Background and aims Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity. Methods Thirty patients with COVID-19 and a mean age of 65.6 ± 13.1 years from a level-one medical center in Berlin, Germany, were included in the present cross-sectional analysis. COVID-19 was confirmed by polymerase chain reaction (PCR) from nasal and throat swabs. A severe clinical course of COVID-19 was defined by hospitalization in the intensive care unit (ICU) and/or invasive mechanical ventilation. Fat was measured at the level of the first lumbar vertebra on routinely acquired low-dose chest computed tomography (CT). Results An increase in visceral fat area (VFA) by ten square centimeters was associated with a 1.37-fold higher likelihood of ICU treatment and a 1.32-fold higher likelihood of mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference was associated with a 1.13-fold higher likelihood of ICU treatment and a 1.25-fold higher likelihood of mechanical ventilation. Conclusions Our proof-of-concept study suggests that visceral adipose tissue and upper abdominal circumference specifically increase the likelihood of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routine chest CTs may therefore be a simple tool for risk assessment in COVID-19 patients.

Writing Committee for the REMAP-CAP Investigators IMPORTANCE The evidence for benefit of convalescent plasma for critically ill patients with COVID-19 is inconclusive.OBJECTIVE To determine whether convalescent plasma would improve outcomes for critically ill adults with COVID-19. DESIGN, SETTING, AND PARTICIPANTSThe ongoing Randomized, Embedded, Multifactorial, Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) enrolled and randomized 4763 adults with suspected or confirmed COVID-19 between March 9, 2020, and January 18, 2021, within at least 1 domain; 2011 critically ill adults were randomized to open-label interventions in the immunoglobulin domain at 129 sites in 4 countries. Follow-up ended on April 19, 2021. INTERVENTIONSThe immunoglobulin domain randomized participants to receive 2 units of high-titer, ABO-compatible convalescent plasma (total volume of 550 mL ± 150 mL) within 48 hours of randomization (n = 1084) or no convalescent plasma (n = 916). MAIN OUTCOMES AND MEASURESThe primary ordinal end point was organ support-free days (days alive and free of intensive care unit-based organ support) up to day 21 (range, −1 to 21 days; patients who died were assigned -1 day). The primary analysis was an adjusted bayesian cumulative logistic model. Superiority was defined as the posterior probability of an odds ratio (OR) greater than 1 (threshold for trial conclusion of superiority >99%). Futility was defined as the posterior probability of an OR less than 1.2 (threshold for trial conclusion of futility >95%). An OR greater than 1 represented improved survival, more organ support-free days, or both. The prespecified secondary outcomes included in-hospital survival; 28-day survival; 90-day survival; respiratory support-free days; cardiovascular support-free days; progression to invasive mechanical ventilation, extracorporeal mechanical oxygenation, or death; intensive care unit length of stay; hospital length of stay; World Health Organization ordinal scale score at day 14; venous thromboembolic events at 90 days; and serious adverse events. RESULTS Among the 2011 participants who were randomized (median age, 61 [IQR, 52 to 70] years and 645/1998 [32.3%] women), 1990 (99%) completed the trial. The convalescent plasma intervention was stopped after the prespecified criterion for futility was met. The median number of organ support-free days was 0 (IQR, -1 to 16) in the convalescent plasma group and 3 (IQR, -1 to 16) in the no convalescent plasma group. The in-hospital mortality rate was 37.3% (401/1075) for the convalescent plasma group and 38.4% (347/904) for the no convalescent plasma group and the median number of days alive and free of organ support was 14 (IQR, 3 to 18) and 14 (IQR, 7 to 18), respectively. The median-adjusted OR was 0.97 (95% credible interval, 0.83 to 1.15) and the posterior probability of futility (OR <1.2) was 99.4% for the convalescent plasma group compared with the no convalescent plasma group. The treatment effects were consistent across the primary outcome and the 11...

