Myrickhanley4049

Pubertal delay can be the clinical presentation of both idiopathic hypogonadotropic hypogonadism (IHH) and self-limited delayed puberty (SLDP). Distinction between these conditions is a common but important diagnostic challenge in adolescents.

To assess whether gene panel testing can assist with clinical differential diagnosis, to allow accurate and timely management of delayed puberty patients.

Retrospective study Methods Patients presenting with delayed puberty to UK Paediatric services, followed up to final diagnosis, were included. Whole-exome sequencing was analysed using a virtual panel of genes previously reported to cause either IHH or SLDP to identify rare, predicted deleterious variants. Deleterious variants were verified by in silico prediction tools. The correlation between clinical and genotype diagnosis was analysed.

Forty-six patients were included, 54% with a final clinical diagnosis of SLDP and 46% with IHH. Red flags signs of IHH were present in only 3 patients. Fifteen predicted deleterious variants in 12 genes were identified in 33% of the cohort, with most inherited in a heterozygous manner. A fair correlation between final clinical diagnosis and genotypic diagnosis was found. Panel testing was able to confirm a diagnosis of IHH in patients with pubertal delay. Genetic analysis identified three patients with IHH that had been previously diagnosed as SLDP.

This study supports the use of targeted exome sequencing in the clinical setting to aid the differential diagnosis between IHH and SLDP in adolescents presenting with pubertal delay. Genetic evaluation thus facilitates earlier and more precise diagnosis, allowing clinicians to direct treatment appropriately.

This study supports the use of targeted exome sequencing in the clinical setting to aid the differential diagnosis between IHH and SLDP in adolescents presenting with pubertal delay. Genetic evaluation thus facilitates earlier and more precise diagnosis, allowing clinicians to direct treatment appropriately.

Polycystic ovary syndrome is diagnosed based on clinical signs, but its presentation is heterogeneous and potentially confounded by concurrent conditions, as obesity and insulin-resistance. MicroRNAs have recently emerged as putative pathophysiological and diagnostic factors in PCOS. However, no reliable miRNA-based method for molecular diagnosis of PCOS has been reported. The aim of this study was to develop a tool for accurate diagnosis of PCOS by targeted miRNA profiling of plasma samples, defined on the basis of unbiased biomarker-finding analyses and biostatistical-tools.

A case-control PCOS cohort was cross-sectionally studied, including 170 women classified into four groups non-PCOS/lean; non-PCOS/obese; PCOS/lean; and PCOS/obese women. High-throughput miRNA analyses were performed in plasma, using NanoString technology and a 800-human-miRNA panel, followed by targeted-qPCR validation. Statistics were applied to define optimal normalization methods, identify deregulated biomarker miRNAs and build cthod allows not only reliable diagnosis of non-obese women with PCOS, but also discrimination between PCOS and obesity.

The diagnosis of growth hormone deficiency (GHD) in children is not always straightforward because IGF-1 or GH stimulation tests may not be able to discriminate GHD from constitutional delay of growth and puberty (CDGP) or other causes of short stature.

Boys and girls, n=429, (0.7 - 16 years old) that attended our department for short stature, participated in this study. They were followed up for an average period of 9 years (4-15). At the end of follow up, a definitive diagnosis was assigned to each individual, and all the components of ternary complex (IGF-1, IGFBP-3, ALS and IGF-1/IGFBP-3 ratio) were evaluated as biomarkers for the respective diagnosis.

All components of ternary complex were tightly correlated with each other and positively related to age. IGF-1, IGFBP-3, ALS, and IGF-1/IGFBP-3 ratio differed significantly between GHD and normal groups. IGF-1 and ALS levels were lower in GHD compared to children with familial short stature, while IGF-1 and IGF-1/IGFBP-3 ratio was significantly lower in GHD compared to children with CDGP. IGF-1 and IGF-1/IGFBP-3 Receiver Operating Curves (ROC) cutoff points were unable to discriminate between GHD and normal or between GHD and CDGP groups.

Despite the tight correlation among all components of the ternary complex, each one shows a statistically significant diagnosis-dependent alteration. There is a superiority of IGF-1, ALS and IGF-1/IGFBP-3 ratio in the distinction between GHD and CDGP or GHD and normal groups but without usable discriminating power, making thus auxology the primary criterion of establishing the diagnosis.

Despite the tight correlation among all components of the ternary complex, each one shows a statistically significant diagnosis-dependent alteration. There is a superiority of IGF-1, ALS and IGF-1/IGFBP-3 ratio in the distinction between GHD and CDGP or GHD and normal groups but without usable discriminating power, making thus auxology the primary criterion of establishing the diagnosis.

