TY - JOUR A1 - Palkopoulou, Eleftheria A1 - Lipson, Mark A1 - Mallick, Swapan A1 - Nielsen, Svend A1 - Rohland, Nadin A1 - Baleka, Sina Isabelle A1 - Karpinski, Emil A1 - Ivancevici, Atma M. A1 - Thu-Hien To, A1 - Kortschak, Daniel A1 - Raison, Joy M. A1 - Qu, Zhipeng A1 - Chin, Tat-Jun A1 - Alt, Kurt W. A1 - Claesson, Stefan A1 - Dalen, Love A1 - MacPhee, Ross D. E. A1 - Meller, Harald A1 - Rocar, Alfred L. A1 - Ryder, Oliver A. A1 - Heiman, David A1 - Young, Sarah A1 - Breen, Matthew A1 - Williams, Christina A1 - Aken, Bronwen L. A1 - Ruffier, Magali A1 - Karlsson, Elinor A1 - Johnson, Jeremy A1 - Di Palma, Federica A1 - Alfoldi, Jessica A1 - Adelsoni, David L. A1 - Mailund, Thomas A1 - Munch, Kasper A1 - Lindblad-Toh, Kerstin A1 - Hofreiter, Michael A1 - Poinar, Hendrik A1 - Reich, David T1 - A comprehensive genomic history of extinct and living elephants JF - Proceedings of the National Academy of Sciences of the United States of America KW - paleogenomics KW - elephantid evolution KW - mammoth KW - admixture KW - species divergence Y1 - 2018 U6 - https://doi.org/10.1073/pnas.1720554115 SN - 0027-8424 VL - 115 IS - 11 SP - E2566 EP - E2574 PB - National Acad. of Sciences CY - Washington ER - TY - GEN A1 - Meyer, Matthias A1 - Palkopoulou, Eleftheria A1 - Baleka, Sina Isabelle A1 - Stiller, Mathias A1 - Penkman, Kirsty E. H. A1 - Alt, Kurt W. A1 - Ishida, Yasuko A1 - Mania, Dietrich A1 - Mallick, Swapan A1 - Meijer, Tom A1 - Meller, Harald A1 - Nagel, Sarah A1 - Nickel, Birgit A1 - Ostritz, Sven A1 - Rohland, Nadin A1 - Schauer, Karol A1 - Schüler, Tim A1 - Roca, Alfred L. A1 - Reich, David A1 - Shapiro, Beth A1 - Hofreiter, Michael T1 - Palaeogenomes of Eurasian straight-tusked elephants challenge the current view of elephant evolution T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - The straight-tusked elephants Palaeoloxodon spp. were widespread across Eurasia during the Pleistocene. Phylogenetic reconstructions using morphological traits have grouped them with Asian elephants (Elephas maximus), and many paleontologists place Palaeoloxodon within Elephas. Here, we report the recovery of full mitochondrial genomes from four and partial nuclear genomes from two P. antiquus fossils. These fossils were collected at two sites in Germany, Neumark-Nord and Weimar-Ehringsdorf, and likely date to interglacial periods similar to 120 and similar to 244 thousand years ago, respectively. Unexpectedly, nuclear and mitochondrial DNA analyses suggest that P. antiquus was a close relative of extant African forest elephants (Loxodonta cyclotis). Species previously referred to Palaeoloxodon are thus most parsimoniously explained as having diverged from the lineage of Loxodonta, indicating that Loxodonta has not been constrained to Africa. Our results demonstrate that the current picture of elephant evolution is in need of substantial revision. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 790 KW - genome sequence KW - woolly mammoth KW - Palaeoloxodon-antiquus KW - phylogenetic analysis KW - African elephants KW - DNA KW - Pleistocene KW - alignment KW - ancient KW - reveal Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-440139 SN - 1866-8372 IS - 790 ER - TY - JOUR A1 - Meyer, Matthias A1 - Palkopoulou, Eleftheria A1 - Baleka, Sina Isabelle A1 - Stiller, Mathias A1 - Penkman, Kirsty E. H. A1 - Alt, Kurt W. A1 - Ishida, Yasuko A1 - Mania, Dietrich A1 - Mallick, Swapan A1 - Meijer, Tom A1 - Meller, Harald A1 - Nagel, Sarah A1 - Nickel, Birgit A1 - Ostritz, Sven A1 - Rohland, Nadin A1 - Schauer, Karol A1 - Schueler, Tim A1 - Roca, Alfred L. A1 - Reich, David A1 - Shapiro, Beth A1 - Hofreiter, Michael T1 - Palaeogenomes of Eurasian