Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge to an incorrect structure. While only an initial proof-of-concept, the framework presented here could be an important first step toward genome-scale cell-free kinetic modeling of the biosynthetic capacity of industrially important organisms

Assessing taxonomic and functional change in British breeding bird assemblages over time

AIM: The aim was to identify the primary drivers of compositional change in breeding bird assemblages over a 40-year period. LOCATION: Britain. TIME PERIOD: From 1970 to 2010. MAJOR TAXA STUDIED: Birds. METHODS: Using morphological trait measurements and a dataset of presence-absence data for British breeding birds surveyed in 10 km × 10 km hectads during two time periods, we calculated temporal taxonomic and functional beta diversity for each hectad alongside the change in species richness, mean nearest taxon distance (MNTD) and mean pairwise distance (MPD). We also estimated potential drivers of beta diversity, including climatic and land-use and land-cover (LULC) change variables, elevation and assemblage species richness in 1970 (1970rich). We used random forest regressions to test which variables best explained compositional change in the assemblages. We also assessed spatial taxonomic and functional change by analysing multiple-site beta diversity and pairwise dissimilarities between time periods. RESULTS: Initial (1970) species richness was the most important predictor (highest importance score) across all models, with areas characterized by higher initial richness experiencing less assemblage change overall. The coordinates included to capture spatial autocorrelation in the data were also important predictors of change. Most cli-mate and LULC variables had relatively low explanatory power; elevation and average temperature were the most influential. All metrics increased slightly with increasing elevation, except for species richness change and MPD, which decreased. MAIN CONCLUSIONS: The composition of British breeding bird assemblages changed substantially between 1970 and 2010. Spatial heterogeneity increased, both taxonomically and functionally. We show evidence that hectads with larger assemblages have been buffered from temporal diversity change and that those at higher elevations changed more in composition than those at lower elevations. Overall, coarse-resolution climate and LULC explained only small to moderate amounts of variation, suggesting that stochastic assembly change or finer-scale drivers might be drivers of temporal changes in assemblage composition.British Trust for Ornithology (BTO).info:eu-repo/semantics/publishedVersio

Unravelling the complexities of biotic homogenization and heterogenization

1) Biotic homogenization is a process whereby species assemblages become more similar through time. The standard way of identifying the process of biotic homogenization is to look for decreases in spatial beta-diversity. However, using a single assemblage-level metric to assess homogenization can mask important changes in the occupancy patterns of individual species.2) Here, we analysed changes in the spatial beta-diversity patterns (i.e., biotic heterogenization or homogenization) of British bird assemblages within 30km x 30km regions between two periods (1988-1991 and 2008-2011). We partitioned the change in spatial beta-diversity into extirpation and colonisation resultant change (i.e., change in spatial beta-diversity within each region resulting from both extirpation and colonisation). We used measures of abiotic change in combination with Bayesian modelling to disentangle the drivers of biotic heterogenization and homogenization. 3) We detected both heterogenization and homogenization across the two time periods and three measures of diversity (taxonomic, phylogenetic, and functional). In addition, both extirpation and colonisation contributed to the observed changes, with heterogenization mainly driven by extirpation and homogenization by colonisation. These assemblage-level changes were primarily due to shifting occupancy patterns of generalist species. 4) Compared to habitat generalists, habitat specialists had significantly (i) higher average contributions to colonisation resultant change (indicating heterogenization within a region due to colonisation) and (ii) lower average contributions to extirpation resultant change (indicating homogenization from extirpation). Generalists showed the opposite pattern. 5) Increased extirpation resultant homogenization within regions was associated with increased urban land cover and decreased habitat diversity, precipitation, and temperature. Changes in extirpation resultant heterogenization and colonisation resultant heterogenization were associated with differences in elevation between regions and changes in temperature and land cover. 6) Many of the ‘winners’ (i.e., species that increased in occupancy) were species that had benefitted from conservation action (e.g., buzzard (Buteo buteo)). The ‘losers’ (i.e., those that decreased in occupancy) consisted primarily of previously common species, such as cuckoo (Cuculus canorus). 7) Our results show that focusing purely on changes in spatial beta-diversity over time may obscure important information about how changes in the occupancy patterns of individual species contribute to homogenization and heterogenization. <br/

Identifying the drivers of spatial taxonomic and functional beta-diversity of British breeding birds

