Nitric oxide mediates fluid accumulation during cardiopulmonary bypass

AbstractFluid accumulation during cardiopulmonary bypass may be related to the production of endogenous vasoactive substances. We investigated the role of nitric oxide in mediating fluid accumulation during cardiopulmonary bypass. Normothermic cardiopulmonary bypass was carried out for 3 hours in male Sprague-Dawley rats with constant, nonpulsatile flow and hemodilution. Fluid accumulation (rate of change of external reservoir volume) was measured under three experimental conditions: saline solution control ( n = 8), l-arginine infusion ( n = 6), and N -nitro-l-arginine methyl ester infusion ( n = 6). At the end of the experiments, body weight and organ wet/dry ratios were examined. Percentage weight gain was 77% greater in the N -nitro-l-arginine methyl ester group and 23% less in the l-arginine group compared with control values. Fluid accumulation was increased with N -nitro-l-arginine methyl ester after 30 minutes ( p < 0.01) and reduced with l-arginine after 120 minutes ( p < 0.01) compared with control animals. Water content was significantly decreased in the heart, lung, skin, muscle and peritoneum in rats receiving l-arginine. These data suggest that endogenous nitric oxide plays an important role in minimizing fluid accumulation during cardiopulmonary bypass. (J Thorac Cardiovasc Surg 1996;112:168-74

GPs’ prescription patterns, experience, and attitudes towards medicinal cannabis—a nationwide survey at the early stage of the Danish test scheme

Abstract Background On 1 January 2018 a four-year test scheme concerning use of medicinal cannabis (MC) was enacted. It has recently been extended for four more years by the Danish Parliament permitting all Danish physicians to prescribe MC to their patients. Previous studies have shown that general practitioners (GPs) have varying prescription experience, little knowledge, and mixed attitudes about MC. However, the present evidence is still limited, and no studies exist about Danish GPs’ prescription experience, knowledge, and attitudes towards MC. Therefore, our aim was to examine Danish GPs’ prescription experience, knowledge, and attitudes towards MC. Methods A national online survey-based study addressing Danish GPs was performed from September 2018 to July 2019. We performed separate multivariable logistic regression analyses including GPs’ prescription experience, knowledge, and attitudes towards MC as outcome variables. Results A total of 427 (38.4%) of 1112 GPs completed the questionnaire. Of these, 37 (8.7%) had experience in prescribing MC. The majority had little or no knowledge about MC (80.6%) as well as a negative view on prescription of MC (71.4%) to patients. Factors associated with prescribing MC to patients were: Single-handed practices (OR = 1.6, 95% CI 1.1;1.8) and perception of having quite some knowledge about MC (OR = 4.8, 95% CI 2.2;10.4). Factors associated with having quite some knowledge about MC were: having a positive attitude towards prescribing MC (OR = 5.2, 95% CI 1.9;14.0), being male (OR = 1.7, 95% CI 1.4;1.8), and being at least 60 years of age (OR = 2.8, 95% CI 1.3;6.0). Factors associated with having a positive attitude towards prescribing MC were: having quite some knowledge about MC (OR = 5.2, 95% CI 2.2;12.5) and GPs being male (OR = 1.7, 95% CI 1.1;1.9). Conclusion In this first study on prescription experience, knowledge, and attitudes about MC among Danish GPs, conducted one year after the Danish test scheme was enacted, we find a very low proportion of prescribers, little knowledge, and an overall negative attitude towards MC. Among the prescribing GPs, four in ten have little to no knowledge and a negative attitude towards MC. We stress that prescribing patterns, knowledge, and attitudes may change throughout the remaining time of the test scheme

System for automated geoscientific analyses (SAGA) v. 2.1.4

The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing

Developing and Validating a Lung Cancer Risk Prediction Model: A Nationwide Population-Based Study

Lung cancer can be challenging to diagnose in the early stages, where treatment options are optimal. We aimed to develop 1-year prediction models for the individual risk of incident lung cancer for all individuals aged 40 or above living in Denmark on 1 January 2017. The study was conducted using population-based registers on health and sociodemographics from 2007–2016. We applied backward selection on all variables by logistic regression to develop a risk model for lung cancer and applied the models to the validation cohort, calculated receiver-operating characteristic curves, and estimated the corresponding areas under the curve (AUC). In the populations without and with previously confirmed cancer, 4274/2,826,249 (0.15%) and 482/172,513 (0.3%) individuals received a lung cancer diagnosis in 2017, respectively. For both populations, older age was a relevant predictor, and the most complex models, containing variables related to diagnoses, medication, general practitioner, and specialist contacts, as well as baseline sociodemographic characteristics, had the highest AUC. These models achieved a positive predictive value (PPV) of 0.0127 (0.006) and a negative predictive value (NPV) of 0.989 (0.997) with a 1% cut-off in the population without (with) previous cancer. This corresponds to 1.2% of the screened population experiencing a positive prediction, of which 1.3% would be incident with lung cancer. We have developed and tested a prediction model with a reasonable potential to support clinicians and healthcare planners in identifying patients at risk of lung cancer

Can National Registries Contribute to Predict the Risk of Cancer? The Cancer Risk Assessment Model (CRAM)

Purpose: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. Methods: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007&ndash;2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). Results: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81&ndash;0.82) for men and 0.75 (95% CI 0.74&ndash;0.75) for women. Conclusion: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice