Lassiterulrich0529

There is a paucity of studies on post-acute COVID-19 syndrome (PCS) among hospitalized COVID-19 survivors from Nigeria. https://www.selleckchem.com/products/lificiguat-yc-1.html We describe the frequency, types, and duration of post-discharge symptoms suggestive of PCS among previously hospitalized COVID-19 patients in a treatment center in Nigeria. We conducted a retrospective review of admission and post-discharge follow-up medical records of COVID-19 survivors admitted between April and December 2020. A standardized checklist was used to document post-discharge symptoms. PCS was defined as persisting or new post-discharge symptoms lasting at least 3 weeks after initial COVID-19 symptoms. The relationship between study variables and development of PCS was ascertained by univariate analysis. Thirty of 51 previously hospitalized COVID-19 patients (median age, 46 years; male, 66.7%) were studied. Seventeen (56.7%) of the 30 patients developed features suggestive of PCS. Approximately three post-discharge symptoms were reported per patient over a follow-up period of ranging from 3 weeks to 9 months after initial COVID-19 symptoms. Cough, fatigue, and dyspnea were the most common post-discharge symptoms reported. A few patients had symptoms suggestive of thrombosis and COVID-19 reinfection. Among all study variables, baseline COVID-19 severity was the only significant variable associated with the development of PCS. PCS is common in our setting and is characterized by multisystemic signs and symptoms that require vigilance by clinicians for appropriate diagnosis and treatment. Long-term multicenter prospective studies are needed to characterize fully the burden of PCS among COVID-19 survivors in Nigeria.Hypothyroidism is a common endocrine disease caused by a deficiency of thyroid hormones, which could affect the hypothalamus-pituitary-gonadal (HPG) axis and cause additional severe fertility problems. However, the pathogenesis of abnormal reproductive capacity caused by hypothyroidism and whether there are differences between females and males need more study. Here, we constructed a prolonged neonatal hypothyroid rat model using 6-propyl-2-thiouracil (PTU). H&E staining and RNA-sequencing were performed to detect histopathological and transcriptome changes. Our results indicated that the numbers of ventromedial hypothalamus nuclei were increased, and the number of pituitary chromophobes was sharply increased, whereas the proportion of pituitary acidophils and pituitary basophils were obviously reduced. The differentially expressed genes of the HPG axis organs were identified, and different tissues shared similar steroid hormone and oxidative stress-related terms in gene ontology analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis indicated oxidative stress, and apoptosis-related genes were more enriched in male hypothyroid pituitaries, whereas the serum levels of growth hormone, follicle-stimulating hormone, and luteinizing hormone that were detected by ELISA were also reduced more in male hypothyroid rats, suggesting that prolonged neonatal hypothyroidism may have a more significant impact on male pituitaries. Moreover, the multi-organ oxidative stress in hypothyroid rats was confirmed by the higher expression of oxidative stress-related genes, such as the Txnip. The increased level of oxidative stress may have contributed to the histopathological and transcriptome changes of HPG axis organs in the prolonged neonatal hypothyroidism rats, especially in male pituitaries.Orphan nuclear receptors (ONRs) are a subset of the nuclear receptor family that lacks known endogenous ligands. Among 48 nuclear receptors identified in humans, 25 are classified as ONRs. They function as transcription factors and control the expression of a wide range of genes to regulate metabolism, fertility, immunity, angiogenesis, and many other functions. Angiogenic factors are essential during ovarian follicle development, including follicle growth and ovulation. The correct development of blood vessels contributes to preantral and antral follicular development, selection of the dominant follicle or follicles, follicular atresia, and ovulation. Although progress has been made in understanding the molecular mechanisms that regulate follicular angiogenesis, the role of ONRs as regulators is not clear. Based on their functions in other tissues, the ONRs NR1D1 (REV-ERBβ), NR2C2 (TR4), NR2F2 (COUP-TF-II) and NR3B1, 2, and 3 (ERRα, ERRβ and ERRγ) may modulate angiogenesis during antral follicle development. We hypothesize that this is achieved by effects on the expression and function of VEGFA, ANGPT1, THBS1, and soluble VEGFR1. Further, angiogenesis during ovulation is expected to be influenced by ONRs. NR5A2 (LRH-1), which is required for ovulation, regulates angiogenic genes in the ovary, including VEGFA and the upstream regulator of angiogenesis, PGE2. These angiogenic molecules may also be regulated by NR5A1 (SF-1). Evidence from outside the reproductive tract suggests that NR2F2 and NR4A1(NUR77) promote VEGFC and PGF, respectively, and NR4As (NUR77, NOR1) seem to be necessary for the angiogenic effects of VEGFA and PGE2. Together, the data suggest that ONRs are important regulators of follicular angiogenesis.

