Garrettegholm3129

A cut-off cortisol value of <18 mg/dL after 60 min of ACTH injection was used to diagnose AI.

One hundred forty-one patients were included and underwent an APST. APST suggested 20/141 (14.2%) had undiagnosed AI. The commonest cause of AI (9/20) was secondary AI because of the use of steroids including inhaled steroids and indigenous medicines contaminated with steroids. In 5 (3.5%) patients hypopituitarism was newly diagnosed. Despite primary AI (PAI) not commonly presenting as EuVHNa, 2/20 patients had PAI.

AI is much commoner in our country, among in-patients with EuVHNa primarily driven by exogenous steroid use and undiagnosed hypopituitarism.

AI is much commoner in our country, among in-patients with EuVHNa primarily driven by exogenous steroid use and undiagnosed hypopituitarism.The cytochrome P450 family 17 (CYP17) is associated with hyperandrogenism in women, and the association between CYP17 gene polymorphism and the risk of polycystic ovary syndrome (PCOS) is not definitive. In order to determine whether the CYP17 T/C (rs74357) gene polymorphism is an exposure risk for PCOS, a comprehensive meta-analysis summarizing 19 studies was performed. The pooled odds ratio (OR) and the corresponding 95% CI were measured under five genetic models, and the stratified analyses by ethnicity, Hardy-Weinberg equilibrium, testosterone levels and BMI in controls were carried out to identify the causes of substantial heterogeneity. The overall results validated that the CYP17 T/C (rs74357) gene polymorphism was significantly associated with PCOS risk in four genetic models. Moreover, the outcomes of subgroup analysis by ethnicity indicated that the frequencies of the C allele of CYP17 T/C (rs74357) polymorphism were markedly higher in women from Asia than in Caucasians (T vs C OR 0.85, 95% CI = 0.74-0.99, P less then 0.05). Therefore, these findings suggested that the CYP17 T/C (rs74357) gene polymorphism played an indispensable part in increasing the susceptibility of PCOS when carrying the C allele, which proposed that the polymorphism of the CYP17 gene may be a predictive factor for the risk of PCOS or an important pathway in PCOS-associated metabolic and hormonal dysregulation.With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes in WSN are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. selleck chemicals llc Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharing and privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.This article studies finite-time stabilization of delayed neural networks (DNNs) whose activation functions are discontinuous. Several sufficient conditions for guaranteeing finite-time stabilization of considered DNNs are obtained by constructing appropriate controllers with giving upper bounds of control time. Subsequently, based on the existing definition of energy consumption, the required energy to achieve stabilization is estimated. To quantify the cost of control, an evaluation index function is constructed to analyze the tradeoff between control time and consumed energy. Ultimately, acquired results are verified by simulating two numerical examples.In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation of l0 matrix norm. The binary constraints of the problem are then equivalently replaced with bilinear constraints to convert the problem to a biconvex problem. The reformulated biconvex problem is finally solved by using a two-timescale duplex neurodynamic approach consisting of two recurrent neural networks (RNNs) operating collaboratively at two timescales. A Gaussian score (GS) is defined as to integrate the bicriteria of factorization errors and sparsity of resulting matrices. link2 The performance of the proposed neurodynamic approach is substantiated in terms of low factorization errors, high sparsity, and high GS on four benchmark datasets.With the rise of artificial intelligence, deep learning has become the main research method of pedestrian recognition re-identification (re-id). However, most of the existing researches usually just determine the retrieval order based on the geographical location of cameras, which ignore the spatio-temporal logic characteristics of pedestrian flow. Furthermore, most of these methods rely on common object detection to detect and match pedestrians directly, which will separate the logical connection between videos from different cameras. In this research, a novel pedestrian re-identification model assisted by logical topological inference is proposed, which includes 1) a joint optimization mechanism of pedestrian re-identification and multicamera logical topology inference, which makes the multicamera logical topology provides the retrieval order and the confidence for re-identification. And meanwhile, the results of pedestrian re-identification as a feedback modify logical topological inference; 2) a dynamic spatio-temporal information driving logical topology inference method via conditional probability graph convolution network (CPGCN) with random forest-based transition activation mechanism (RF-TAM) is proposed, which focuses on the pedestrian's walking direction at different moments; and 3) a pedestrian group cluster graph convolution network (GC-GCN) is designed to measure the correlation between embedded pedestrian features. Some experimental analyses and real scene experiments on datasets CUHK-SYSU, PRW, SLP, and UJS-reID indicate that the designed model can achieve a better logical topology inference with an accuracy of 87.3% and achieve the top-1 accuracy of 77.4% and the mAP accuracy of 74.3% for pedestrian re-identification.Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. link3 When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https//github.com/CuthbertCai/SRDA.Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins are developing rapidly, they suffer from various limitations including unsatisfactory data suitability and low accuracy of predictive results. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on Random Walk and Adaptive Multi-View multi-label Learning. In RWAMVL, taking into account that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks would be obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method would be designed to detect essential proteins based on these different features. Finally, in order to accurately verify the predictive performance of RWAMVL, intensive experiments would be done to compare RWAMVL with multiple state-of-the-art predictive methods under different expeditionary frameworks, and comparative results illustrated that RWAMVL could achieve high prediction accuracy than all these competitive methods as a whole, which demonstrated that RWAMVL may be a potential tool for prediction of key proteins in the future.Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on nine real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.