Schwartzmcmillan2108

High throughput imaging methods can be applied to relevant cell culture models, fostering their use in research and translational applications. Improvements in microscopy, computational capabilities and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images. Nonetheless, trade-offs in acquisition, computation and storage between content and throughput remain, in particular when cells and cell structures are imaged in 3D. Moreover, live 3D phase contrast microscopy images are not often amenable to analysis because of the high level of background noise. Cultures of Human induced pluripotent stem cells (hiPSC) offer unprecedented scope to profile and screen conditions affecting cell fate decisions, self-organisation and early embryonic development. However, quantifying changes in the morphology or function of cell structures derived from hiPSCs over time presents significant challenges. Here, we report a novel method based on the analysis of live phase contrast microscopy images of hiPSC spheroids. We compare self-renewing versus differentiating media conditions, which give rise to spheroids with distinct morphologies; round versus branched, respectively. These cell structures are segmented from 2D projections and analysed based on frame-to-frame variations. Importantly, a tailored convolutional neural network is trained and applied to predict culture conditions from time-frame images. We compare our results with more classic and involved endpoint 3D confocal microscopy and propose that such approaches can complement spheroid-based assays developed for the purpose of screening and profiling. This workflow can be realistically implemented in laboratories using imaging-based high-throughput methods for regenerative medicine and drug discovery.Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and labor-intensive. Therefore, researchers have instead been seeking to develop effective computational algorithms to predict associations between diseases and RNAs. In this paper, we propose a novel model called Graph Attention Adversarial Network (GAAN) for the potential disease-RNA association prediction. To our best knowledge, we are among the pioneers to integrate successfully both the state-of-the-art graph convolutional networks (GCNs) and attention mechanism in our model for the prediction of disease-RNA associations. Comparing to other disease-RNA association prediction methods, GAAN is novel in conducting the computations from the aspect of global structure of disease-RNA network with graph embedding while integrating features of local neighborhoods with the attention mechanism. Moreover, GAAN uses adversarial regularization to further discover feature representation distribution of the latent nodes in disease-RNA networks. GAAN also benefits from the efficiency of deep model for the computation of big associations networks. To evaluate the performance of GAAN, we conduct experiments on networks of diseases associating with two different RNAs MicroRNAs (miRNAs) and Long non-coding RNAs (lncRNAs). XMU-MP-1 nmr Comparisons of GAAN with several popular baseline methods on disease-RNA networks show that our novel model outperforms others by a wide margin in predicting potential disease-RNAs associations.Lamin A, a main constituent of the nuclear lamina, is the major splicing product of the LMNA gene, which also encodes lamin C, lamin A delta 10 and lamin C2. Involvement of lamin A in the ageing process became clear after the discovery that a group of progeroid syndromes, currently referred to as progeroid laminopathies, are caused by mutations in LMNA gene. Progeroid laminopathies include Hutchinson-Gilford Progeria, Mandibuloacral Dysplasia, Atypical Progeria and atypical-Werner syndrome, disabling and life-threatening diseases with accelerated ageing, bone resorption, lipodystrophy, skin abnormalities and cardiovascular disorders. Defects in lamin A post-translational maturation occur in progeroid syndromes and accumulated prelamin A affects ageing-related processes, such as mTOR signaling, epigenetic modifications, stress response, inflammation, microRNA activation and mechanosignaling. In this review, we briefly describe the role of these pathways in physiological ageing and go in deep into lamin A-dependent mechanisms that accelerate the ageing process. Finally, we propose that lamin A acts as a sensor of cell intrinsic and environmental stress through transient prelamin A accumulation, which triggers stress response mechanisms. Exacerbation of lamin A sensor activity due to stably elevated prelamin A levels contributes to the onset of a permanent stress response condition, which triggers accelerated ageing.Infertility affects approximately 186 million people worldwide and 8-12% of couples of reproductive age. Therefore, a comprehensive diagnostic evaluation of infertility is crucial to achieving improvements in targeted prevention and treatment outcomes. The aim of this review is to explore the biochemistry of infertility in order to properly diagnose and treat infertile couples. Recent studies indicate that routine measurement of biochemical parameters reflecting thyroid dysfunction, immunological disorders, autoimmune mechanisms, insulin resistance and malabsorption of selected micro- and macronutrients are required to assess infertility. Due to the complexity of this approach, algorithmic protocols that integrate these biochemical parameters in a dynamic test environment are necessary to provide a more comprehensive diagnostic assessment and more effective treatment strategy for infertile couples.The effect of antimicrobial stewardship (AS) on anaerobic bacteremia is uncertain. This study aimed to assess the effect of interventions by the AS team (AST) on clinical and microbiological outcomes and antimicrobial use. An AS program was introduced at Osaka City University Hospital in January 2014; an interdisciplinary AST was established. We enrolled patients with anaerobic bacteremia between January 2009 and December 2018. Patients were classified into the pre-intervention group (from January 2009 to December 2013) and the post-intervention group (from January 2014 to December 2018). A significant decrease in definitive carbapenem use (P = 0.0242) and an increase in empiric tazobactam/piperacillin use (P = 0.0262) were observed in the post-intervention group. The de-escalation rate increased significantly from 9.38% to 32.7% (P = 0.0316) in the post-intervention group. The susceptibility of Bacteroides species and 30-day mortality did not worsen in the post-intervention group. These results showed that interventions by an AST can reduce carbapenem use and increase the de-escalation rate without worsening patient outcomes.