In situ transgenesis methods such as viruses and electroporation can rapidly create somatic transgenic mice but lack control over copy number, zygosity, and locus specificity. Here we establish mosaic analysis by dual recombinase-mediated cassette exchange (MADR), which permits stable labeling of mutant cells expressing transgenic elements from precisely defined chromosomal loci. We provide a toolkit of MADR elements for combination labeling, inducible and reversible transgene manipulation, VCre recombinase expression, and transgenesis of human cells. Further, we demonstrate the versatility of MADR by creating glioma models with mixed reporter-identified zygosity or with ''personalized'' driver mutations from pediatric glioma. MADR is extensible to thousands of existing mouse lines, providing a flexible platform to democratize the generation of somatic mosaic mice.

Objective To determine whether racial and ethnic differences exist among patients with similar access to care, we examined outcomes after heart failure hospitalization within a large municipal health system. Background Racial and ethnic disparities in heart failure outcomes are present in administrative data, and one explanation is differential access to care. Methods We performed a retrospective cohort study of 8,532 hospitalizations of adults with heart failure at 11 hospitals in New York City from 2007 to 2010. Primary exposure was ethnicity/race, and outcomes were 30- and 90-day readmission and 30-day and one-year mortality. Generalized estimating equations were used to test for association between ethnicity/race and outcomes with covariate adjustment. Results Of included hospitalizations, 4,305 (51%) were for blacks, 2,449 (29%) were for Hispanics, 1,494 (18%) were for whites, and 284 (3%) were for Asians. Compared to whites, blacks and Asians had lower one-year mortality, with adjusted odds ratios (aORs) 0.75 (95% CI 0.59–0.94) and 0.57 (95% CI 0.38–0.85), whereas Hispanics were not significantly different (aOR 0.81: 95% CI 0.64–1.03). Hispanics had higher odds of readmission than whites, with aORs 1.27 (95% CI 1.03–1.57) at 30 days and 1.40 (95% CI 1.15–1.70) at 90 days. Blacks had higher odds of readmission than whites at 90 days (aOR 1.21: 95% CI 1.01–1.47). Conclusions Racial and ethnic differences in outcomes after heart failure hospitalization were present within a large municipal health system. Access to a municipal health system may not be sufficient to eliminate disparities in heart failure outcomes.

Importance Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification, but are often inaccurate or incomplete. Machine learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective To develop algorithms that use readily available clinical data to identify heart failure patients while in the hospital. Design, Setting, and Participants We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients≥18 years who were admitted after January 1, 2013 and discharged prior to February 28, 2015 were included. From a random 75% sample of hospitalizations, we developed five algorithms for heart failure identification using electronic health record (EHR) data: 1) heart failure on problem list; 2) presence of at least one of three characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide≥500 pg/ml; 3) logistic regression of 30 clinically relevant structured data elements; 4) machine learning approach using unstructured notes; 5) machine learning approach using both structured and unstructured data. Main Outcome and Measure Heart failure diagnosis, based on discharge diagnosis and physician review of sampled charts. Results Of 47,119 included hospitalizations, 6,549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating curves (AUCs) of 0.953, 0.969, and 0.974, respectively. With PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. Conclusion and Relevance The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future EHR systems can improve cohort identification.

IntroductionMillions of children in India still suffer from poor health and under-nutrition, despite substantial improvement over decades of public health programmes. The Anganwadi centres under the Integrated Child Development Scheme (ICDS) provide a range of health and nutrition services to pregnant women, children <6 years and their mothers. However, major gaps exist in ICDS service delivery. The government is currently strengthening ICDS through an mHealth intervention called Common Application Software (ICDS-CAS) installed on smart phones, with accompanying multilevel data dashboards. This system is intended to be a job aid for frontline workers, supervisors and managers, aims to ensure better service delivery and supervision, and enable real-time monitoring and data-based decision-making. However, there is little to no evidence on the effectiveness of such large-scale mHealth interventions integrated with public health programmes in resource-constrained settings on the service delivery and subsequent health and nutrition outcomes.Methods and analysisThis study uses a village-matched controlled design with repeated cross-sectional surveys to evaluate whether ICDS-CAS can enable more timely and appropriate services to pregnant women, children <12 months and their mothers, compared with the standard ICDS programme. The study will recruit approximately 1500 Anganwadi workers and 6000+ mother-child dyads from 400+ matched-pair villages in Bihar and Madhya Pradesh. The primary outcomes are the proportion of beneficiaries receiving (a) adequate number of home visits and (b) appropriate level of counselling by the Anganwadi workers. Secondary outcomes are related to improvements in other ICDS services, and knowledge and practices of the Anganwadi workers and beneficiaries.Ethics and disseminationEthical oversight is provided by the Committee for the Protection of Human Subjects at the University of California at Berkeley, and the Suraksha Independent Ethics Committee in India. The results will be published in peer-reviewed journals and analysis data will be made public.Trial registration numberISRCTN83902145

As the list of putative driver mutations in glioma grows, we are just beginning to elucidate the effects of dysregulated developmental signaling pathways on the transformation of neural cells. We have employed a postnatal, mosaic, autochthonous glioma model that captures the first hours and days of gliomagenesis in more resolution than conventional genetically engineered mouse models of cancer. We provide evidence that disruption of the Nf1-Ras pathway in the ventricular zone at multiple signaling nodes uniformly results in rapid neural stem cell depletion, progenitor hyperproliferation, and gliogenic lineage restriction. Abolishing Ets subfamily activity, which is upregulated downstream of Ras, rescues these phenotypes and blocks glioma initiation. Thus, the Nf1-Ras-Ets axis might be one of the select molecular pathways that are perturbed for initiation and maintenance in glioma.