Genetic justification of severe COVID-19 using a rigorous algorithm

Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm

BACKGROUND

The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.

METHODS AND RESULTS

Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).

CONCLUSIONS

Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology

COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

Unraveling Links between Chronic Inflammation and Long COVID: Workshop Report

As COVID-19 continues, an increasing number of patients develop long COVID symptoms varying in severity that last for weeks, months, or longer. Symptoms commonly include lingering loss of smell and taste, hearing loss, extreme fatigue, and "brain fog." Still, persistent cardiovascular and respiratory problems, muscle weakness, and neurologic issues have also been documented. A major problem is the lack of clear guidelines for diagnosing long COVID. Although some studies suggest that long COVID is due to prolonged inflammation after SARS-CoV-2 infection, the underlying mechanisms remain unclear. The broad range of COVID-19's bodily effects and responses after initial viral infection are also poorly understood. This workshop brought together multidisciplinary experts to showcase and discuss the latest research on long COVID and chronic inflammation that might be associated with the persistent sequelae following COVID-19 infection.

Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection

The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.

Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors

We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.

Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19.

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson's Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

Complement and COVID-19: Three years on, what we know, what we don't know, and what we ought to know

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus was identified in China in 2019 as the causative agent of COVID-19, and quickly spread throughout the world, causing over 7 million deaths, of which 2 million occurred prior to the introduction of the first vaccine. In the following discussion, while recognising that complement is just one of many players in COVID-19, we focus on the relationship between complement and COVID-19 disease, with limited digression into directly-related areas such as the relationship between complement, kinin release, and coagulation. Prior to the 2019 COVID-19 outbreak, an important role for complement in coronavirus diseases had been established. Subsequently, multiple investigations of patients with COVID-19 confirmed that complement dysregulation is likely to be a major driver of disease pathology, in some, if not all, patients. These data fuelled evaluation of many complement-directed therapeutic agents in small patient cohorts, with claims of significant beneficial effect. As yet, these early results have not been reflected in larger clinical trials, posing questions such as who to treat, appropriate time to treat, duration of treatment, and optimal target for treatment. While significant control of the pandemic has been achieved through a global scientific and medical effort to comprehend the etiology of the disease, through extensive SARS-CoV-2 testing and quarantine measures, through vaccine development, and through improved therapy, possibly aided by attenuation of the dominant strains, it is not yet over. In this review, we summarise complement-relevant literature, emphasise its main conclusions, and formulate a hypothesis for complement involvement in COVID-19. Based on this we make suggestions as to how any future outbreak might be better managed in order to minimise impact on patients.

Targeted genotyping of COVID-19 patients reveals a signature of complement C3 and factor B coding SNPs associated with severe infection

We have attempted to explore further the involvement of complement components in the host COVID-19 (Coronavirus disease-19) immune responses by targeted genotyping of COVID-19 adult patients and analysis for missense coding Single Nucleotide Polymorphisms (coding SNPs) of genes encoding Alternative pathway (AP) components. We have identified a small group of common coding SNPs in Survivors and Deceased individuals, present in either relatively similar frequencies (CFH and CFI SNPs) or with stark differences in their relative abundance (C3 and CFB SNPs). In addition, we have identified several sporadic, potentially protective, coding SNPs of C3, CFB, CFD, CFH, CFHR1 and CFI in Survivors. No coding SNPs were detected for CD46 and CD55. Our demographic analysis indicated that the C3 rs1047286 or rs2230199 coding SNPs were present in 60 % of all the Deceased patients (n = 25) (the rs2230199 in 67 % of all Deceased Males) and in 31 % of all the Survivors (n = 105, p = 0.012) (the rs2230199 in 25 % of all Survivor Males). When we analysed these two major study groups using the presence of the C3 rs1047286 or rs2230199 SNPs as potential biomarkers, we noticed the complete absence of the protective CFB rs12614 and rs641153 coding SNPs from Deceased Males compared to Females (p = 0.0023). We propose that in these individuals, C3 carrying the R102G and CFB lacking the R32W or the R32Q amino acid substitutions, may contribute to enhanced association dynamics of the C3bBb AP pre-convertase complex assembly, thus enabling the exploitation of the activation of the Complement Alternative pathway (AP) by SARS-CoV-2.

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R²), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R² = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R² = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

Complement activation in COVID-19 and targeted therapeutic options: A scoping review

Increasing evidence suggests that activation of the complement system plays a key role in the pathogenesis and disease severity of Coronavirus disease 2019 (COVID-19). We used a systematic approach to create an overview of complement activation in COVID-19 based on histopathological, preclinical, multiomics, observational and clinical interventional studies. A total of 1801 articles from PubMed, EMBASE and Cochrane was screened of which 157 articles were included in this scoping review. Histopathological, preclinical, multiomics and observational studies showed apparent complement activation through all three complement pathways and a correlation with disease severity and mortality. The complement system was targeted at different levels in COVID-19, of which C5 and C5a inhibition seem most promising. Adequately powered, double blind RCTs are necessary in order to further investigate the effect of targeting the complement system in COVID-19.

