durable outcomes.As whole-genome data become available for increasing numbers of individuals across diverse populations, the list of genomic variants of unknown significance (VOUS) continues to grow. One powerful tool in VOUS interpretation is determining whether an allele is too common to be considered pathogenic. As genetic and epidemiological parameters vary across disease models, so too does the pathogenic allele frequency threshold for each disease gene. One threshold-setting approach is the maximum credible allele frequency (MCAF) method. However, estimating some of the input values MCAF requires, especially those involving heterogeneity, can present nontrivial statistical challenges. Here, we introduce FREQMAX, our alternative approach for determining allele frequency thresholds in carrier screening. FREQMAX makes efficient use of the data available for well-studied traits and exhibits flexibility for traits where information may be less complete. For cystic fibrosis, more alleles are excluded as benign by FREQMAX than by MCAF. For less-comprehensively characterized traits like ciliary dyskinesia and Smith-Lemli-Opitz syndrome, FREQMAX is able to set the allele frequency threshold without requiring a priori estimates of maximum genetic and allelic contributions. Furthermore, though we describe FREQMAX in the context of carrier screening, its classical population genetics framework also provides context for adaptation to other trait models. Aortic root and ascending aortic aneurysms are traditionally surgically treated through the deployment of a conduit with an artificial aortic valve, which significantly increases the risk of postoperative complications in the form of thrombosis. We report a case of Wolfe procedure in a 78-year-old female patient with aortic root aneurysm at high risk for conventional Bentall surgery. We use this case to discuss the effectiveness and short-term results of this procedure.We use this case to discuss the effectiveness and short-term results of this procedure. We aim to present our experience with the bidirectional Glenn (BDG) in patients less than 4 months of age and to compare their outcomes with the patients who underwent BDG after the age of 4 months. A retrospective review of data was performed for patients who underwent the BDG procedure from 2002 to 2018 at our institutions. We reviewed the patients' demographics, echocardiographic findings, cardiac catheterization data, operative details, postoperative data, and outcome variables. The study was conducted on 213 patients. At the time of the BDG operation, 32 patients were younger than 4 months (younger group) and 181 patients were older than 4 months (older group). The preoperative mean pulmonary artery pressure was significantly higher in the younger group (p = .035) but there were no significant differences between both groups in Qp/Qs, ventricular end-diastolic pressure, indexed pulmonary vascular resistance, and preoperative oxygen saturation. However, the initial postoperative oxygen saturation of the younger group was lower than the older group (p = .007). The duration of mechanical ventilation, duration of pleural drainage, ICU stay, and hospital stay after BDG were significantly longer in the younger group compared to the older group. The early mortality was higher in the younger group, but this difference did not reach statistical significance (p = .283). Performing BDG procedure in infants less than 4 months of age is safe, with favorable outcomes. https://www.selleckchem.com/products/sodium-ascorbate.html Early BDG is associated with a less-smooth postoperative course without a significant increase in early or late mortality.Performing BDG procedure in infants less than 4 months of age is safe, with favorable outcomes. Early BDG is associated with a less-smooth postoperative course without a significant increase in early or late mortality.The global pandemic of coronavirus 2019 (COVID-19) caused by coronavirus has had a profound impact on the delivery of health care in the United States and globally. Boston was among the earliest hit cities in the United States, and within Boston, the Massachusetts General Hospital provided care for more patients with COVID-19 than any other hospital in the region. This necessitated a massive reallocation of resources and priorities, with a near doubling of intensive care bed capacity and a halt in all deferrable surgical cases. During this crisis, the Division of Cardiac Surgery responded in a unified manner, dealing honestly with the necessity to reduce Intensive Care Unit resource utilization for the benefit of both the institution and our community by deferring nonemergent cases while also continuing to efficiently care for those patients in urgent or emergent need of surgery. Many of the interventions that we instituted have continued to support teamwork as we adapt to the remarkably fluid changes in resource availability during the recovery phase. We believe that the culture of our division and the structure of our practice facilitated our ability to contribute to the mission of our hospital to support the community in this crisis, and now to its recovery. We describe here the challenge we faced in Boston and some of the details of the structure and function of our division.Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation. More biological models of tumor dynamics can be obtained by using nonlinear models, but they are limited by the fact that parameter identifiability require rich dataset. In that context Bayesian approaches are particularly suited to incorporate the biological knowledge and increase the information available, but they are limited by the high computing cost of Monte-Carlo by Markov Chains algorithms. Here, we aimed to assess the performances of the Hamiltonian Monte-Carlo (HMC) algorithm implemented in Stan for inference in a nonlinear joint model. The method was validated on simulated data where HMC provided proper posterior distributions and credibility intervals in a reasonable computational time.