straight-tusked elephants challenge the current view of elephant evolution JF - eLife N2 - The straight-tusked elephants Palaeoloxodon spp. were widespread across Eurasia during the Pleistocene. Phylogenetic reconstructions using morphological traits have grouped them with Asian elephants (Elephas maximus), and many paleontologists place Palaeoloxodon within Elephas. Here, we report the recovery of full mitochondrial genomes from four and partial nuclear genomes from two P. antiquus fossils. These fossils were collected at two sites in Germany, Neumark-Nord and Weimar-Ehringsdorf, and likely date to interglacial periods similar to 120 and similar to 244 thousand years ago, respectively. Unexpectedly, nuclear and mitochondrial DNA analyses suggest that P. antiquus was a close relative of extant African forest elephants (Loxodonta cyclotis). Species previously referred to Palaeoloxodon are thus most parsimoniously explained as having diverged from the lineage of Loxodonta, indicating that Loxodonta has not been constrained to Africa. Our results demonstrate that the current picture of elephant evolution is in need of substantial revision. Y1 - 2017 U6 - https://doi.org/10.7554/eLife.25413 SN - 2050-084X VL - 6 PB - eLife Sciences Publications CY - Cambridge ER - TY - JOUR A1 - Chan, Lili A1 - Chaudhary, Kumardeep A1 - Saha, Aparna A1 - Chauhan, Kinsuk A1 - Vaid, Akhil A1 - Zhao, Shan A1 - Paranjpe, Ishan A1 - Somani, Sulaiman A1 - Richter, Felix A1 - Miotto, Riccardo A1 - Lala, Anuradha A1 - Kia, Arash A1 - Timsina, Prem A1 - Li, Li A1 - Freeman, Robert A1 - Chen, Rong A1 - Narula, Jagat A1 - Just, Allan C. A1 - Horowitz, Carol A1 - Fayad, Zahi A1 - Cordon-Cardo, Carlos A1 - Schadt, Eric A1 - Levin, Matthew A. A1 - Reich, David L. A1 - Fuster, Valentin A1 - Murphy, Barbara A1 - He, John C. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - AKI in hospitalized patients with COVID-19 JF - Journal of the American Society of Nephrology : JASN N2 - Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge. KW - acute renal failure KW - clinical nephrology KW - dialysis KW - COVID-19 Y1 - 2021 U6 - https://doi.org/10.1681/ASN.2020050615 SN - 1046-6673 SN - 1533-3450 VL - 32 IS - 1 SP - 151 EP - 160 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Vaid, Akhil A1 - Somani, Sulaiman A1 - Russak, Adam J. A1 - De Freitas, Jessica K. A1 - Chaudhry, Fayzan F. A1 - Paranjpe, Ishan A1 - Johnson, Kipp W. A1 - Lee, Samuel J. A1 - Miotto, Riccardo A1 - Richter, Felix A1 - Zhao, Shan A1 - Beckmann, Noam D. A1 - Naik, Nidhi A1 - Kia, Arash A1 - Timsina, Prem A1 - Lala, Anuradha A1 - Paranjpe, Manish A1 - Golden, Eddye A1 - Danieletto, Matteo A1 - Singh, Manbir A1 - Meyer, Dara A1 - O'Reilly, Paul F. A1 - Huckins, Laura A1 - Kovatch, Patricia A1 - Finkelstein, Joseph A1 - Freeman, Robert M. A1 - Argulian, Edgar A1 - Kasarskis, Andrew A1 - Percha, Bethany A1 - Aberg, Judith A. A1 - Bagiella, Emilia A1 - Horowitz, Carol R. A1 - Murphy, Barbara A1 - Nestler, Eric J. A1 - Schadt, Eric E. A1 - Cho, Judy H. A1 - Cordon-Cardo, Carlos A1 - Fuster, Valentin A1 - Charney, Dennis S. A1 - Reich, David L. A1 - Böttinger, Erwin A1 - Levin, Matthew A. A1 - Narula, Jagat A1 - Fayad, Zahi A. A1 - Just, Allan C. A1 - Charney, Alexander W. A1 - Nadkarni, Girish N. A1 - Glicksberg, Benjamin S. T1 - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes. KW - machine learning KW - COVID-19 KW - electronic health record KW - TRIPOD KW - clinical KW - informatics KW - prediction KW - mortality KW - EHR KW - cohort KW - hospital KW - performance Y1 - 2020 U6 - https://doi.org/10.2196/24018 SN - 1439-4456 SN - 1438-8871 VL - 22 IS - 11 PB - Healthcare World CY - Richmond, Va. ER -