Objectives Although a theoretical link between childhood adversity and mental states recognition has been established, empirical findings are mixed. Some prior work indicates that childhood adversity might enhance, preserve, or reduce mentalization skills in selected at-risk populations. In the current study, we examine whether the presence of risky alcohol use during adolescence moderates the association between childhood alcohol-related family adversity and mental states recognition in young adulthood. Methods Secondary data analysis was conducted on 266 young adults who participated in the Michigan Longitudinal Study-a multiwave prospective study on at-risk youth. Children were assessed after initial recruitment (wave 1, target child age range 3-5 years), with assessments repeated every 3 years using parallel measures. The current study focuses on data spanning wave 2 (age range 7-9 years) through wave 6 (target child age range 18-21 years). A family adversity index was derived reflecting exposure to a malf alcohol risk. Conclusions Findings indicate that history of childhood adversity may actually improve young adult negative and neutral mental states recognition among those demonstrating high levels of risky alcohol use, as substance use may serve as an external self-regulatory tool. Clinical interventions that target enhancing metacognitive competence and emotion regulation could ultimately help to break the cycle of alcohol-related family adversity.Dialysis patients are more vulnerable and susceptible to the severe coronavirus disease 2019 (COVID-19) infection due to multiple comorbidities. Since Taiwan has the highest incidence and prevalence of treated end-stage kidney disease worldwide, it is crucial to act in advance to prevent a potential disaster. In the face of the COVID-19 pandemic, we implement proactive infection control measures to prevent it from spreading without sacrificing the dialysis care quality. In this article, we focused on hemodialysis vascular access (HVA) care in particular. As a life-line of hemodialysis (HD) patients, HVA care has a profound impact on the patient's quality of dialysis and life.  https://www.selleckchem.com/products/mavoglurant.html  Specifically, in our facility, the working and office areas of the HD units are separated to reduce cross-infection. All elective procedures for HVA are postponed, and operating rooms equipped with a negative-pressure anteroom are used for the suspected or confirmed COVID-19 patients. Herein, we share how we modified our HVA care policy not only to prevent our patients from COVID-19 infection but also to maintain the quality of HVA care.Background Optical coherence tomography (OCT) is considered as a sensitive and non-invasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti-vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians. Methods We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images. Results Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models were 96.48% and 95.15%. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models. Conclusion We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.Purpose of review The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables. Recent findings In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation. Summary Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.Introduction The current coronavirus disease (COVID-19) pandemic led to a significant disruption in the care of pain from chronic and subacute conditions. The impact of this cessation of pain treatment may have unintended consequences of increased pain, reduced function, increased reliance on opioid medications and potential increased morbidity, due to the systemic impact of untreated disease burden. This may include decreased mobility, reduction in overall health status and increase of opioid use with the associated risks. Methods The article is the work of the American Society of Pain and Neuroscience (ASPN) COVID-19 Task Force to evaluate the policies set forth by federal, state, and local agencies to reduce or eliminate elective procedures for those patients with pain from spine, nerve and joint disease. The impact of these decisions, which were needed to reduce the spread of the pandemic, led to a delay in care for many patients. We hence review an emergence plan to reinitiate this pain-related care. The goal is to outline a path to work with federal, state, and local authorities to combat the spread of the pandemic and minimize the deleterious impact of pain and suffering on our chronic pain patients.