The risk of developing pancreatitis is elevated in type 2 diabetes and obesity. Cases of pancreatitis have been reported in type 2 diabetes patients treated with GLP-1 (GLP-1R) receptor agonists. To examine whether the GLP-1R agonist exenatide potentially induces or modulates pancreatitis, the effect of exenatide was evaluated in normal or diabetic rodents. Normal and diabetic rats received a single exenatide dose (0.072, 0.24, and 0.72 nmol/kg) or vehicle. Diabetic ob/ob or HF-STZ mice were infused with exenatide (1.2 and 7.2 nmol·kg Ϫ1 ·day Ϫ1 ) or vehicle for 4 wk. Post-exenatide treatment, pancreatitis was induced with caerulein (CRN) or sodium taurocholate (ST), and changes in plasma amylase and lipase were measured. In ob/ob mice, plasma cytokines (IL-1␤, IL-2, IL-6, MCP-1, IFN␥, and TNF␣) and pancreatitis-associated genes were assessed. Pancreata were weighed and examined histologically. Exenatide treatment alone did not modify plasma amylase or lipase in any models tested. Exenatide attenuated CRN-induced release of amylase and lipase in normal rats and ob/ob mice but did not modify the response to ST infusion. Plasma cytokines and pancreatic weight were unaffected by exenatide. Exenatide upregulated Reg3b but not Il6, Ccl2, Nfkb1, or Vamp8 expression. Histological analysis revealed that the highest doses of exenatide decreased CRN-or ST-induced acute inflammation, vacuolation, and acinar single cell necrosis in mice and rats, respectively. Ductal cell proliferation rates were low and similar across all groups of ob/ob mice. In conclusion, exenatide did not modify plasma amylase and lipase concentrations in rodents without pancreatitis and improved chemically induced pancreatitis in normal and diabetic rodents. diabetes; mouse; rat; caerulein; sodium taurocholate; pancreatic duct GLUCAGON-LIKE PEPTIDE-1 (GLP-1) exerts multiple glucoregulatory actions by enhancing glucose-dependent insulin secretion, regulating gastric emptying, decreasing postprandial glucagon secretion, and decreasing food intake. Moreover, GLP-1 has been observed to improve ␤-cell mass by augmenting ␤-cell survival and proliferation in rodents (7,19,42). In recent years, the therapeutic potential of GLP-1 receptor (GLP-1R) agonists or dipeptidyl peptidase IV (DPP IV) inhibitors for treatment of type 2 diabetes gained widespread attention, and several drugs affecting the GLP-1 pathway were approved to control hyperglycemia, including the GLP-1R agonists exenatide and liraglutide (12, 18) and the DPP IV inhibitors sitagliptin and saxagliptin.Cases of pancreatitis have been observed in patients treated with GLP-1 receptor agonists and DPP IV inhibitors (1, 9, 10, 13, 38). Acute pancreatitis is a complex clinical condition that ranges in severity from mild to life-threatening. Abdominal pain, ultrasound-confirmed pancreatic pathological changes and increased plasma amylase and lipase concentrations are the most common markers of acute pancreatitis in the clinic (21). Type 2 diabetes and/or obesity are risk factors for the development of pa...

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware. Chest radiographs are among the most frequently used imaging procedures in radiology. They have been widely employed in the field of computer vision, as chest radiographs are a standardized technique and, if compared to other radiological examinations such as computed tomography or magnetic resonance imaging, contain a smaller group of relevant pathologies. Although many artificial neural networks for the classification of chest radiographs have been developed, it is still subject to intensive research. Only a few groups design their own networks from scratch, while most use already established architectures, such as ResNet-50 or DenseNet-121 (with 50 and 121 representing the number of layers within the respective neural network) 1-6. These neural networks have often been trained on large, openly available datasets, such as ImageNet, and are therefore already able to recognize numerous image features. When training a model for a new task, such as the classification of chest radiographs, the use of pre-trained networks may improve the training speed and accuracy of the new model, since important image features that have already been learned can be transferred to the new task and do not have to be learned again. However, the feature space of freely available data sets such as ImageNet differs from chest radiographs as they contain color images and more categories. The ImageNet Challenge includes 1,000 possible categories per image, while CheXpert, a large freely available data set of chest radiographs, only distinguishes between 14 categories (or classes) 7 , and the COVID-19 Image Data Collection only differentiates between three classes 8. Although the ImageNet...