Patient portals have been introduced in many countries over the last ten years, but many health information managers still feel they have too little knowledge of patient portals. A taxonomy can help them to better compare and select portals. This has led us to develop the TOPCOP taxonomy for classifying and comparing patient portals. However, the taxonomy has not been evaluated by users.

To evaluate the taxonomy's usefulness to support health information managers in comparing, classifying, defining a requirement profile for, and selecting patient portals, and to improve the taxonomy where needed.

We used a modified Delphi approach. We sampled a heterogeneous panel of thirteen health information managers from three countries using the criterion sampling strategy. Four anonymous survey rounds with qualitative and quantitative questions were conducted online. In round one, the panelists assessed the appropriateness of each dimension and we collected new ideas to improve the dimensions. In rounds two and thng portals, creating a requirement profile, and selecting portals. This allowed us to test the usefulness of the final taxonomy with the intended users.

The TOPCOP taxonomy aims to support health information managers in comparing and selecting patient portals. By providing a standardized terminology to describe various aspects of patient portals independent of clinical setting or country, the taxonomy will also be useful for advancing research and evaluation of patient portals.

[This corrects the article DOI 10.2196/21929.].This article proposes robust inverse Q-learning algorithms for a learner to mimic an expert's states and control inputs in the imitation learning problem. These two agents have different adversarial disturbances. To do the imitation, the learner must reconstruct the unknown expert cost function. The learner only observes the expert's control inputs and uses inverse Q-learning algorithms to reconstruct the unknown expert cost function. The inverse Q-learning algorithms are robust in that they are independent of the system model and allow for the different cost function parameters and disturbances between two agents. We first propose an offline inverse Q-learning algorithm which consists of two iterative learning loops 1) an inner Q-learning iteration loop and 2) an outer iteration loop based on inverse optimal control. Then, based on this offline algorithm, we further develop an online inverse Q-learning algorithm such that the learner mimics the expert behaviors online with the real-time observation of the expert control inputs. This online computational method has four functional approximators a critic approximator, two actor approximators, and a state-reward neural network (NN). It simultaneously approximates the parameters of Q-function and the learner state reward online. Convergence and stability proofs are rigorously studied to guarantee the algorithm performance.The recommender system is a popular research topic in the past decades, and various models have been proposed. Among them, collaborative filtering (CF) is one of the most effective approaches. The underlying philosophy of CF is to capture and utilize two types of relationships among users/items, that is, the user-item preferences and the similarities among users/items, to make recommendations. In recent years, graph neural networks (GNNs) have gained popularity in many research fields, and in the recommendation field, GNN-based CF models have also been proposed, which are shown to have impressive performance. However, in our research, we observe a crucial drawback of these models, that is, while they can explicitly model and utilize the user-item preferences, the other necessary type of relationship, that is, the similarities among users/items, can only be implied and then utilized, which seems to hinder the performance of these models. Motivated by this, in this article, we first propose a novel dual-message propagation mechanism (DPM). The DPM can explicitly model and utilize both preferences and similarities to make recommendations; thus, it seems to be a better realization of CF's philosophy. Then, a dual-message graph CF (DGCF) model is proposed. Different from the existing models, in the DGCF, each user's/item's embedding is processed by two GNNs, with one handling the preferences and the other handling the similarities. Extensive experiments conducted on three real-world datasets demonstrate that DGCF substantially outperforms state-of-the-art CF models, and the small amount of sacrifice of time efficiency is tolerable considering the substantial improvement of model performance.This article presents a structure constraint matrix factorization framework for different behavior segmentation of the human behavior sequential data. This framework is based on the structural information of the behavior continuity and the high similarity between neighboring frames. Due to the high similarity and high dimensionality of human behavior data, the high-precision segmentation of human behavior is hard to achieve from the perspective of application and academia. check details By making the behavior continuity hypothesis, first, the effective constraint regular terms are constructed. Subsequently, the clustering framework based on constrained non-negative matrix factorization is established. Finally, the segmentation result can be obtained by using the spectral clustering and graph segmentation algorithm. For illustration, the proposed framework is applied to the Weiz dataset, Keck dataset, mo_86 dataset, and mo_86_9 dataset. Empirical experiments on several public human behavior datasets demonstrate that the structure constraint matrix factorization framework can automatically segment human behavior sequences. Compared to the classical algorithm, the proposed framework can ensure consistent segmentation of sequential points within behavior actions and provide better performance in accuracy.Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model suffers from two major limitations 1) it linearly combines the prototype and variation images in the observational pixel-spatial space and cannot generalize to multiple nonlinear variations, e.g., poses, which are common in face images and 2) it would be severely impaired once the enrolment face images are contaminated by nuisance variations. To address the two limitations, it is desirable to disentangle the prototype and variation in a latent feature space and to manipulate the images in a semantic manner. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, which consists of an encoder-decoder generator and two discriminators.