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Genetic and immunologic evaluation of children with inborn errors of immunity and severe or critical COVID-19

BACKGROUND

Most severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals are asymptomatic or only exhibit mild disease. In about 10% of cases, the infection leads to hypoxemic pneumonia, although it is much more rare in children.

OBJECTIVE

We evaluated 31 young patients aged 0.5 to 19 years who had preexisting inborn errors of immunity (IEI) but lacked a molecular diagnosis and were later diagnosed with coronavirus disease 2019 (COVID-19) complications.

METHODS

Genetic evaluation by whole-exome sequencing was performed in all patients. SARS-CoV-2-specific antibodies, autoantibodies against type I IFN (IFN-I), and inflammatory factors in plasma were measured. We also reviewed COVID-19 disease severity/outcome in reported IEI patients.

RESULTS

A potential genetic cause of the IEI was identified in 28 patients (90.3%), including mutations that may affect IFN signaling, T- and B-cell function, the inflammasome, and the complement system. From tested patients 65.5% had detectable virus-specific antibodies, and 6.8% had autoantibodies neutralizing IFN-I. Five patients (16.1%) fulfilled the diagnostic criteria of multisystem inflammatory syndrome in children. Eleven patients (35.4%) died of COVID-19 complications. All together, at least 381 IEI children with COVID-19 have been reported in the literature to date. Although many patients with asymptomatic or mild disease may not have been reported, severe presentation of COVID-19 was observed in 23.6% of the published cases, and the mortality rate was 8.7%.

CONCLUSIONS

Young patients with preexisting IEI may have higher mortality than children without IEI when infected with SARS-CoV-2. Elucidating the genetic basis of IEI patients with severe/critical COVID-19 may help to develop better strategies for prevention and treatment of severe COVID-19 disease and complications in pediatric patients.

Complement-mediated microvascular injury and thrombosis in the pathogenesis of severe COVID-19: A review

Coronavirus disease 2019 (COVID-19) causes acute microvascular thrombosis in both venous and arterial structures which is highly associated with increased mortality. The mechanisms leading to thromboembolism are still under investigation. Current evidence suggests that excessive complement activation with severe amplification of the inflammatory response (cytokine storm) hastens disease progression and initiates complement-dependent cytotoxic tissue damage with resultant prothrombotic complications. The concept of thromboinflammation, involving overt inflammation and activation of the coagulation cascade causing thrombotic microangiopathy and end-organ damage, has emerged as one of the core components of COVID-19 pathogenesis. The complement system is a major mediator of the innate immune response and inflammation and thus an appealing treatment target. In this review, we discuss the role of complement in the development of thrombotic microangiopathy and summarize the current data on complement inhibitors as COVID-19 therapeutics.

Targeted Genotyping of MIS-C Patients Reveals a Potential Alternative Pathway Mediated Complement Dysregulation during COVID-19 Infection

Complement dysregulation has been documented in adults with COVID-19 and implicated in relevant pediatric inflammatory responses against SARS-CoV-2. We propose that signatures of complement missense coding SNPs associated with dysregulation could also be identified in children with multisystem inflammatory syndrome (MIS-C). We investigated 71 pediatric patients with RT-PCR validated SARS-CoV-2 hospitalized in pediatric COVID-19 care units (November 2020–March 2021) in three major groups. Seven (7) patients suffered from MIS-C (MIS-C group), 32 suffered from COVID-19 and were hospitalized (admitted group), whereas 32 suffered from COVID-19, but were sent home. All patients survived and were genotyped for variations in the C3, C5, CFB, CFD, CFH, CFHR1, CFI, CD46, CD55, MASP1, MASP2, MBL2, COLEC11, FCN1, and FCN3 genes. Upon evaluation of the missense coding SNP distribution patterns along the three study groups, we noticed similarities, but also considerably increased frequencies of the alternative pathway (AP) associated with SNPs rs12614 CFB, rs1061170, and rs1065489 CFH in the MIS-C patients. Our analysis suggests that the corresponding substitutions potentially reduce the C3b-inactivation efficiency and promote slower and weaker AP C3bBb pre-convertase assembly on virions. Under these circumstances, the complement AP opsonization capacity may be impaired, leading to compromised immune clearance and systemic inflammation in the MIS-C syndrome.

