Magnitude and determinants of excess total, age-specific and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project.

INTRODUCTION: To examine the impact of the COVID-19 pandemic on mortality, we estimated excess all-cause mortality in 24 countries for 2020 and 2021, overall and stratified by sex and age. METHODS: Total, age-specific and sex-specific weekly all-cause mortality was collected for 2015-2021 and excess mortality for 2020 and 2021 was calculated by comparing weekly 2020 and 2021 age-standardised mortality rates against expected mortality, estimated based on historical data (2015-2019), accounting for seasonality, and long-term and short-term trends. Age-specific weekly excess mortality was similarly calculated using crude mortality rates. The association of country and pandemic-related variables with excess mortality was investigated using simple and multilevel regression models. RESULTS: Excess cumulative mortality for both 2020 and 2021 was found in Austria, Brazil, Belgium, Cyprus, England and Wales, Estonia, France, Georgia, Greece, Israel, Italy, Kazakhstan, Mauritius, Northern Ireland, Norway, Peru, Poland, Slovenia, Spain, Sweden, Ukraine, and the USA. Australia and Denmark experienced excess mortality only in 2021. Mauritius demonstrated a statistically significant decrease in all-cause mortality during both years. Weekly incidence of COVID-19 was significantly positively associated with excess mortality for both years, but the positive association was attenuated in 2021 as percentage of the population fully vaccinated increased. Stringency index of control measures was positively and negatively associated with excess mortality in 2020 and 2021, respectively. CONCLUSION: This study provides evidence of substantial excess mortality in most countries investigated during the first 2 years of the pandemic and suggests that COVID-19 incidence, stringency of control measures and vaccination rates interacted in determining the magnitude of excess mortality.

Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.

Sepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters.

GeoWaVe: geometric median clustering with weighted voting for ensemble clustering of cytometry data.

MOTIVATION: Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorize cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different 'view' of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. RESULTS: We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in single-cell RNA sequencing analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualization and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions and therapeutic and diagnostic options. AVAILABILITY AND IMPLEMENTATION: GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster and published on the Python Package Index https://pypi.org/project/cytocluster. Benchmarking data described are available from https://doi.org/10.5281/zenodo.7134723. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Responses of Salmonella biofilms to oxidizing biocides: Evidence of spatial clustering.

The spatial organization of biofilm bacterial communities can be influenced by several factors, including growth conditions and challenge with antimicrobials. Differential survival of clusters of cells within biofilms has been observed. In this work, we present a variety of methods to identify, quantify and statistically analyse clusters of live cells from images of two Salmonella strains with differential biofilm forming capacity exposed to three oxidizing biocides. With a support vector machine approach, we showed spatial separation between the two strains, and, using statistical testing and high-performance computing (HPC), we determined conditions which possess an inherent cluster structure. Our results indicate that there is a relationship between biocide potency and inherent biofilm formation capacity with the tendency to select for spatial clusters of survivors. There was no relationship between positions of clusters of live or dead cells within stressed biofilms. This work identifies an approach to robustly quantify clusters of physiologically distinct cells within biofilms and suggests work to understand how clusters form and survive is needed. SIGNIFICANCE STATEMENT: Control of biofilm growth remains a major challenge and there is considerable uncertainty about how bacteria respond to disinfection within a biofilm and how clustering of cells impacts survival. We have developed a methodological approach to identify and statistically analyse clusters of surviving cells in biofilms after biocide challenge. This approach can be used to understand bacterial behaviour within biofilms under stress and is widely applicable.

Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation.

BACKGROUND: The role of specific blood tests to predict poor prognosis in patients admitted with infection from SARS-CoV-2 remains uncertain. During the first wave of the global pandemic, an extended laboratory testing panel was integrated into the local pathway to guide triage and healthcare resource utilisation for emergency admissions. We conducted a retrospective service evaluation to determine the utility of extended tests (D-dimer, ferritin, high-sensitivity troponin I, lactate dehydrogenase and procalcitonin) compared with the core panel (full blood count, urea and electrolytes, liver function tests and C reactive protein). METHODS: Clinical outcomes for adult patients with laboratory-confirmed COVID-19 admitted between 17 March and 30 June 2020 were extracted, alongside costs estimates for individual tests. Prognostic performance was assessed using multivariable logistic regression analysis with 28-day mortality used as the primary endpoint and a composite of 28-day intensive care escalation or mortality for secondary analysis. RESULTS: From 13 500 emergency attendances, we identified 391 unique adults admitted with COVID-19. Of these, 113 died (29%) and 151 (39%) reached the composite endpoint. 'Core' test variables adjusted for age, gender and index of deprivation had a prognostic area under the curve of 0.79 (95% CI 0.67 to 0.91) for mortality and 0.70 (95% CI 0.56 to 0.84) for the composite endpoint. Addition of 'extended' test components did not improve on this. CONCLUSION: Our findings suggest use of the extended laboratory testing panel to risk stratify community-acquired COVID-19 positive patients on admission adds limited prognostic value. We suggest laboratory requesting should be targeted to patients with specific clinical indications.

CytoPy: An autonomous cytometry analysis framework.

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.

Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach.

