Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a multinational observational cohort study and ESICM Trials Group Project

Purpose: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). Methods: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. Results: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. Conclusion: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection

A bottom-up view of food surplus: using stable carbon and nitrogen isotope analysis to investigate agricultural strategies and diet at Bronze Age Archontiko and Thessaloniki Toumba, northern Greece

We use stable isotope analysis of crop, faunal and human remains to investigate agricultural strategies and diet at EBA-LBA Archontiko and MBA-LBA Thessaloniki Toumba. Crop production strategies varied between settlements, phases and species; flexibility is also apparent within the crop stores of individual houses. Escalating manuring intensity at LBA Thessaloniki Toumba coincides with large co-residential ‘blocks’ geared towards hoarding of agricultural surpluses, spectacularly preserved by fire at nearby LBA Assiros Toumba. Faunal isotope values reflect a range of feeding strategies, including probable herding of cattle on C4-rich coastal salt marshes, evident at Archontiko through to the LBA alongside bulk cockle harvesting. Palaeodietary analysis of LBA humans at Thessaloniki Toumba indicates that C3 crops represent the only plausible staples. Millet was a minor food but may have played a particular role in the sub-adult diet. Meat probably featured in supra-household food sharing and hospitality, associated with Mycenaean-style tableware in the LBA

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Centrality measures and H-bond clustering in proteins

Dataset includes : 1. MATLAB workflow to compute and plot centrality measures in protein structures, 2. Tcl script to visualize centrality measures in protein structures in VMD, 3. Matlab workflow to compute H-bond clusters in protein structures, 4. Tcl script to visualize clusters in protein structures in VMD, 5. Folder sample_folder contains output results after running the scripts in folders centrality_measures & hbond_clusters for SARS-CoV-2 spike glycoprotein in closed conformation (PDB ID: 6VXX). Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3. Guidelines for running the scripts are in README text file in the analysis_code folder. "When using these scripts, please cite: Karathanou, K., Lazaratos, M., Bertalan, É., Siemers, M., Buzar, K., Schertler, G.F., Del Val, C. and Bondar, A.N., 2020. A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2. Journal of structural biology, p.107617." ################################################################################################# Betweenness & Degree centrality measures: The Betweenness Centrality (BC) of a node ni gives the number of shortest-distance paths between any two other nodes nj and nk that pass via node ni divided by the total number of shortest paths that connect nj and nk irrespective of whether they pass via node ni. The normalized BC value of node ni is computed by dividing its BC by the number of pairs of nodes not including ni. The Degree Centrality (DC) of a node ni gives the number of edges of the node. The normalized DC value of node ni is computed by dividing its DC by the maximum possible edges to ni (which is N-1, where N is the number of nodes in the graph). References: Freeman LC: A set of measures of centrality based on betweenness. Sociometry 1977, 40:35-41. Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239. Brandes U: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 2001, 25:163-177. ################################################################################################# The Connected Component search gives a sub-graph of H bonds, in which at least two nodes are connected to each other by H-bond pathways and no other nodes are connected in the sub-graph. We denote those sub-graphs as H-bond clusters. The cluster size is given by the total number of nodes (H-bonding amino-acid residues) of each cluster. References: Cormen TH, Leiserson CE, Rivest RL, Sten C (2009). Introduction to algorithms, 3rd edn. Massachusetts Institute of Technolog

Graph-based algorithm for common topologies of dynamic lipid clusters

Dataset includes:1. Tcl scripts to compute H-bonds between lipids, H-bonds between lipid and water molecules (1-water bridges), interactions between lipids and ions from simulations.2. Matlab scripts to compute lipid H-bond clusters and detect types of cluster topologies using depth-first search (DFS) algorithm.3. Tcl script to visualize in VMD lipid H-bonds or ion interactions. Lipids are color-coded based on the topology type. Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3.Guidelines for running the scripts are in README text file in the topology_analysis folder."When using these scripts, please cite:Karathanou, K. and Bondar, A.N., 2022. Algorithm to catalogue topologies of dynamic lipid hydrogen-bond networks. Biochimica et Biophysica Acta (BBA)-Biomembranes, p.183859."###########################################################################################################DFS algorithm:To cluster lipid H-bond clusters and detect types of topologies, we perform Connected Component searches based on the Depth-First Search (DFS) algorithm. The DFS algorithm starts from an initial (source) node and performs exhaustive searches of all the nodes along the current path. When all nodes are visited, it moves backwards on the same path to find unvisited nodes. When all nodes of the current path are visited, the algorithm selects the next unexplored path. The computation is completed when the entire graph is explored. The Degree Centrality (DC) of a node ni gives the number of edges of the node.Algorithm computes three main types of topologies linear, star and circular and combinations thereof. All paths are catalogued according to their path length. For each lipid cluster found in the membrane and for each simulation time, the length of each path is defined as the longest number of edges between a start and an end node excluding short branches from star paths, and keep for the circular paths only the edge that connects the longest path to the end node.References:Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C., 2009. Introduction to Algorithms (3-rd edition). MIT Press and McGraw-Hill.Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239.V.K. Balakrishnan, Schaum's outline of theory and problems of graph theory, McGraw-Hill, 1997.J.L. Gross, J. Yellen, Graph theory and its applications, CRC Press, 1998.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Graph-based algorithm for common topologies of dynamic lipid clusters

