, were observed to depend on the ultrasound-assisted fabrication method as well as on the melt flow index of the iPP. A relationship between agglomerate size and area ratio with electric charge was found. Several trends in the overall data were identified and are discussed in terms of MFI and the different fabrication methods.     

Clinical and subclinical cardiovascular disease in female SLE patients: Interplay between body mass index and bone mineral density

Background and aims

Since accelerated atherosclerosis has been reported in systemic lupus erythematosus (SLE), predictive biomarkers of cardiovascular disease (CVD) are needed. Among non-traditional risk factors, bone mineral density (BMD) has been related to CVD. However, its role in SLE remains controversial. This study aims to analyze the associations of subclinical atherosclerosis with traditional and non-traditional CV risk factors.