Genetic and Functional Evidence of Complement Dysregulation in Multiple Myeloma Patients with Carfilzomib-Induced Thrombotic Microangiopathy Compared to Controls

Background: Carfilzomib, an irreversible proteasome inhibitor approved for the treatment of relapsed/refractory Multiple Myeloma (MM) has been associated with Thrombotic Microangiopathy (TMA). Several pathogenetic mechanisms of carfilzomib-induced TMA have been proposed; however, recently, there has been a shift of focus on the potential contribution of complement dysregulation. Our aim was to explore whether patients with carfilzomib-induced TMA harbor germline variants of complement-related genes, which have been characterized as risk factors for TMA. Methods: We retrospectively recruited consecutive MM patients with carfilzomib-induced TMA and compared them to MM patients who received ≥4 cycles of carfilzomib and did not develop signs/symptoms of TMA, in a 1:2 ratio. Genomic DNA from peripheral blood was analyzed using next generation sequencing (NGS) with a complement-related gene panel; ADAMTS13 activity and soluble C5b-9 were measured using ELISA. Results: Complement-related variants were more common in patients with carfilzomib-induced TMA compared to non-TMA controls, regardless of patient and treatment characteristics; ADAMTS13 activity and C5b-9 were compatible with the phenotype of complement-related TMA. Conclusions: We confirmed the previous findings that implicated complement-related genes in the pathogenesis of carfilzomib-induced TMA. Most importantly, by incorporating a control group of non-TMA MM patients treated with carfilzomib-based regimens and functional complement assays, we enhanced the credibility of our findings.

Complement Levels at Admission Reflecting Progression to Severe Acute Kidney Injury (AKI) in Coronavirus Disease 2019 (COVID-19): A Multicenter Prospective Cohort Study

Background

Dysregulation of complement system is thought to be a major player in development of multi-organ damage and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). This study aimed to examine associations between complement system activity and development of severe acute kidney injury (AKI) among hospitalized COVID-19 patients.

Materials and Methods

In this multicenter, international study, complement as well as inflammatory and thrombotic parameters were analyzed in COVID-19 patients requiring hospitalization at one US and two Hungarian centers. The primary endpoint was development of severe AKI defined by KDIGO stage 2+3 criteria, while the secondary endpoint was need for renal replacement therapy (RRT). Complement markers with significant associations with endpoints were then correlated with a panel of inflammatory and thrombotic biomarkers and assessed for independent association with outcome measures using logistic regression.

Results

A total of 131 hospitalized COVID-19 patients (median age 66 [IQR, 54–75] years; 54.2% males) were enrolled, 33 from the US, and 98 from Hungary. There was a greater prevalence of complement over-activation and consumption in those who developed severe AKI and need for RRT during hospitalization. C3a/C3 ratio was increased in groups developing severe AKI (3.29 vs. 1.71; p < 0.001) and requiring RRT (3.42 vs. 1.79; p < 0.001) in each cohort. Decrease in alternative and classical pathway activity, and consumption of C4 below reference range, as well as elevation of complement activation marker C3a above the normal was more common in patients progressing to severe AKI. In the Hungarian cohort, each standard deviation increase in C3a (SD = 210.1) was independently associated with 89.7% increased odds of developing severe AKI (95% CI, 7.6–234.5%). Complement was extensively correlated with an array of inflammatory biomarkers and a prothrombotic state.

Conclusion

Consumption and dysregulation of complement system is associated with development of severe AKI in COVID-19 patients and could represent a promising therapeutic target for reducing thrombotic microangiopathy in SARS-CoV-2 infection.

Long-term outcomes of pediatric infections: from traditional infectious diseases to long covid

There is limited evidence available on the long-term impact of SARS-CoV-2 infection in children. In this article, the authors analyze the recent evidence on pediatric long covid and lessons learnt from a pediatric post-covid unit in Rome, Italy. To gain a better understanding of the concerns raised by parents and physicians in relation to the potential long-term consequences of this novel infection, it is important to recognize that long-term effect of a post-infectious disease is not a new phenomenon.

The authors analyze the recent evidence on pediatric long covid and lessons learnt from a pediatric post-covid unit in Rome, Italy. Also, we analyze the long-term effects of other infectious diseases.

An analysis of the recent evidence on pediatric long covid and lessons learnt from a pediatric post-covid unit in Rome, Italy.

Activation of Complement Components on Circulating Blood Monocytes From COVID-19 Patients

The coronavirus disease-2019 (COVID-19) caused by the SARS-CoV-2 virus may vary from asymptomatic to severe infection with multi-organ failure and death. Increased levels of circulating complement biomarkers have been implicated in COVID-19-related hyperinflammation and coagulopathy. We characterized systemic complement activation at a cellular level in 49-patients with COVID-19. We found increases of the classical complement sentinel C1q and the downstream C3 component on circulating blood monocytes from COVID-19 patients when compared to healthy controls (HCs). Interestingly, the cell surface-bound complement inhibitor CD55 was also upregulated in COVID-19 patient monocytes in comparison with HC cells. Monocyte membrane-bound C1q, C3 and CD55 levels were associated with plasma inflammatory markers such as CRP and serum amyloid A during acute infection. Membrane-bounds C1q and C3 remained elevated even after a short recovery period. These results highlight systemic monocyte-associated complement activation over a broad range of COVID-19 disease severities, with a compensatory upregulation of CD55. Further evaluation of complement and its interaction with myeloid cells at the membrane level could improve understanding of its role in COVID-19 pathogenesis.

Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease‐19 (COVID‐19). We aimed to a) identify complement‐related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement‐related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID‐19. Through targeted next‐generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH‐related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID‐19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID‐19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID‐19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.

COVID-19, