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Colorectal cancer (CRC) screening programs help diagnose cancer precursors and early cancers and help reduce CRC mortality. However, currently recommended tests, the fecal immunochemical test (FIT) and colonoscopy, have low uptake. There is therefore a pressing need for screening strategies that are minimally invasive and consequently more acceptable to patients, most likely blood based, to increase early CRC identification. MicroRNAs (miRNAs) released from cancer cells are detectable in plasma in a remarkably stable form, making them ideal cancer biomarkers. Using plasma samples from FIT-positive (FIT+) subjects in an Italian CRC screening program, we aimed to identify plasma circulating miRNAs that detect early CRC. miRNAs were initially investigated by quantitative real-time PCR in plasma from 60 FIT+ subjects undergoing colonoscopy at Fondazione IRCCS Istituto Nazionale dei Tumori, then tested on an internal validation cohort (IVC, 201 cases) and finally in a large multicenter prospective series (external validation cohort [EVC], 1121 cases). For each endoscopic lesion (low-grade adenoma [LgA], high-grade adenoma [HgA], cancer lesion [CL]), specific signatures were identified in the IVC and confirmed on the EVC. A two-miRNA-based signature for CL and six-miRNA signatures for LgA and HgA were selected. In a multivariate analysis including sex and age at blood collection, the areas under the receiver operating characteristic curve (95% confidence interval) of the signatures were 0.644 (0.607–0.682), 0.670 (0.626–0.714) and 0.682 (0.580–0.785) for LgA, HgA and CL, respectively. A miRNA-based test could be introduced into the FIT+ workflow of CRC screening programs so as to schedule colonoscopies only for subjects likely to benefit most.  相似文献   
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Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.  相似文献   
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Epidermolysis bullosa simplex (EBS) is a skin fragility disorder resulting from mutations of structural proteins in the epidermis. We provide a brief report of long‐term survival and reproduction in a mother with EBS due to keratin 5 (KRT5) c.1429G > A (p.E477K) mutation, which causes a particularly severe form of the disease.  相似文献   
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