A major controversy in psychiatric genetics is whether nonadditive genetic interaction effects contribute to the risk of highly polygenic disorders. We applied a support vector machines (SVMs) approach, which is capable of building linear and nonlinear models using kernel methods, to classify cases from controls in a large schizophrenia case-control sample of 11,853 subjects (5,554 cases and 6,299 controls) and compared its prediction accuracy with the polygenic risk score (PRS) approach. We also investigated whether SVMs are a suitable approach to detecting nonlinear genetic effects, that is, interactions. We found that PRS provided more accurate case/control classification than either linear or nonlinear SVMs, and give a tentative explanation why PRS outperforms both multivariate regression and linear kernel SVMs. In addition, we observe that nonlinear kernel SVMs showed higher classification accuracy than linear SVMs when a large number of SNPs are entered into the model. We conclude that SVMs are a potential tool for assessing the presence of interactions, prior to searching for them explicitly.

Predictors of Attack Performance in High-Level Male Volleyball Players.

PURPOSE: In volleyball, teams win the majority of points through attacks (spikes), and therefore attack effectiveness (AE) is one of the most important predictors of victory. Traditionally, greater vertical jump heights (VJH) and higher spike speeds (SS) have been thought to increase AE; however, relevant research is limited. The authors' aim was to assess the relationship of VJH and SS with AE, as well as identifying possible associations of demographic and anthropometric factors, including common volleyball injuries, with VJH, SS, and AE. METHODS: A total of 22 male volleyball players from 2 teams in the top division of the Cypriot championship were included in the study. VJH was measured with the jump-and-reach test, SS was tested with a sports speed radar, and AE was calculated from performance reports of 4 matches between the 2 teams. RESULTS: Statistically significant results included positive correlations between VJH and SS, percentage lean mass and SS, body-bone percentage and SS, frequency of volleyball practice and SS, and frequency of resistance training and SS. AE was found to increase with increasing age, while SS over 90 km·h-1 appeared to have a negative effect on AE. History of pain in the dominant shoulder and in the ankles/knees was associated with lower SS and higher VJH, respectively. CONCLUSIONS: Based on the findings and the existing literature, volleyball players and coaches are advised to focus on maximization of VJH and optimization of attack technique; recommendations to improve attack success are provided.

Dominant vs. non-dominant shoulder morphology in volleyball players and associations with shoulder pain and spike speed.

The aims of our study were to compare the dominant (DOM) and non-dominant (NDOM) shoulders of high-level volleyball athletes and identify possible associations of shoulder adaptations with spike speed (SS) and shoulder pathology. A total of 22 male volleyball players from two teams participating in the first division of the Cypriot championship underwent clinical shoulder tests and simple measurements around their shoulder girdle joints bilaterally. SS was measured with the use of a sports speed radar. Compared with the NDOM side, the DOM scapula was more lateralised, the DOM dorsal capsule demonstrated greater laxity, the DOM dorsal muscles stretching ability was compromised, and the DOM pectoralis muscle was more lengthened. Players with present or past DOM shoulder pain demonstrated greater laxity in their DOM dorsal capsule, tightening of their DOM inferior capsule, and lower SS compared with those without shoulder pain. Dorsal capsule measurements bilaterally were significant predictors of SS. None of the shoulder measurements was associated with team roles or infraspinatus atrophy, while scapular lateralisation was more pronounced with increasing years of experience, and scapular antetilting was greater with increasing age. Adaptations of the DOM shoulder may be linked to pathology and performance. We describe simple shoulder measurements that may have the potential to predict chronic shoulder injury and become part of injury prevention programmes. Detailed biomechanical and large prospective studies are warranted to assess the validity of our findings and reach more definitive conclusions.

Direct current insulator-based dielectrophoretic characterization of erythrocytes: ABO-Rh human blood typing.

A microfluidic platform developed for quantifying the dependence of erythrocyte (red blood cell, RBC) responses by ABO-Rh blood type via direct current insulator dielectrophoresis (DC-iDEP) is presented. The PDMS DC-iDEP device utilized a 400 x 170 µm² rectangular insulating obstacle embedded in a 1.46-cm long, 200-µm wide inlet channel to create spatial non-uniformities in direct current (DC) electric field density realized by separation into four outlet channels. The DC-iDEP flow behaviors were investigated for all eight blood types (A+, A-, B+, B-, AB+, AB-, O+, O-) in the human ABO-Rh blood typing system. Three independent donors of each blood type, same donor reproducibility, different conductivity buffers (0.52-9.1 mS/cm), and DC electric fields (17.1-68.5 V/cm) were tested to investigate separation dependencies. The data analysis was conducted from image intensity profiles across inlet and outlet channels in the device. Individual channel fractions suggest that the dielectrophoretic force experienced by the cells is dependent on erythrocyte antigen expression. Two different statistical analysis methods were conducted to determine how distinguishable a single blood type was from the others. Results indicate that channel fraction distributions differ by ABO-Rh blood types suggesting that antigens present on the erythrocyte membrane polarize differently in DC-iDEP fields. Under optimized conductivity and field conditions, certain blind blood samples could be sorted with low misclassification rates.