Familial adenomatous polyposis (FAP), an autosomal dominant disease, is a colon cancer predisposition syndrome that manifests as a large number of adenomatous polyps. Mutations in the Adenomatous polyposis coli (APC) gene are responsible for the majority of cases of FAP. The purpose of the present study was to report the clinical features of a Chinese family with FAP and screen for novel mutations using the targeted next-generation sequencing technology. Among the 29 family members, 12 were diagnosed of FAP. Based on an established filtering strategy and data analyses, along with confirmation by Sanger sequencing and co-segregation, a novel frameshift mutation c.1317delA (p.Ala440LeufsTer14) in exon 10 of the APC gene was identified. To the best of our knowledge, this mutation has not been reported prior to the present study. In addition, it was correlated with extra-colonic phenotypes featuring duodenal polyposis and sebaceous cysts in this family. This novel frameshift mutation causing FAP not only expands the germline mutation spectrum of the APC gene in the Chinese population, but it also increases the understanding of the phenotypic and genotypic correlations of FAP, and may potentially lead to improved genetic counseling and specific treatment for families with FAP in the future.
In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher's linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.
Gliomas, often known as low-grade gliomas, are malignant brain tumors. Codeletion of chromosomal arms 1p/19q has been connected with a good response to treatment in low-grade gliomas (LGG) in several studies. For treatment planning, the ability to anticipate 1p19q status is crucial. This research’s purpose is to develop a noninvasive approach based on MR images using our efficient CNNs. While public networks like VGGNet, GoogleNet, and other well-known public networks can use transfer learning to identify brain cancer on MRI, the model contains a large number of components that are unrelated to brain tumors. We build a model from the bottom-up, rather than relying on transfer learning. Our network structure flexibly uses a deep convolution stack mixed with dropout and dense operation, which reduces overfitting and enhances performance. We increase the number of samples by augmenting the dataset. The Gaussian noise is introduced during the model training. To address the issue of data imbalance, we use stratified
k
-fold cross-validation during training to find the best model. Our proposed model is compared with models fine-tuned through transfer learning, such as MobileNetV2, InceptionResNetV2, and VGG16. Our model achieves better results than these models on the same small dataset. In the test set, when deciding whether or not an image should be 1p/19q codeleted, the proposed architecture achieved an F1-score of 96.50%, precision of 96.50%, recall of 96.49%, and accuracy of 96.50%. By comparing with the transfer model, we found that transfer learning does not outperform CNN on a small dataset.
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