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Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
Background: There is no international consensus up to which age women with a diagnosis of triple-negative breast cancer (TNBC) and no family history of breast or ovarian cancer should be offered genetic testing for germline BRCA1 and BRCA2 (gBRCA) mutations. Here, we explored the association of age at TNBC diagnosis with the prevalence of pathogenic gBRCA mutations in this patient group.
Methods: The study comprised 802 women (median age 40 years, range 19–76) with oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor type 2 negative breast cancers, who had no relatives with breast or ovarian cancer. All women were tested for pathogenic gBRCA mutations. Logistic regression analysis was used to explore the association between age at TNBC diagnosis and the presence of a pathogenic gBRCA mutation.
Results: A total of 127 women with TNBC (15.8%) were gBRCA mutation carriers (BRCA1: n = 118, 14.7%; BRCA2: n = 9, 1.1%). The mutation prevalence was 32.9% in the age group 20–29 years compared to 6.9% in the age group 60–69 years. Logistic regression analysis revealed a significant increase of mutation frequency with decreasing age at diagnosis (odds ratio 1.87 per 10 year decrease, 95%CI 1.50–2.32, p < 0.001). gBRCA mutation risk was predicted to be > 10% for women diagnosed below approximately 50 years.
Conclusions: Based on the general understanding that a heterozygous mutation probability of 10% or greater justifies gBRCA mutation screening, women with TNBC diagnosed before the age of 50 years and no familial history of breast and ovarian cancer should be tested for gBRCA mutations. In Germany, this would concern approximately 880 women with newly diagnosed TNBC per year, of whom approximately 150 are expected to be identified as carriers of a pathogenic gBRCA mutation.
Objective: TGF-β2 (TGF-β, transforming growth factor beta), the less-investigated sibling of TGF-β1, is deregulated in rodent and human liver diseases. Former data from bile duct ligated and MDR2 knockout (KO) mouse models for human cholestatic liver disease suggested an involvement of TGF-β2 in biliary-derived liver diseases.
Design: As we also found upregulated TGFB2 in liver tissue of patients with primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC), we now fathomed the positive prospects of targeting TGF-β2 in early stage biliary liver disease using the MDR2-KO mice. Specifically, the influence of TgfB2 silencing on the fibrotic and inflammatory niche was analysed on molecular, cellular and tissue levels.
Results: TgfB2-induced expression of fibrotic genes in cholangiocytes and hepatic stellate cellswas detected. TgfB2 expression in MDR2-KO mice was blunted using TgfB2-directed antisense oligonucleotides (AON). Upon AON treatment, reduced collagen deposition, hydroxyproline content and αSMA expression as well as induced PparG expression reflected a significant reduction of fibrogenesis without adverse effects on healthy livers. Expression analyses of fibrotic and inflammatory genes revealed AON-specific regulatory effects on Ccl3, Ccl4, Ccl5, Mki67 and Notch3 expression. Further, AON treatment of MDR2-KO mice increased tissue infiltration by F4/80-positive cells including eosinophils, whereas the number of CD45-positive inflammatory cells decreased. In line, TGFB2 and CD45 expression correlated positively in PSC/PBC patients and localised in similar areas of the diseased liver tissue.
Conclusions: Taken together, our data suggest a new mechanistic explanation for amelioration of fibrogenesis by TGF-β2 silencing and provide a direct rationale for TGF-β2-directed drug development.