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To study the causality relationship between diastolic blood pressure (DBP) and hypertensive renal disease with renal failure (HRDRF) and the mediating role of heart failure (HF) in the causality relationship by network Mendelian randomization (MR).

To enhance the performance of neural machine translation (NMT) and ameliorate the detrimental impact of high uncertainty in monolingual data during the self-training process, a self-training NMT model based on priority sampling was proposed. Initially, syntactic dependency trees were constructed and the importance of monolingual tokenization was assessed using grammar dependency analysis. Subsequently, a monolingual lexicon was built, and priority was defined based on the importance of monolingual tokenization and uncertainty. Finally, monolingual priorities were computed, and sampling was carried out based on these priorities, consequently generating a synthetic parallel dataset for training the student NMT model. Experimental results on a large-scale subset of the WMT English to German dataset demonstrate that the proposed model effectively enhances NMT translation performance and mitigates the impact of high uncertainty on the model.

Studies have suggested that the progress of most kidney diseases from occurrence to course and subsequent related complications are closely related to inflammatory reaction. Increased common leukocytes count in the family (neutrophils, eosinophils, basophils, lymphocytes, etc.) are also involved in the tissue damage of kidney diseases. However, these studies are only traditional observational studies, which cannot prove whether there is a causal relationship between these four kinds of leukocytes count and kidney diseases. We aim to explore the causal relationship between these four kinds of leukocytes count and kidney diseases by Mendelian randomization (MR). Large sample size of the genome-wide association database of four cell traits (neutrophil, basophil, lymphocyte, and eosinophil cell counts) in the leukocyte family were used as exposure variables. The outcome variables were various renal diseases (including chronic renal failure, acute renal failure, hypertensive heart or/and kidney disease, hypertensive renal disease, disorders resulting from impaired renal tubular function, and type 1 diabetes with renal complications). The covariates used in multivariable MR are also four cell traits related to blood cells (neutrophil, basophil, lymphocyte, and eosinophil cell counts). Instrumental variables and single nucleotide polymorphic loci were identified (P < 5 × 10−8. Linkage disequilibrium R2 < 0.001). The causal relationships were studied by inverse variance weighted (IVW), weighted median, and MR-Egger regression. Sensitivity analysis was also performed. In our study, IVW analysis results showed that increased neutrophil cell count was a risk factor for chronic renal failure (OR = 2.0245861, 95% CI = 1.1231207–3.649606, P = 0.01896524), increased basophil cell count was a risk factor for chronic renal failure (OR = 3.975935, 95% CI = 1.4871198–10.62998, P = 0.005942755). Basophil cell count was not a risk factor for acute renal failure (OR = 1.160434, 95% CI = 0.9455132–1.424207, P = 0.15448828). Increased basophil cell count was a protective factor for hypertensive heart and/or renal disease (OR = 0.7716065, 95% CI = 0.6484979–0.9180856, P = 0.003458707). Increased basophil cell count was a risk factor for disorders resulting from impaired renal tubular function (OR = 1.648131, 95% CI = 1.010116–2.689133, P = 0.04546835). Increased lymphocyte cell count was a risk factor for hypertensive renal disease (OR = 1.372961, 95% CI = 1.0189772–1.849915, P = 0.03719874). Increased eosinophil cell count was a risk factor for type 1 diabetes with renal complications (OR = 1.516454, 95% CI = 1.1826453–1.944482, P = 0.001028964). Macrophage inflammatory protein 1b levels was a protective factor for renal failure (OR = 0.9381862, 95% CI = 0.8860402–0.9934013, P = 0.02874872). After multivariable MR was used to correct covariates (neutrophil, basophil, and lymphocyte cell counts), the correlation effect between increased eosinophil cell counts and type 1 diabetes with renal complications was still statistically significant (P = 0.02201152). After adjusting covariates (neutrophil, basophil, and eosinophil cell counts) with multivariable MR, the correlation effect between increased lymphocyte cell counts and hypertensive renal disease was still statistically significant (P = 0.02050226). This study shows that increased basophils can increase the relative risk of chronic renal failure and renal tubular dysfunction, and reduce the risk of hypertensive heart disease and/or hypertensive nephropathy, while increased basophil cell count will not increase the relative risk of acute renal failure, increased neutrophil cell count can increase the risk of chronic renal failure, increased lymphocyte cell count can increase the relative risk of hypertensive nephropathy, and increased eosinophil cell count can increase the relative risk of type 1 diabetes with renal complications. Macrophage inflammatory protein 1b levels was a protective factor for renal failure.