Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V.

BEATS: Bayesian hybrid design with flexible sample size adaptation for time-to-event endpoints.

As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages.

Impact of socioeconomics and race on clinical follow-up and trial enrollment and adherence in cerebral cavernous malformation.

Cerebral cavernous malformation (CCM) affects more than a million Americans but advanced care for symptomatic lesions and access to research studies is largely limited to referral academic centers MATERIALS AND METHODS: A cohort of CCM patients screened for research studies at an accredited center of excellence for CCM was analyzed. Demographics, lesion location, history of hemorrhage, insurance type and area of deprivation index (ADI) were collected. Primary outcomes were clinical follow-up within a year from initial evaluation, and enrollment and adherence in clinical trials among eligible subjects RESULTS: A majority (52.

Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma.

Background: Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species.

Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials.

Purpose: Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials.

Methods: BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error.

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease.

Patients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients.

Perfusion and Permeability MRI Predicts Future Cavernous Angioma Hemorrhage and Growth.

Background: Cerebral cavernous angioma (CA) is a capillary vasculopathy affecting more than a million Americans with a small fraction of cases demonstrating lesional bleed or growth with major clinical sequelae. Perfusion and permeability are fundamental features of CA pathophysiology, but their role as prognostic biomarkers is unclear.

Purpose: To investigate whether perfusion or permeability lesional descriptors derived from dynamic contrast-enhanced quantitative perfusion (DCEQP) magnetic resonance imaging (MRI) can predict subsequent lesional bleed/growth in the year following imaging.

Perfusion and permeability as diagnostic biomarkers of cavernous angioma with symptomatic hemorrhage.

Cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of rebleeding, and hence an accurate diagnosis is needed. With blood flow and vascular leak as established mechanisms, we analyzed perfusion and permeability derivations of dynamic contrast-enhanced quantitative perfusion (DCEQP) MRI in 745 lesions of 205 consecutive patients. Thirteen respective derivations of lesional perfusion and permeability were compared between lesions that bled within a year prior to imaging (N = 86), versus non-CASH (N = 659) using machine learning and univariate analyses.

Contact reductions from live poultry market closures limit the epidemic of human infections with H7N9 influenza.

An early steep increase in the number of humans infected with avian influenza A(H7N9) virus was observed in China, raising great public concern domestically and internationally. Little is known about the dynamics of the transmission contacts between poultry and human populations, although such understanding is essential for developing effective strategies to control this zoonosis. In this study, we evaluated the effects of contact reductions from live poultry markets (LPMs) closures on the transmission of H7N9 virus during epidemics in Guangdong Province, China.

An Integrative Analysis Reveals a Central Role of P53 Activation via MDM2 in Zika Virus Infection Induced Cell Death.

(ZIKV) infection is an emerging global threat that is suspected to be associated with fetal microcephaly. However, the molecular mechanisms underlying ZIKV disease pathogenesis in humans remain elusive. Here, we investigated the human protein interaction network associated with ZIKV infection using a systemic virology approach, and reconstructed the transcriptional regulatory network to analyze the mechanisms underlying ZIKV-elicited microcephaly pathogenesis.

Model-informed risk assessment for Zika virus outbreaks in the Asia-Pacific regions.

Recently, Zika virus (ZIKV) has been recognized as a significant threat to global public health. The disease was present in large parts of the Americas, the Caribbean, and also the western Pacific area with southern Asia during 2015 and 2016. However, little is known about the factors affecting the transmission of ZIKV.

Dynamic Forecasting of Zika Epidemics Using Google Trends.

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.