Thomsengoldberg8218

Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer. Graphical abstract A DCNN model was generated to accurately predict survival rates of brain cancer patients (classified in 4 different classes) accurately. After training the model using images from H&E stained tissue biopsies from The Cancer Genome Atlas database (TCGA, left), the model can predict for each patient, based on a histological image (top right), its survival class accurately (bottom right).A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier.The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support. Graphical abstract.BACKGROUND Obesity is a risk factor for vitamin D deficiency and hyperparathyroidism. Hyperparathyroidism could exert a negative effect on glucose metabolism and vascular function. The aim of this study was to identify the determinants of hyperparathyroidism beyond vitamin D deficiency, whether hyperparathyroidism could have a negative impact on individual health and whether laparoscopic sleeve gastrectomy (LSG) negatively affects the levels of intact parathyroid hormone (iPTH) and 25(OH) vitamin D (25(OH)D). METHODS We evaluated the levels of iPTH, 25(OH)D, and leptin, together with markers of insulin sensitivity and early cardiovascular disease, in a cohort of 160 patients with severe obesity before and after an LSG intervention. RESULTS Ninety-seven percent of subjects had vitamin D deficiency, and 72% of them had hyperparathyroidism. After correcting for possible confounders, we found a correlation between iPTH levels and carotid intima-media thickness, as well as with the HOMA index. After the LSG, 25(OH)D levels were significantly increased, while iPTH levels were significantly reduced. The reduction of iPTH was significantly correlated with the reduction of BMI, diastolic blood pressure, and leptin, which was the independent predictor of iPTH reduction. CONCLUSIONS Our results suggest that vitamin D deficiency is not the sole determinant of hyperparathyroidism in severe obesity because visceral fat deposition and leptin could both play a role. Obesity-related hyperparathyroidism is associated with insulin resistance and atherosclerosis, although the results from previous studies were conflicting. Finally, LSG intervention does not negatively affect vitamin D status and improves hyperparathyroidism.INTRODUCTION Transient tissue elastography (TTE) may estimate the degree of hepatic fibrosis in patients with obesity, but the method has restrictions that are mainly related to patients' BMI. PURPOSE To compare the results of the evaluation of hepatic fibrosis by biochemical methods and TTE with those determined by liver biopsy in patients after RYGB. Gossypol clinical trial METHODS This was a cross-sectional study involving patient data, TTE, and liver biopsy 1 year after RYGB. RESULTS Of the 94 selected patients, 33 underwent TTE and liver biopsy. The average weight of patients was 84.4 ± 15.4 kg. The mean APRI was 0.2 ± 0.1, and 36 patients (97.3%) were classified as F0-F1. The average NFS was - 2.0 ± 1.0, with 25 patients (67%) classified as F0-F1 and 12 patients (32.4%) classified as F2. The agreement rate between Fibroscan and liver biopsy was 80.0%. Histological analysis revealed regression of inflammatory changes in all patients 26 patients (72.2%) had some degree of non-alcoholic steatohepatitis (NAS ≥ 5), and after surgery, no patient presented inflammation upon biopsy.