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Although observational studies have shown that abnormal systemic iron status is associated with an increased risk of heart failure (HF), it remains unclear whether this relationship represents true causality. We aimed to explore the causal relationship between iron status and HF risk. Two-sample Mendelian randomisation (MR) was applied to obtain a causal estimate. Genetic summary statistical data for the associations (p < 5 × 10−8) between single nucleotide polymorphisms (SNPs) and four iron status parameters were obtained from the Genetics of Iron Status Consortium in genome-wide association studies involving 48,972 subjects. Statistical data on the association of SNPs with HF were extracted from the UK biobank consortium (including 1088 HF cases and 360,106 controls). The results were further tested using MR based on the Bayesian model averaging (MR-BMA) and multivariate MR (MVMR). Of the twelve SNPs considered to be valid instrumental variables, three SNPs (rs1800562, rs855791, and rs1799945) were associated with all four iron biomarkers. Genetically predicted iron status biomarkers were not causally associated with HF risk (all p > 0.05). Sensitivity analysis did not show evidence of potential heterogeneity and horizontal pleiotropy. Convincing evidence to support a causal relationship between iron status and HF risk was not found. The strong relationship between abnormal iron status and HF risk may be explained by an indirect mechanism. [ABSTRACT FROM AUTHOR]

What is known and objectives: Haematological toxicity including thrombocytopenia, anaemia and leucopenia is the main adverse events of linezolid (LZD) therapy. This study aimed to investigate the risk factors for LZD‐induced haematological toxicity and define the threshold of plasma trough concentration to minimize the haematological toxicity. Methods: 145 patients who received LZD for more than 10 days were retrospectively reviewed to determine the incidence of LZD‐induced haematological toxicity. Meanwhile, the risk factors of haematological toxicity were confirmed by univariate and multivariate logistic regression analysis. Results and discussion: 9 (6.2%) patients developed leucopenia, while 52 (35.9%) and 26 (17.9%) patients developed thrombocytopenia and anaemia, respectively. The estimated glomerular filtration rate (eGFR) <90 ml/min/1.73 m2 (OR, 2.744; 95% CI, 1.117–6.734; p = 0.028) and baseline platelet count <200 × 109/L (OR, 6.817; 95% CI, 2.870–16.193; p < 0.0001) were found to be significant risk factors for LZD‐related thrombocytopenia. Aspartate aminotransferase (AST) >80 U/L (OR, 4.844; 95% CI, 1.207–19.451; p = 0.026) and eGFR <90 ml/min/1.73 m2 (OR, 7.132; 95% CI, 2.088–24.357; p = 0.002) were the risk factors for LZD‐related anaemia. However, no significant risk factors were identified for LZD‐related leucopenia. Moreover, LZD plasma trough concentration >8 mg/L [OR, 3.047; 95% CI, 1.233–7.539; p = 0.016] could be a predictor for the development of thrombocytopenia and anaemia. What is new and conclusion: Hepatic and/or renal dysfunction are the risk factors for LZD‐related haematological toxicity, while the target plasma trough concentration within 8 mg/L via dose reduction could minimize the haematological toxicity induced by LZD. [ABSTRACT FROM AUTHOR]

A semi-structured document has more structured information compared to an ordinary document, and the relation among semi-structured documents can be fully utilized. In order to take advantage of the structure and link information in a semi-structured document for better mining, a structured link vector model (SLVM) is presented in this paper, where a vector represents, a document, and vectors’ elements are determined by terms, document structure and neighboring documents. Text mining based on SLVM is described in the procedure of K-means for briefness and clarity: calculating document similarity and calculating cluster center. The clustering based on SLVM performs significantly better than that based on a conventional vector space model in the experiments, and its F value increases from 0.65–0.73 to 0.82–0.86. [ABSTRACT FROM AUTHOR]