Summary Neurogenesis and gliogenesis continue in discrete regions of the adult mammalian brain. A fundamental question remains whether cell genesis occurs from distinct lineage-restricted progenitors or from self-renewing and multipotent neural stem cells in the adult brain. Here, we developed a genetic marking strategy for lineage-tracing of individual, quiescent, and nestin-expressing radial glia-like (RGL) precursors in the adult mouse dentate gyrus. Clonal analysis identified multiple modes of RGL activation, including asymmetric and symmetric self-renewal. Long-term lineage-tracing in vivo revealed a significant percentage of clones that contained RGL(s), neurons, and astrocytes, indicating capacity of individual RGLs for both self-renewal and multi-lineage differentiation. Furthermore, conditional Pten deletion in RGLs initially promotes their activation and symmetric self-renewal, but ultimately leads to terminal astrocytic differentiation and depletion in the adult hippocampus. Our study identifies RGLs as self-renewing and multipotent neural stem cells and provides novel insights into in vivo properties of adult neural stem cells.

Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Na€ ve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble meth-ods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival.Conclusions: Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients.NOTE: There were too few early-stage EOC patients with residual tumor. A definition for the significance of bold is P value of < 0.05.

Excess cellular iron increases reactive oxygen species (ROS) production and causes cellular damage. Mitochondria are the major site of iron metabolism and ROS production; however, few studies have investigated the role of mitochondrial iron in the development of cardiac disorders, such as ischemic heart disease or cardiomyopathy (CM). We observe increased mitochondrial iron in mice after ischemia/reperfusion (I/R) and in human hearts with ischemic CM, and hypothesize that decreasing mitochondrial iron protects against I/R damage and the development of CM. Reducing mitochondrial iron genetically through cardiac-specific overexpression of a mitochondrial iron export protein or pharmacologically using a mitochondria-permeable iron chelator protects mice against I/R injury. Furthermore, decreasing mitochondrial iron protects the murine hearts in a model of spontaneous CM with mitochondrial iron accumulation. Reduced mitochondrial ROS that is independent of alterations in the electron transport chain's ROS producing capacity contributes to the protective effects. Overall, our findings suggest that mitochondrial iron contributes to cardiac ischemic damage, and may be a novel therapeutic target against ischemic heart disease.

There is a strong perceived need for HIE, most respondents were not aware of HIE prior to this study, and there are certain types of data and presentations of data that are preferred by emergency physicians in the New York City region.

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Objective Demonstrate that use of the Washington State health information exchange (HIE) to facilitate access to prescription monitoring program (PMP) data enhances the effectiveness of a PMP. The increased accessibility will lead to improved patient care by giving providers more complete and recent data on patients' controlled substance prescriptions. Introduction Washington State experienced a five-fold increase in deaths from unintentional drug overdoses between 1998 and 2014. The PMP collects data on controlled substances prescribed to patients and makes the data available to healthcare providers, giving providers another tool for patient care and safety. Optimal impact for the program depends on providers regularly accessing the information to review patients' dispensing history. We have found through provider surveys and work with stakeholders that the best way to increase use is to make data seamlessly accessible through electronic health record systems (EHRs). This approach does not require a separate login to the PMP portal. This linkage works through the Health Information Exchange (HIE) to make PMP data available to providers via EHRs. The HIE facilitates electronic communication of patient information among organizations including hospitals and providers. In addition to the PMP, another resource to address the prescription drug abuse problem is the Emergency Department Information Exchange (EDIE), a web-based technology that specifically connects emergency departments statewide to track patients who visit multiple EDs. We also developed a connection between EDIE and PMP data through the HIE.

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