Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Voxel based beta particle dosimetry methods in mice

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on August 14, 2007)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2006.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Radionuclide therapy cancer agents require dose information to determine efficacy in preliminary animal studies. Patient specific techniques for radionuclide transport simulation adapted from external beam dosimetry methodologies are developing currently. Dosimetry from Monte Carlo algorithms is an estimate of energy deposition based on source radionuclide and geometry configuration. These estimates result in organ specific radionuclide absorbed fractions [and S-factors] that are central to the dosimetry estimates from the MIRD schema. This work compares the results of a patient specific technique to stylized mathematical models in the literature for potential therapeutic beta emitters in mouse studies. Patient specific results have been obtained and validated for self-absorption against previous methods. These new results demonstrate progress in cross organ absorbed fractions by accurately modeling actual anatomical geometry. This particular adaptation has the potential of characterizing dose for combined radionuclide and external beam therapy.Includes bibliographical reference

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests

Antibacterial, anti-inflammatory and peroxidasemediated cyclooxygenase-1 inhibitory properties of Fusarium solani extract

Context: Nigerian soil fungi population is unexplored. It is hypothesized that they harbour new bioactive chemicals. This hypothesis is based on the large percentage of currently approved medicines that originated from soil-inhabiting micro-organisms Objectives: To investigate the antimicrobial and anti-inflammatory properties of Fusarium solani ethyl acetate (EtOAc) extract selected based on its broad spectrum of antimicrobial potential in an overlay experiment with seven other soil fungi strains. Materials and methods: Fungus number 6 (F6), identified by molecular characterization as Fusarium solani (Mart.) Sacc (Nectriaceae) was selected for studies from eight purified soil fungi due to its superior broad-spectrum antibiotics producing potential following agar overlay experiment. F6 was fermented for 21 d and the minimum inhibitory concentration (MIC) of its EtOAc fermentation extract (dose range: 12.5–100 µg/mL) was determined using agar dilution method for Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, Salmonella typhi and anti-inflammatory properties determined using rat-paw (250–500 mg/kg) and xylene induced oedema (250–500 µg/kg) (in Swiss albino rats and mice) models, respectively. The ability of the extract to inhibit cyclooxygenase (COX) enzyme was also determined in vitro using Cayman test kit-760111. Result: The MIC of the EtOAc extract was <12.5 µg/mL for S. aureus, P. aeruginosa and Escherichia coli. It inhibited xylene induced oedema by 65% compared with 61% observed for diclofenac and was significantly (p < 0.05) better than diclofenac in rat-paw-oedema model within the first phase of inflammation. The extract inhibited COX-1 peroxidase-mediated activities with an IC50 below 5 µg/mL. Conclusions: The extract exhibited strong antibacterial and anti-inflammatory properties, warranting further investigations into therapeutic potential of this fungus. This study design can be adapted in soil fungi metabolomic investigations. We report for the first time the potent anti-inflammatory property of the ethyl acetate extract of soil strain of F. solani with a possible mechanism of action that involves the inhibition of COX enzyme