Inflammation, organomegaly, and muscle wasting despite hyperphagia in a mouse model of burn cachexia.

BACKGROUND: Burn injury results in a chronic inflammatory, hypermetabolic, and hypercatabolic state persisting long after initial injury and wound healing. Burn survivors experience a profound and prolonged loss of lean body mass, fat mass, and bone mineral density, associated with significant morbidity and reduced quality of life. Understanding the mechanisms responsible is essential for developing therapies. A complete characterization of the pathophysiology of burn cachexia in a reproducible mouse model was lacking. METHODS: Young adult (12-16 weeks of age) male C57BL/6J mice were given full thickness burns using heated brass plates or sham injury. Food and water intake, organ and muscle weights, and muscle fiber diameters were measured. Body composition was determined by Piximus. Plasma analyte levels were determined by bead array assay. RESULTS: Survival and weight loss were dependent upon burn size. The body weight nadir in burned mice was 14 days, at which time we observed reductions in total body mass, lean carcass mass, individual muscle weights, and muscle fiber cross-sectional area. Muscle loss was associated with increased expression of the muscle ubiquitin ligase, MuRF1. Burned mice also exhibited reduced fat mass and bone mineral density, concomitant with increased liver, spleen, and heart mass. Recovery of initial body weight occurred at 35 days; however, burned mice exhibited hyperphagia and polydipsia out to 80 days. Burned mice had significant increases in serum cytokine, chemokine, and acute phase proteins, consistent with findings in human burn subjects. CONCLUSIONS: This study describes a mouse model that largely mimics human pathophysiology following severe burn injury. These baseline data provide a framework for mouse-based pharmacological and genetic investigation of burn-injury-associated cachexia

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

American College of Rheumatology Provisional Criteria for Clinically Relevant Improvement in Children and Adolescents With Childhood-Onset Systemic Lupus Erythematosus

10.1002/acr.23834ARTHRITIS CARE & RESEARCH715579-59

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Primary gastrointestinal tract lymphoma in the pediatric patient: review of 265 patients from the SEER registry

The objective of this study is to determine outcomes of pediatric patients with primary gastrointestinal tract lymphoma (PGTL) and the impact of surgery or radiation on survival. The Surveillance, Epidemiology, and End Result database was queried from 1973 to 2006 for patients younger than 20 years with PGTL. 265 patients with PGTL were identified. Overall 5- and 10-year survivals were 84% and 83%, respectively. Tumors of the stomach (9%) and rectum/anus (2%) had the worst and best 10-year survivals, respectively (59% vs 100%, P = .023). There was no significant difference in 10-year survival for patients younger than 10 years of age who had surgical extirpation (83% vs 85% no surgery, P = .958) or radiotherapy (76% vs 85% no radiotherapy, P = .532). However, there was a significantly decreased 10-year survival in patients 10 years or older who had surgical extirpation (79% vs 100% no surgery, P = .013) or radiotherapy (49% vs 87% no radiotherapy, P = .001). Under multivariate analysis, tumor location was an independent predictor of improved survival (small bowel, HR 0.21, P = .002; large bowel, HR 0.23, P = .004). We found no significant survival advantage for surgical extirpation or radiotherapy in patients younger than 10 years with PGTL, whereas either treatment modality was associated with lower survival in patients 10 years or older

Long-Term Antipsychotic Use and Major Cardiovascular Events

Objective: Chronic treatment with antipsychotics may result in both metabolic side effects and cardiovascular disease. Our aim was to evaluate the effect of antipsychotic medications categorized by their metabolic side effect profiles as low, intermediate, or high risk on major cardiovascular events.Methods: A retrospective cohort study was conductedin adult outpatients aged 30 years or older initiating antipsychotic treatment from 2002 to 2007. Antipsychotic medications were divided into 3 groups (low-, intermediate-, and high-risk) according to the severity of their side-effect profiles in developing metabolic abnormalities associated with cardiovascular disease. The primary outcome measure was the time to the composite of acute myocardial infarction, acute coronary syndrome, ischemic stroke, peripheral artery disease, or a new revascularization procedure. Inverse probability weighting of a marginal structural Cox model was used to adjust for confounding.Results: A total of 1,008 patients were included (meanage = 72.4 years, median follow-up = 36.5 months), and 19.6% of patients experienced the primary outcome. The adjusted hazard ratios of a major cardiovascular event for patients in the high- or intermediate-risk medication groups compared to the low-risk group were 2.82 (95% CI, 1.57?5.05) and 2.57 (95% CI, 1.43?4.63), respectively.Conclusions: Older adult patients under antipsychotic regimens with high or intermediate risk of metabolic side effects may face a higher incidence of major cardiovascular events than those under a low-risk regimen during long-term follow-up.Fil: Szmulewicz, Alejandro G.. Universidad Favaloro; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital de Emergencias Psiquiáicas "Torcuato de Alvear"; Argentina. Universidad de Buenos Aires; ArgentinaFil: Angriman, Federico. Universidad de Buenos Aires; Argentina. Harvard T. H. Chan School of Public Health; Estados UnidosFil: Pedroso, Felipe E.. Harvard T. H. Chan School of Public Health; Estados Unidos. Columbia University Medical Center; Estados UnidosFil: Vázquez, Carolina. Hospital Italiano; ArgentinaFil: Martino, Diego Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Favaloro; Argentin