Huntington's disease (HD) is a fatal neurodegenerative disease caused by a genetic expansion of the CAG repeat region in the huntingtin (HTT) gene. Studies in HD mouse models have shown that artificial miRNAs can reduce mutant HTT, but evidence for their effectiveness and safety in larger animals is lacking. HD transgenic sheep express the full-length human HTT with 73 CAG repeats. AAV9 was used to deliver unilaterally to HD sheep striatum an artificial miRNA targeting exon 48 of the human HTT mRNA under control of two alternative promoters: U6 or CβA. The treatment reduced human mutant (m) HTT mRNA and protein 50-80% in the striatum at 1 and 6 months post injection. Silencing was detectable in both the caudate and putamen. Levels of endogenous sheep HTT protein were not affected. There was no significant loss of neurons labeled by DARPP32 or NeuN at 6 months after treatment, and Iba1-positive microglia were detected at control levels. It is concluded that safe and effective silencing of human mHTT protein can be achieved and sustained in a large-animal brain by direct delivery of an AAV carrying an artificial miRNA.
Patients with depression are at increased risk for a range of comorbid diseases, with, however, unclear explanations. In this large community-based cohort study of the UK Biobank, 24,130 patients diagnosed with depression were compared to 120,366 matched individuals without such a diagnosis. Follow-up was conducted from 6 months after the index date until death or the end of 2019, for the occurrence of 470 medical conditions and 16 specific causes of death. The median age at the time of the depression diagnosis was 62.0 years, and most of the patients were female (63.63%). During a median follow-up of 4.94 years, 129 medical conditions were found to be significantly associated with a prior diagnosis of depression, based on adjusted Cox regression models. Using disease trajectory network analysis to visualize the magnitude of disease–disease associations and the temporal order of the associated medical conditions, we identified three main affected disease clusters after depression (i.e., cardiometabolic diseases, chronic inflammatory diseases, and diseases related to tobacco abuse), which were further linked to a wider range of other conditions. In addition, we also identified three depression-mortality trajectories leading to death due to cardiovascular disease, respiratory system disease and malignant neoplasm. In conclusion, an inpatient diagnosis of depression in later life is associated with three distinct network-based clusters of medical conditions, indicating alterations in the cardiometabolic system, chronic status of inflammation, and tobacco abuse as key pathways to a wide range of other conditions downstream. If replicated, these pathways may constitute promising targets for the health promotion among depression patients.
Background: IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide and up to 40% will develop end-stage renal disease (ESRD) within 20 years. However, predicting which patients will progress to ESRD is difficult. The purpose of this study was to develop a predictive model which could accurately predict whether IgAN patients would progress to ESRD. Methods: Six machine learning algorithms were used to predict whether IgAN patients would progress to ESRD: logistic regression, random forest, support vector machine (SVM), decision tree, artificial neural network (ANN), k nearest neighbors (KNN). Nineteen demographic, clinical, pathologic and treatment parameters were used as input for the prediction models. Results: Random forest is best able to predict progression to ESRD. The model had accuracy of 93.97% and sensitivity and specificity of 80.60% and 95.27%, respectively. Conclusions: Machine learning algorithms can effectively predict which patients with IgA nephropathy will progress to end stage renal disease.
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