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

Design and Characterization of Auxotrophy-Based Amino Acid Biosensors

<div><p>Efficient and inexpensive methods are required for the high-throughput quantification of amino acids in physiological fluids or microbial cell cultures. Here we develop an array of <em>Escherichia coli</em> biosensors to sensitively quantify eleven different amino acids. By using online databases, genes involved in amino acid biosynthesis were identified that – upon deletion – should render the corresponding mutant auxotrophic for one particular amino acid. This rational design strategy suggested genes involved in the biosynthesis of arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, threonine, tryptophan, and tyrosine as potential genetic targets. A detailed phenotypic characterization of the corresponding single-gene deletion mutants indeed confirmed that these strains could neither grow on a minimal medium lacking amino acids nor transform any other proteinogenic amino acid into the focal one. Site-specific integration of the <em>egfp</em> gene into the chromosome of each biosensor decreased the detection limit of the GFP-labeled cells by 30% relative to turbidometric measurements. Finally, using the biosensors to determine the amino acid concentration in the supernatants of two amino acid overproducing <em>E. coli</em> strains (i.e. <em>ΔhisL and ΔtdcC</em>) both turbidometrically and via GFP fluorescence emission and comparing the results to conventional HPLC measurements confirmed the utility of the developed biosensor system. Taken together, our study provides not only a genotypically and phenotypically well-characterized set of publicly available amino acid biosensors, but also demonstrates the feasibility of the rational design strategy used.</p> </div

Comparison of the single-gene deletions that were identified in this study to result in specific amino acid auxotrophies of <i>E. coli</i> with published information on predicted and empirically tested amino acid auxotrophies.

<p>1  =  Tepper & Shlomi (2011), 2  =  Baba (2006), 3  =  Li & Ricke (2003), ‘  =  strain shows weak growth.</p><p>The effect of the corresponding deletion is indicated as: <b>A</b> strongly reduced growth of mutant on minimal medium, <b>B</b> mutant does not grow on minimal medium, <b>C</b> mutant cannot transform any other amino acid into the focal one, and <b>ns</b> degree of auxotrophy not specified.</p

Confirmation of the biosensors' auxotrophy.

<p>The eleven biosensors were grown in minimal medium, which was supplemented with 3 mM of the required focal amino acid (<b>+</b>), devoid of any amino acid supplementation (<b>0</b>), or supplemented with 3 mM of each of the 19 other, proteinogenic amino acids (<b>++</b>). Growth of eight replicates was determined turbidometrically (OD_{600 nm}) after 18 h (histidine, arginine, tryptophan, isoleucine, methionine, phenylalanine, tyrosine, proline, and lysine) or 24 h (leucine and threonine) of cultivation. Boxplots: median (horizontal lines in boxes), interquartile range (boxes), 1.5-fold interquartile range (whiskers).</p

Bacterial strains and plasmids used in this study.

<p>Bacterial strains and plasmids used in this study.</p

Coefficients of determination (R²) of calibration curves generated from amino acid-dependent growth of the different amino acid biosensors tested.

<p>Biosensor growth was determined either turbidometrically (OD_{600 nm}) or by measuring GFP fluorescence emission (RFU).</p

Amino acid-dependent growth of the eleven GFP-labeled biosensors.

<p>Each biosensor was cultivated in minimal medium, to which increasing concentrations of the required focal amino acid have been added. Cell growth was measured either turbidometrically (OD_{600 nm}, ▪) or as relative fluorescence units (RFU, Δ) after 18 h (histidine, arginine, tryptophan, isoleucine, methionine, phenylalanine, tyrosine, proline and lysine) or 24 h (leucine and threonine) of cultivation. Means (±95% confidence interval) of four replicates are given.</p

Quantification of amino acid concentrations in the culture supernatant of two mutant <i>E. coli</i> strains.

<p>Mean (±95% confidence interval) amount of (A) histidine produced by <i>E. coli ΔhisL</i>, and (B) tryptophan produced by <i>E. coli ΔtrpcC</i> as determined by conventional HPLC measurements (black bar) or the growth of the respective biosensors, which was quantified turbidometrically (OD_{600 nm}, grey bar) or GFP fluorescence emission (white bar). Different letters indicate significant differences (S-N-K test: P<0.05, n = 4).</p

Data from: Metabolic cross-feeding via intercellular nanotubes among bacteria

Bacteria frequently exchange metabolites by diffusion through the extracellular environment, yet it remains generally unclear whether bacteria can also use cell–cell connections to directly exchange nutrients. Here we address this question by engineering cross-feeding interactions within and between Acinetobacter baylyi and Escherichia coli, in which two distant bacterial species reciprocally exchange essential amino acids. We establish that in a well-mixed environment E. coli, but likely not A. baylyi, can connect to other bacterial cells via membrane-derived nanotubes and use these to exchange cytoplasmic constituents. Intercellular connections are induced by auxotrophy-causing mutations and cease to establish when amino acids are externally supplied. Electron and fluorescence microscopy reveal a network of nanotubular structures that connects bacterial cells and enables an intercellular transfer of cytoplasmic materials. Together, our results demonstrate that bacteria can use nanotubes to exchange nutrients among connected cells and thus help to distribute metabolic functions within microbial communities