In the context of a viral outbreak and the stay-at-home measures, a significant increase in psychological distress, such as stress or fear behaviours, has previously been reported in adult and paediatric population. Children and adolescents seem to be particularly at risk of developing psychiatric disorders during and after the stay-at-home but evidences are lacking. The main objective of this article is to present the methodology of Coronavirus Confinement 2020 (CoCo20) Study, which aims to assess the impact of the coronavirus pandemic (COVID-19) and stay-at-home on the development of psychiatric disorders, including post-traumatic stress disorder (PTSD), in children and adolescents.

We describe a longitudinal and multicentre study in the paediatric population during and after stay-at-home related to COVID-19 pandemic. Inclusions started on 30 March 2020 for 6 months. This study is proposed to all consecutive paediatric outpatients consulting during and after stay-at-home related to COVID-19 pandemic in lts of the study to relevant journals and offer national and international presentations. This study will enable better characterisation of the impact of the stay-at-home (related to COVID-19 pandemic) on the mental health of children and adolescents.

NCT04498416.

NCT04498416.

The COVID-19 pandemic has largely affected people's mental health and psychological well-being. Specifically, individuals with a pre-existing mental health disorder seem more impaired by lockdown measures posing as major stress factors. Medical rehabilitation treatment can help people cope with these stressors. The internet and digital apps provide a platform to contribute to regular treatment and to conduct research on this topic.

Making use of internet-based assessments, this study investigated individuals from the general population and patients from medical, psychosomatic rehabilitation clinics. Levels of depression, anxiety, loneliness, and perceived stress during the COVID-19 pandemic, common COVID-19-related worries, and the intention to use digital apps were compared. Furthermore, we investigated whether participating in internet-delivered digital trainings prior to and during patients' rehabilitation stay, as well as the perceived usefulness of digital trainings, were associated with improved menand anxiety (F

=6.22, P=.01, η



=0.01) from pre- to postrehabilitation.

This study validated the increased mental health constraints of psychosomatic rehabilitation patients in comparison to the general population and the effects of rehabilitation treatment. Digital rehabilitation components are promising tools that could prepare patients for their rehabilitation stay, could integrate well with face-to-face therapy during rehabilitation treatment, and could support aftercare.

ClinicalTrials.gov NCT04453475; https//clinicaltrials.gov/ct2/show/NCT04453475 and ClinicalTrials.gov NCT03855735; https//clinicaltrials.gov/ct2/show/NCT03855735.

ClinicalTrials.gov NCT04453475; https//clinicaltrials.gov/ct2/show/NCT04453475 and ClinicalTrials.gov NCT03855735; https//clinicaltrials.gov/ct2/show/NCT03855735.[This corrects the article DOI 10.2196/mhealth.7208.].[This corrects the article DOI 10.2196/29583.].Due to the multilayer nature of real-world systems, the problem of inferring multilayer network structures from nonlinear and complex dynamical systems is prominent in many fields, including engineering, biological, physical, and computer sciences. Many network reconstruction methods have been proposed to address this problem, but none of them consider the similarities among network reconstruction tasks at different component layers, which are inspired by topology correlations and dynamic couplings among different component layers. This article develops an evolutionary multitasking multilayer network reconstruction framework to make use of the correlations among different component layers to improve the reconstruction performance; we refer to this framework as EM2MNR. In EM2MNR, the multilayer network reconstruction problem is first established as a multitasking multilayer network reconstruction problem, where the goal of each task is to reconstruct the network structure of a component layer. In addition, multitasking multilayer network reconstruction problems are high dimensional, but existing evolutionary multitasking algorithms may have poor performance when dealing with optimization problems with a high-dimensional search space. Inspired by the sparsity of multilayer networks, EM2MNR employs the restricted Boltzmann machine to extract low effective features from the original decision space and then decides whether to conduct knowledge transfer on these features. To verify the performance of EM2MNR, this article also designs a test suite for multilayer network reconstruction problems. The experimental results demonstrate the significant improvement obtained by the proposed EM2MNR framework on 96 multilayer network reconstruction problems.Learning from complementary labels (CLs) is a useful learning paradigm, where the CL specifies the classes that the instance does not belong to, instead of providing the ground truth as in the ordinary supervised learning scenario. In general, although it is less laborious and more efficient to collect CLs compared with ordinary labels, the less informative signal in the complementary supervision is less helpful to learn competent feature representation. Consequently, the final classifier's performance greatly deteriorates. In this article, we leverage generative adversarial networks (GANs) to derive an algorithm GAN-CL to effectively learn from CLs. In addition to the role in original GAN, the discriminator also serves as a normal classifier in GAN-CL, with the objective constructed partly with the complementary information. To further prove the effectiveness of our schema, we study the global optimality of both generator and discriminator for the GAN-CL under mild assumptions. We conduct extensive experiments on benchmark image datasets using deep models, to demonstrate the compelling improvements, compared with state-of-the-art CL learning approaches.