Most consensus recommendations for the genetic diagnosis of neurodevelopmental disorders (NDDs) do not include the use of next generation sequencing (NGS) and are still based on chromosomal microarrays, such as comparative genomic hybridization array (aCGH). This study compares the diagnostic yield obtained by aCGH and clinical exome sequencing in NDD globally and its spectrum of disorders. To that end, 1412 patients clinically diagnosed with NDDs and studied with aCGH were classified into phenotype categories global developmental delay/intellectual disability (GDD/ID); autism spectrum disorder (ASD); and other NDDs. These categories were further subclassified based on the most frequent accompanying signs and symptoms into isolated forms, forms with epilepsy; forms with micro/macrocephaly and syndromic forms. Two hundred and forty-five patients of the 1412 were subjected to clinical exome sequencing. Diagnostic yield of aCGH and clinical exome sequencing, expressed as the number of solved cases, was compared for each phenotype category and subcategory. Clinical exome sequencing was superior than aCGH for all cases except for isolated ASD, with no additional cases solved by NGS. Globally, clinical exome sequencing solved 20% of cases (versus 5.7% by aCGH) and the diagnostic yield was highest for all forms of GDD/ID and lowest for Other NDDs (7.1% versus 1.4% by aCGH) and ASD (6.1% versus 3% by aCGH). In the majority of cases, diagnostic yield was higher in the phenotype subcategories than in the mother category. These results suggest that NGS could be used as a first-tier test in the diagnostic algorithm of all NDDs followed by aCGH when necessary.Ulcerative colitis and Crohn's disease are forms of inflammatory bowel disease whose incidence and prevalence are increasing worldwide. These diseases lead to chronic inflammation of the gastrointestinal tract as a result of an abnormal response of the immune system.  https://www.selleckchem.com/products/5-ethynyluridine.html  Recent studies positioned Cortistatin, which shows low stability in plasma, as a candidate for IBD treatment. Here, using NMR structural information, we design five Cortistatin analogues adopting selected native Cortistatin conformations in solution. One of them, A5, preserves the anti-inflammatory and immunomodulatory activities of Cortistatin in vitro and in mouse models of the disease. Additionally, A5 displays an increased half-life in serum and a unique receptor binding profile, thereby overcoming the limitations of the native Cortistatin as a therapeutic agent. This study provides an efficient approach to the rational design of Cortistatin analogues and opens up new possibilities for the treatment of patients that fail to respond to other therapies.Cells can encode information about their environment by modulating signaling dynamics and responding accordingly. Yet, the mechanisms cells use to decode these dynamics remain unknown when cells respond exclusively to transient signals. Here, we approach design principles underlying such decoding by rationally engineering a synthetic short-pulse decoder in budding yeast. A computational method for rapid prototyping, TopoDesign, allowed us to explore 4122 possible circuit architectures, design targeted experiments, and then rationally select a single circuit for implementation. This circuit demonstrates short-pulse decoding through incoherent feedforward and positive feedback. We predict incoherent feedforward to be essential for decoding transient signals, thereby complementing proposed design principles of temporal filtering, the ability to respond to sustained signals, but not to transient signals. More generally, we anticipate TopoDesign to help designing other synthetic circuits with non-intuitive dynamics, simply by assembling available biological components.Monoclonal antibodies (mAbs) and remdesivir, a small-molecule antiviral, are promising monotherapies for many viruses, including members of the genera Marburgvirus and Ebolavirus (family Filoviridae), and more recently, SARS-CoV-2. One of the major challenges of acute viral infections is the treatment of advanced disease. Thus, extending the window of therapeutic intervention is critical. Here, we explore the benefit of combination therapy with a mAb and remdesivir in a non-human primate model of Marburg virus (MARV) disease. While rhesus monkeys are protected against lethal infection when treatment with either a human mAb (MR186-YTE; 100%), or remdesivir (80%), is initiated 5 days post-inoculation (dpi) with MARV, no animals survive when either treatment is initiated alone beginning 6 dpi. However, by combining MR186-YTE with remdesivir beginning 6 dpi, significant protection (80%) is achieved, thereby extending the therapeutic window. These results suggest value in exploring combination therapy in patients presenting with advanced filovirus disease.Immunotherapy is regarded as the most promising treatment for cancers. Various cancer immunotherapies, including adoptive cellular immunotherapy, tumor vaccines, antibodies, immune checkpoint inhibitors, and small-molecule inhibitors, have achieved certain successes. In this review, we summarize the role of macrophages in current immunotherapies and the advantages of targeting macrophages. To better understand and make better use of this type of cell, their development and differentiation characteristics, categories, typical markers, and functions were collated at the beginning of the review. Therapeutic strategies based on or combined with macrophages have the potential to improve the treatment efficacy of cancer therapies.Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error less then 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p  less then  0.