Spatial variation in community composition may be driven by a variety of processes, including environmental filtering and dispersal limitation. While work has been conducted on the relative importance of these processes on various taxa and at varying resolutions, tests using high-resolution empirical data across large spatial extents are sparse. Here, we use a dataset on the presence/absence of breeding bird species collected at the 10 km × 10 km scale across the whole of Britain. Pairwise spatial taxonomic and functional beta diversity, and the constituent components of each (turnover and nestedness/richness loss or gain), were calculated alongside two other measures of functional change (mean nearest taxon distance and mean pairwise distance). Predictor variables included climate and land use measures, as well as a measure of elevation, human influence, and habitat diversity. Generalized dissimilarity modeling was used to analyze the contribution of each predictor variable to variation in the different beta diversity metrics. Overall, we found that there was a moderate and unique proportion of the variance explained by geographical distance per se, which could highlight the role of dispersal limitation in community dissimilarity. Climate, land use, and human influence all also contributed to the observed patterns, but a large proportion of the explained variance in beta diversity was shared between these variables and geographical distance. However, both taxonomic nestedness and functional nestedness were uniquely predicted by a combination of land use, human influence, elevation, and climate variables, indicating a key role for environmental filtering. These findings may have important conservation implications in the face of a warming climate and future land use change.info:eu-repo/semantics/publishedVersio

Assessing taxonomic and functional change in British breeding bird assemblages over time

Aim: The aim was to identify the primary drivers of compositional change in breeding bird assemblages over a 40-year period. Location: Britain. Time period: From 1970 to 2010. Major taxa studied: Birds. Methods: Using morphological trait measurements and a dataset of presence–absence data for British breeding birds surveyed in 10 km × 10 km hectads during two time periods, we calculated temporal taxonomic and functional beta diversity for each hectad alongside the change in species richness, mean nearest taxon distance (MNTD) and mean pairwise distance (MPD). We also estimated potential drivers of beta diversity, including climatic and land-use and land-cover (LULC) change variables, elevation and assemblage species richness in 1970 (1970rich). We used random forest regressions to test which variables best explained compositional change in the assemblages. We also assessed spatial taxonomic and functional change by analysing multiple-site beta diversity and pairwise dissimilarities between time periods. Results: Initial (1970) species richness was the most important predictor (highest importance score) across all models, with areas characterized by higher initial richness experiencing less assemblage change overall. The coordinates included to capture spatial autocorrelation in the data were also important predictors of change. Most climate and LULC variables had relatively low explanatory power; elevation and average temperature were the most influential. All metrics increased slightly with increasing elevation, except for species richness change and MPD, which decreased. Main conclusions: The composition of British breeding bird assemblages changed substantially between 1970 and 2010. Spatial heterogeneity increased, both taxonomically and functionally. We show evidence that hectads with larger assemblages have been buffered from temporal diversity change and that those at higher elevations changed more in composition than those at lower elevations. Overall, coarse-resolution climate and LULC explained only small to moderate amounts of variation, suggesting that stochastic assembly change or finer-scale drivers might be drivers of temporal changes in assemblage composition

Unravelling the complexities of biotic homogenization and heterogenization

1) Biotic homogenization is a process whereby species assemblages become more similar through time. The standard way of identifying the process of biotic homogenization is to look for decreases in spatial beta-diversity. However, using a single assemblage-level metric to assess homogenization can mask important changes in the occupancy patterns of individual species.2) Here, we analysed changes in the spatial beta-diversity patterns (i.e., biotic heterogenization or homogenization) of British bird assemblages within 30km x 30km regions between two periods (1988-1991 and 2008-2011). We partitioned the change in spatial beta-diversity into extirpation and colonisation resultant change (i.e., change in spatial beta-diversity within each region resulting from both extirpation and colonisation). We used measures of abiotic change in combination with Bayesian modelling to disentangle the drivers of biotic heterogenization and homogenization. 3) We detected both heterogenization and homogenization across the two time periods and three measures of diversity (taxonomic, phylogenetic, and functional). In addition, both extirpation and colonisation contributed to the observed changes, with heterogenization mainly driven by extirpation and homogenization by colonisation. These assemblage-level changes were primarily due to shifting occupancy patterns of generalist species. 4) Compared to habitat generalists, habitat specialists had significantly (i) higher average contributions to colonisation resultant change (indicating heterogenization within a region due to colonisation) and (ii) lower average contributions to extirpation resultant change (indicating homogenization from extirpation). Generalists showed the opposite pattern. 5) Increased extirpation resultant homogenization within regions was associated with increased urban land cover and decreased habitat diversity, precipitation, and temperature. Changes in extirpation resultant heterogenization and colonisation resultant heterogenization were associated with differences in elevation between regions and changes in temperature and land cover. 6) Many of the ‘winners’ (i.e., species that increased in occupancy) were species that had benefitted from conservation action (e.g., buzzard (Buteo buteo)). The ‘losers’ (i.e., those that decreased in occupancy) consisted primarily of previously common species, such as cuckoo (Cuculus canorus). 7) Our results show that focusing purely on changes in spatial beta-diversity over time may obscure important information about how changes in the occupancy patterns of individual species contribute to homogenization and heterogenization. <br/