Dataset includes:1. Tcl scripts to compute H-bonds between lipids, H-bonds between lipid and water molecules (1-water bridges), interactions between lipids and ions from simulations.2. Matlab scripts to compute lipid H-bond clusters and detect types of cluster topologies using depth-first search (DFS) algorithm.3. Tcl script to visualize in VMD lipid H-bonds or ion interactions. Lipids are color-coded based on the topology type. Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3.Guidelines for running the scripts are in README text file in the topology_analysis folder."When using these scripts, please cite:"###########################################################################################################DFS algorithm:To cluster lipid H-bond clusters and detect types of topologies, we perform Connected Component searches based on the Depth-First Search (DFS) algorithm. The DFS algorithm starts from an initial (source) node and performs exhaustive searches of all the nodes along the current path. When all nodes are visited, it moves backwards on the same path to find unvisited nodes. When all nodes of the current path are visited, the algorithm selects the next unexplored path. The computation is completed when the entire graph is explored. The Degree Centrality (DC) of a node ni gives the number of edges of the node.Algorithm computes three main types of topologies linear, star and circular and combinations thereof. All paths are catalogued according to their path length. For each lipid cluster found in the membrane and for each simulation time, the length of each path is defined as the longest number of edges between a start and an end node excluding short branches from star paths, and keep for the circular paths only the edge that connects the longest path to the end node.References:Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C., 2009. Introduction to Algorithms (3-rd edition). MIT Press and McGraw-Hill.Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239.V.K. Balakrishnan, Schaum's outline of theory and problems of graph theory, McGraw-Hill, 1997.J.L. Gross, J. Yellen, Graph theory and its applications, CRC Press, 1998.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Centrality measures and H-bond clustering in proteins

Dataset includes : 1. MATLAB workflow to compute and plot centrality measures in protein structures, 2. Tcl script to visualize centrality measures in protein structures in VMD, 3. Matlab workflow to compute H-bond clusters in protein structures, 4. Tcl script to visualize clusters in protein structures in VMD, 5. Folder sample_folder contains output results after running the scripts in folders centrality_measures & hbond_clusters for SARS-CoV-2 spike glycoprotein in closed conformation (PDB ID: 6VXX). Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3. Guidelines for running the scripts are in README text file in the analysis_code folder. "When using these scripts, please cite: Karathanou, K., Lazaratos, M., Bertalan, É., Siemers, M., Buzar, K., Schertler, G.F., Del Val, C. and Bondar, A.N., 2020. A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2. Journal of structural biology, p.107617." ################################################################################################# Betweenness & Degree centrality measures: The Betweenness Centrality (BC) of a node ni gives the number of shortest-distance paths between any two other nodes nj and nk that pass via node ni divided by the total number of shortest paths that connect nj and nk irrespective of whether they pass via node ni. The normalized BC value of node ni is computed by dividing its BC by the number of pairs of nodes not including ni. The Degree Centrality (DC) of a node ni gives the number of edges of the node. The normalized DC value of node ni is computed by dividing its DC by the maximum possible edges to ni (which is N-1, where N is the number of nodes in the graph). References: Freeman LC: A set of measures of centrality based on betweenness. Sociometry 1977, 40:35-41. Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239. Brandes U: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 2001, 25:163-177. ################################################################################################# The Connected Component search gives a sub-graph of H bonds, in which at least two nodes are connected to each other by H-bond pathways and no other nodes are connected in the sub-graph. We denote those sub-graphs as H-bond clusters. The cluster size is given by the total number of nodes (H-bonding amino-acid residues) of each cluster. References: Cormen TH, Leiserson CE, Rivest RL, Sten C (2009). Introduction to algorithms, 3rd edn. Massachusetts Institute of Technolog

NanoCrystal: A Web-Based Crystallographic Tool for the Construction of Nanoparticles Based on Their Crystal Habit

Modeling of nanoparticles is an essential first step to assess their capacities for different uses such as energy storage and drug delivery. However, creating an initial starting conformation for modeling and simulation is tedious because every crystalline material grows with a different crystal habit. In this application note, we describe NanoCrystal, a novel web-based crystallographic tool that creates nanoparticle models from any crystal structure guided by their preferred equilibrium shape under standard conditions according to the Wulff morphology (crystal habit). Users can upload a cif file, define the Miller indices and their corresponding minimum surface energies according to the Wulff construction of a particular crystal, and specify the size of the nanocrystal. As a result, the nanoparticle is constructed and visualized, and the coordinates of the atoms are output to the user. NanoCrystal can be accessed at http://nanocrystal.vi-seem.edu/. © 2018 American Chemical Society