TY  - BOOK
A1  - Gänz, Victoria
A1  - Hinz, Carsten
A1  - Huhn, Nicola
A1  - Klitsch, Constantin
A1  - Löffler, Robert
A1  - Meis, Robin
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schellen, Ricarda
A1  - Schrödter, Ayla
A1  - Schulz, Oliver
A1  - Simon, Veronika
A1  - Trojecka, Anetta
A1  - Verwohlt, Peter
A1  - Vogler, Christin
A1  - Vogt, Birgit
T1  - #Gesellschaftslehre 7/8
BT  - Gesellschaftslehre für die Gesamtschule und Sekundarschule
Y1  - 2022
SN  - 978-3-661-70052-6
VL  - [Schülerband]
PB  - C.C. Buchner
CY  - Bamberg
ER  - 
TY  - BOOK
A1  - Kirchner, Vera
A1  - Deeken, Johannes
A1  - Hinz, Carsten
A1  - Klitsch, Constantin
A1  - Löffler, Robert
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
T1  - Differenzierungsheft
BT  - Materialien für einen differenzierenden und sprachsensiblen Unterricht
T3  - #Wirtschaft Band 7/8 - Nordrhein-Westfalen
Y1  - 2022
SN  - 978-3-66182-249-5
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - GEN
A1  - Benecke, Karin
A1  - Deeken, Johannes
A1  - Hammer, Carolin
A1  - Hinz, Carsten
A1  - Löffler, Robert
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
A1  - Scherer, Hubertus
ED  - Kirchner, Vera
T1  - #Wirtschaft – Niedersachsen
BT  - Wirtschaft für die Haupt-, Real-, Ober- und Gesamtschule : Gesamtband für die Jahrgangsstufen 7–10
Y1  - 2020
SN  - 978-3-661-82241-9
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - JOUR
A1  - Damle, Mitali
A1  - Sparre, Martin
A1  - Richter, Philipp
A1  - Hani, Maan H.
A1  - Nuza, Sebastian
A1  - Pfrommer, Christoph
A1  - Grand, Robert J. J.
A1  - Hoffman, Yehuda
A1  - Libeskind, Noam
A1  - Sorce, Jenny
A1  - Steinmetz, Mathias
A1  - Tempel, Elmo
A1  - Vogelsberger, Mark
A1  - Wang, Peng
T1  - Cold and hot gas distribution around the Milky-Way – M31 system in the HESTIA simulations
JF  - Monthly notices of the Royal Astronomical Society
N2  - Recent observations have revealed remarkable insights into the gas reservoir in the circumgalactic medium (CGM) of galaxy haloes. In this paper, we characterise the gas in the vicinity of Milky Way and Andromeda analogues in the hestia (High resolution Environmental Simulations of The Immediate Area) suite of constrained Local Group (LG) simulations. The hestia suite comprise of a set of three high-resolution arepo-based simulations of the LG, run using the Auriga galaxy formation model.
For this paper, we focus only on the 𝑧 = 0 simulation datasets and generate mock skymaps along with a power spectrum analysis to show that the distributions of ions tracing low-temperature gas (H i and Si iii) are more clumpy in comparison to warmer gas tracers (O vi, O vii and O viii). We compare to the spectroscopic CGM observations of M31 and low-redshift galaxies.
hestia under-produces the column densities of the M31 observations, but the simulations are consistent with the observations of low-redshift galaxies. A possible explanation for these findings is that the spectroscopic observations of M31 are contaminated by gas residing in the CGM of the Milky Way.
KW  - software: data analysis
KW  - software: simulations
KW  - Galaxy: evolution
KW  - galaxies: evolution
KW  - galaxies: Local Group
Y1  - 2022
U6  - https://doi.org/10.1093/mnras/stac663
SN  - 0035-8711
SN  - 1365-2966
VL  - 512
SP  - 3717
EP  - 3737
PB  - Oxford Univ. Press
CY  - Oxford
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  -