Loss of mitochondrial ClpP,Lonp1, and Tfam triggers transcriptional induction of Rnf213, a susceptibility factor for moyamoya disease

neurogenetics - Human RNF213, which encodes the protein mysterin, is a known susceptibility gene for moyamoya disease (MMD), a cerebrovascular condition with occlusive lesions and compensatory...     

Examination of potential novel biochemical factors in relation to prostate cancer incidence and mortality in UK Biobank

Background Although prostate cancer is a leading cause of cancer death, its aetiology is not well understood. We aimed to identify novel biochemical factors for prostate cancer incidence and mortality in UK Biobank.Methods A range of cardiovascular, bone, joint, diabetes, renal and liver-related biomarkers were measured in baseline blood samples collected from up to 211,754 men at recruitment and in a subsample 5 years later. Participants were followed-up via linkage to health administrative datasets to identify prostate cancer cases. Hazard ratios (HRs) and 95% confidence intervals were calculated using multivariable-adjusted Cox regression corrected for regression dilution bias. Multiple testing was accounted for by using a false discovery rate controlling procedure.Results After an average follow-up of 6.9 years, 5763 prostate cancer cases and 331 prostate cancer deaths were ascertained. Prostate cancer incidence was positively associated with circulating vitamin D, urea and phosphate concentrations and inversely associated with glucose, total protein and aspartate aminotransferase. Phosphate and cystatin-C were the only biomarkers positively and inversely, respectively, associated with risk in analyses excluding the first 4 years of follow-up. There was little evidence of associations with prostate cancer death.Conclusion We found novel associations of several biomarkers with prostate cancer incidence. Future research will examine associations by tumour characteristics.Subject terms: Predictive markers, Prostate cancer, Risk factors     

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case–control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case–control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR_1SD) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR_1SD = 0.77, 95% confidence interval 0.66–0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR_1SD = 0.72, 0.57–0.90), or lysophosphatidylcholines (OR_1SD = 0.81, 0.69–0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR_1SD = 0.77, 0.61–0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.     

Experimentelle Untersuchungen über die Entzündung der Hornhaut

Ohne ZusammenfassungHierzu Taf. XV–XVI.     

p38丝裂原激活蛋白激酶对人胚胎肾293细胞一氧化氮合酶基因转录的调控

目的探讨p38 丝裂原激活蛋白激酶(MAPK)家族四种亚型对诱导型一氧化氮合酶(iNOS)基因的转录调控。方法以人胚胎肾293(HEK 293)细胞为靶细胞,采用脂质体(LPS)介导的细胞基因共转染技术、荧光素酶报告基因技术,分别将FLAG-tagged p38 MAPK 4种亚型、含有鼠iNOS基因启动子区的荧光素酶报告基因质粒(piNOS-Luc)、空载体(pcDNA3)、β-半乳糖苷酶表达质粒(pCMV-β)共转染,检测并比较荧光素酶相对活性。结果(1)未加刺激时,在HEK 293细胞中,p38 MAPK中仅有p38α能够诱导iNOS基因启动子的转录活性;(2)在LPS刺激下,p38 MAPK 4种亚型均能够诱导iNOS启动子的转录活性,其中,p38β所诱导的转录活性最高,p38α的诱导作用亦很明显。结论(1)LPS能够在HEK 293细胞中诱导iNOS基因转录活性;(2)在HEK 293细胞中,p38 MAPK参与了静息时及LPS刺激下对iN-OS基因的转录调控。     

骨钙素酶免疫检测方法的研究和应用

目的：建立骨钙素酶免疫测定方法，并应用于临床检测血清骨钙素。方法：应用了3种检测模式，根据检测灵敏度、剂量反应曲线的形态进行分析，选择出合适的模式进一步应用于临床血清标本的骨钙素检测。结果：所采用的骨钙素单克隆抗体是钙离子依赖型的。固相抗原竞争法适合于临床定量测定骨钙素。最低可测限为1．4μg/L;20μg/L骨钙素样品批内CV＝3．98％，批间CV＝12．67％。测定正常献血员140名（年龄17－45岁），第5－95百分位点的骨钙素含量，男性为7．5－15．0μg/L女性为7.0-17.5μg/L。70例疑有骨质疏松症患者血清骨钙素含量2．91－30．20μg/L，以第95百分位点的骨钙素含量为cut-off值，升高5例。与进口试剂盒（Novocalcin,USA）相比较，有较好的相关性，r=0.81。结论：固相抗原竞争法酶免疫测定可以灵敏地检测血清中骨钙素含量，有实用价值。     

间接测热法和反向Fick氏法测定心脏手术病人术后氧耗量的比较

目的 采用反向Fick氏法和间接测热法测定心脏手术后病人的全身氧耗,并比较两种测量方法的相关性和准确性。方法8例心脏手术术后病人,分别在入ICU后2h和6h时同时采用反向Fick氏和间接测热法测定病人的全身氧耗量。结果 间接测热法和反向Fick法测得氧耗分别是(16±30)ml·min-1·m-2和(127±23)ml·min-1·m-2,前者的测定结果显著高于后者(P<0.01)。相关分析显示两者有较好的相关性(r=0.92,P<0.01)。采用Bland和Altman统计分析提示两种测定结果的平均偏离值为(35.5±13.4)ml·min-1·m-2,其95％的分布范围是(9～62)ml·min-1·m-2,提示两种测量结果间的一致性较差。结论 两种方法测定心脏病人术后全身氧耗的结果有明显差异,其中反向Fick氏法的测定结果误差大、准确性差,而间接测热法是较好的临床选择。     

Measurement of diet in a large national survey: comparison of computerized and manual coding of records in household measures

A diary method using household measures was employed to obtain dietary records in a large national prospective survey and a computer program, DIDO (Diet In Data Out), was designed for direct entry of the diaries. The accuracy of this computerized coding system was examined alongside that of the manual coding used for a similar diary in a previous wave, 7 years earlier, of the same survey. Accuracy was assessed by analysis of the errors in the coded and checked records by stringent re-checking of nominal 2% random subsamples of the diet diaries coded by each method. The mean time to code and check each of the 2086 7-day records in the whole survey using DIDO was 58 minutes (SD 30) compared with reported results of 1–4 hours for manual methods. The mean error rate of computerized coding and checking with DIDO was 2.3% (SD 2.1; range 0–8.9) per diary in the subsample. Correcting these mistakes made insignificant changes to the calculated mean energy and nutrient intakes for the subsample. The percentage of individuals changing to an adjacent third of nutrient distribution after correcting unambiguous errors ranged from none (for alcohol) to 11% (for carbohydrate and calcium intake). The mean error rate on a similar subsample of diaries from the earlier survey which had been coded manually was significantly higher at 5.9% (SD 4.1; range 0–17) per diary. Emphasis is laid on the importance, in coding, of dealing with ambiguities in the subjects' records, since this can affect the accuracy and the precision of the nutrient results obtained. We conclude that the DIDO coding method has the advantages of greater accuracy, speed, consistency and efficient data handling, and affords greater data accessibility for checking, compared with manual systems.