Udsenhammond8322

On the contrary, the effects on PI3K-Akt and MAPK-ERK1/2 signaling pathways were marginal. To conclude, our data confirm the high T/N GIPR ratio in MTC tumors and suggest that it may represent an index for the degree of advancement of the malignant process. We have also observed a functional coupling between GIP/GIPR axis and calcitonin secretion in MTC models. However, the molecular mechanisms underlying this process and the possible implication of GIP/GIPR axis activation in MTC diagnosis and prognosis need further evaluation.The study investigated the existing guidelines on the quality and frequency of the follow-up visits after total hip replacement surgery and assessed the level of evidence of these recommendations. The review process was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Additional works were retrieved by direct investigation of the available guidelines of the most important orthopedic societies and regulatory agencies. The current systematic review of the literature resulted in zero original papers, four guidelines for routine follow-up and three guidelines for special cases. Concerning the quality of evidence behind them, these guidelines were not evidence based but drafted from expert consensus. The most important finding of this review is the large variation of recommendations in the follow-up schedule after total hip arthroplasty and the lack of evidence-based indications. MK-5108 inhibitor Indeed, all the above-reported guidelines are the result of a consensus among experts in the field (level of recommendation class D 'very low') and not based on clinical studies.

Health care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients.

The aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery.

The study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires.

A total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions througengagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time.

ClinicalTrials.gov NCT03532256; https//clinicaltrials.gov/ct2/show/NCT03532256.

ClinicalTrials.gov NCT03532256; https//clinicaltrials.gov/ct2/show/NCT03532256.[This corrects the article DOI 10.2196/31972.].

The qualification and order of authorship in scientific manuscripts are the main disputes in collaborative research work.

The aim of this project was to develop an open-access web-based platform for objective decision-making of authorship qualification and order in medical and science journals.

The design science process methodology was used to develop suitable software for authorship qualification and order. The first part of the software was designed to differentiate between qualification for authorship versus acknowledgment, using items of the recommendations of the International Committee of Medical Journal Editors. The second part addressed the order of authorship, using the analytical hierarchy process for objective multiple criteria decision-making and ranking. The platform was evaluated qualitatively (n=30) and quantitatively (n=18) using a dedicated questionnaire, by an international panel of medical and biomedical professionals and research collaborators worldwide.

Authorships.org representsrg allows transparent evaluation of authorship qualification and order in academic medical and science journals. Objectified proof of authorship contributions may become mandatory during manuscript submission in high-quality academic journals.Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space. For each sample, we generate heterogeneous representations and use view-consistency loss to make their representations consistent with each other. Multiview representation also inspires to supervise the pseudolabels generation by the aid of mutual supervision between views. In this article, we thus propose a view-consistent heterogeneous network (VCHN) to learn better representations by aligning view-agnostic semantics. Specifically, VCHN is constructed by constraining the predictions between two views so that the view pairs can supervise each other. To make the best use of cross-view information, we further propose a novel training strategy to generate more reliable pseudolabels, which thus enhances predictions of the VCHN. Extensive experimental results on three benchmark datasets demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.This article is concerned with the problem of dissipativity for discrete-time singular systems with time-varying delays. First, the discrete-state decomposition technique is proposed after performing the restricted equivalent transformation for singular systems. To reduce the use of decision variables, the state-decomposed Lyapunov function is established based on the decomposed state vectors. Second, to obtain the condition with less conservatism, the two zero-value equations, especially concerning difference subsystems and algebraic ones, the discrete Wirtinger-based inequality and the extended reciprocally convex inequality are employed to bound the forward difference of the Lyapunov function. Then, the less conservative dissipativity criteria with lower computational complexity are obtained. Finally, simulation results are provided to demonstrate the superiority of the proposed technique.Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, themost populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government's lockdown policies and people's mask-wearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available.We study the asymptotical consensus problem for multi-agent systems (MASs) consisting of a high-dimensional leader and multiple followers with unknown nonlinear dynamics under directed switching topology by using a neural network (NN) adaptive control approach. First, we design an observer for each follower to reconstruct the states of the leader. Second, by using the idea of discontinuous control, we design a discontinuous consensus controller together with an NN adaptive law. Finally, by using the average dwell time (ADT) method and the Barbǎlat's lemma, we show that asymptotical neuroadaptive consensus can be achieved in the considered MAS if the ADT is larger than a positive threshold. Moreover, we study the asymptotical neuroadaptive consensus problem for MASs with intermittent topology. Finally, we perform two simulation examples to validate the obtained theoretical results. In contrast to the existing works, the asymptotical neuroadaptive consensus problem for MASs is firstly solved under directed switching topology.In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains.