Beth Levine in memoriam

Beth Levine was born on 7 April 1960 in Newark, New Jersey. She went to college at Brown University where she received an A.B. Magna Cum Laude, and she attended medical school at Cornell University Medical College, receiving her MD in 1986. She completed her internship and residency in Internal Medicine at Mount Sinai Hospital in New York, and her fellowship in Infectious Diseases at The Johns Hopkins Hospital. Most recently, Beth was a Professor of Internal Medicine and Microbiology, Director of the Center for Autophagy Research, and holder of the Charles Sprague Distinguished Chair in Biomedical Science at the University of Texas Southwestern Medical Center in Dallas. Beth died on 15 June 2020 from cancer. Beth is survived by her husband, Milton Packer, and their two children, Rachel (26 years old) and Ben (25 years old). Dr. Levine was as an international leader in the field of autophagy research. Her laboratory identified the mammalian autophagy gene BECN1/beclin 1; identified conserved mechanisms underlying the regulation of autophagy (e.g. BCL2-BECN1 complex formation, insulin-like signaling, EGFR, ERBB2/HER2 and AKT1-mediated BECN1 phosphosphorylation); and provided the first evidence that autophagy genes are important in antiviral host defense, tumor suppression, lifespan extension, apoptotic corpse clearance, metazoan development, Na,K-ATPase-regulated cell death, and the beneficial metabolic effects of exercise. She developed a potent autophagy-inducing cell permeable peptide, Tat-beclin 1, which has potential therapeutic applications in a range of diseases. She was a founding Associate Editor of the journal Autophagy and an editorial board member of Cell and Cell Host & Microbe. She has received numerous awards/honors in recognition of her scientific achievement, including: The American Cancer Society Junior Faculty Research Award (1994); election into the American Society of Clinical Investigation (2000); the Ellison Medical Foundation Senior Scholars Award in Global Infectious Diseases (2004); elected member, American Association of Physicians (2005); appointment as a Howard Hughes Medical Institute Investigator (2008); Edith and Peter O’Donnell Award in Medicine (2008); elected fellow, American Association for the Advancement of Science (2012); election into the National Academy of Sciences (2013); election into the Academy of Medicine, Engineering and Science of Texas (2013); the ASCI Stanley J. Korsmeyer Award (2014); Phyllis T. Bodel Women in Medicine Award, Yale University School of Medicine (2018); recipient, Barcroft Medal, Queen’s University Belfast (2018).Fil: An, Zhenyi. No especifíca;Fil: Ballabi, Andrea. No especifíca;Fil: Bennett, Lynda. No especifíca;Fil: Boya, Patricia. No especifíca;Fil: Cecconi, Francesco. No especifíca;Fil: Chiang, Wei Chung. No especifíca;Fil: Codogno, Patrice. No especifíca;Fil: Colombo, Maria Isabel. No especifíca;Fil: Cuervo, Ana Maria. No especifíca;Fil: Debnath, Jayanta. No especifíca;Fil: Deretic, Vojo. No especifíca;Fil: Dikic, Ivan. No especifíca;Fil: Dionne, Keith. No especifíca;Fil: Dong, Xiaonan. No especifíca;Fil: Elazar, Zvulun. No especifíca;Fil: Galluzzi, Lorenzo. No especifíca;Fil: Gentile, Frank. No especifíca;Fil: Griffin, Diane E.. No especifíca;Fil: Hansen, Malene. No especifíca;Fil: Hardwick, J. Marie. No especifíca;Fil: He, Congcong. No especifíca;Fil: Huang, Shu Yi. No especifíca;Fil: Hurley, James. No especifíca;Fil: Jackson, William T.. No especifíca;Fil: Jozefiak, Cindy. No especifíca;Fil: Kitsis, Richard N.. No especifíca;Fil: Klionsky, Daniel J.. No especifíca;Fil: Kroemer, Guido. No especifíca;Fil: Meijer, Alfred J.. No especifíca;Fil: Meléndez, Alicia. No especifíca;Fil: Melino, Gerry. No especifíca;Fil: Mizushima, Noboru. No especifíca;Fil: Murphy, Leon O.. No especifíca;Fil: Nixon, Ralph. No especifíca;Fil: Orvedahl, Anthony. No especifíca;Fil: Pattingre, Sophie. No especifíca;Fil: Piacentini, Mauro. No especifíca;Fil: Reggiori, Fulvio. No especifíca;Fil: Ross, Theodora. No especifíca;Fil: Rubinsztein, David C.. No especifíca;Fil: Ryan, Kevin. No especifíca;Fil: Sadoshima, Junichi. No especifíca;Fil: Schreiber, Stuart L.. No especifíca;Fil: Scott, Frederick. No especifíca;Fil: Sebti, Salwa. No especifíca;Fil: Shiloh, Michael. No especifíca;Fil: Shoji, Sanae. No especifíca;Fil: Simonsen, Anne. No especifíca;Fil: Smith, Haley. No especifíca;Fil: Sumpter, Kathryn M.. No especifíca;Fil: Thompson, Craig B.. No especifíca;Fil: Thorburn, Andrew. No especifíca;Fil: Thumm, Michael. No especifíca;Fil: Tooze, Sharon. No especifíca;Fil: Vaccaro, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; ArgentinaFil: Virgin, Herbert W.. No especifíca;Fil: Wang, Fei. No especifíca;Fil: White, Eileen. No especifíca;Fil: Xavier, Ramnik J.. No especifíca;Fil: Yoshimori, Tamotsu. No especifíca;Fil: Yuan, Junying. No especifíca;Fil: Yue, Zhenyu. No especifíca;Fil: Zhong, Qing. No especifíca

Diabetic ketoacidosis drives COVID-19 related hospitalizations in children with type 1 diabetes.

BackgroundDiabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies.MethodsT1D Exchange coordinated a US type 1 diabetes COVID-19 registry. Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization.ResultsData from 266 patients with previously established type 1 diabetes aged <19 years with COVID-19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID-19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34-1.84) after adjusting for age, gender, insurance, and race/ethnicity.ConclusionsHigher A1c remained the only predictor for hospitalization with COVID-19. Diabetic ketoacidosis is the primary concern among this group

Diabetic ketoacidosis drives COVID‐19

BackgroundDiabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies.MethodsT1D Exchange coordinated a US type 1 diabetes COVID-19 registry. Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization.ResultsData from 266 patients with previously established type 1 diabetes aged <19 years with COVID-19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID-19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34-1.84) after adjusting for age, gender, insurance, and race/ethnicity.ConclusionsHigher A1c remained the only predictor for hospitalization with COVID-19. Diabetic ketoacidosis is the primary concern among this group

Inequities in Diabetic Ketoacidosis Among Patients With Type 1 Diabetes and COVID-19: Data From 52 US Clinical Centers

We examined whether diabetic ketoacidosis (DKA), a serious complication of type 1 diabetes (T1D) was more prevalent among Non-Hispanic (NH) Black and Hispanic patients with T1D and laboratory-confirmed coronavirus disease 2019 (COVID-19) compared with NH Whites. This is a cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 52 clinical sites in the United States, data were collected from April to August 2020. We examined the distribution of patient factors and DKA events across NH White, NH Black, and Hispanic race/ethnicity groups. Multivariable logistic regression analysis was performed to examine the odds of DKA among NH Black and Hispanic patients with T1D as compared with NH White patients, adjusting for potential confounders, such as age, sex, insurance, and last glycated hemoglobin A1c (HbA1c) level. We included 180 patients with T1D and laboratory-confirmed COVID-19 in the analysis. Forty-four percent (n = 79) were NH White, 31% (n = 55) NH Black, 26% (n = 46) Hispanic. NH Blacks and Hispanics had higher median HbA1c than Whites (%-points [IQR]: 11.7 [4.7], P < 0.001, and 9.7 [3.1] vs 8.3 [2.4], P = 0.01, respectively). We found that more NH Black and Hispanic presented with DKA compared to Whites (55% and 33% vs 13%, P < 0.001 and P = 0.008, respectively). After adjusting for potential confounders, NH Black patients continued to have greater odds of presenting with DKA compared with NH Whites (OR [95% CI]: 3.7 [1.4, 10.6]). We found that among T1D patients with COVID-19 infection, NH Black patients were more likely to present in DKA compared with NH White patients. Our findings demonstrate additional risk among NH Black patients with T1D and COVID-19

If and when: intrinsic differences and environmental stressors influence migration in brown trout (Salmo trutta)

Partial migration is a common phenomenon, yet the causes of individual differences in migratory propensity are not well understood. We examined factors that potentially influence timing of migration and migratory propensity in a wild population of juvenile brown trout (Salmo trutta) by combining experimental manipulations with passive integrated transponder telemetry. Individuals were subjected to one of six manipulations: three designed to mimic natural stressors (temperature increase, food deprivation, and chase by a simulated predator), an injection of exogenous cortisol designed to mimic an extreme physiological challenge, a sham injection, and a control group. By measuring length and mass of 923 individuals prior to manipulation and by monitoring tagged individuals as they left the stream months later, we assessed whether pre-existing differences influenced migratory tendency and timing of migration, and whether our manipulations affected growth, condition, and timing of migration. We found that pre-existing differences predicted migration, with smaller individuals and individuals in poor condition having a higher propensity to migrate. Exogenous cortisol manipulation had the largest negative effect on growth and condition, and resulted in an earlier migration date. Additionally, low-growth individuals within the temperature and food deprivation treatments migrated earlier. By demonstrating that both pre-existing differences in organism state and additional stressors can affect whether and when individuals migrate, we highlight the importance of understanding individual differences in partial migration. These effects may carry over to influence migration success and affect the evolutionary dynamics of sub-populations experiencing different levels of stress, which is particularly relevant in a changing world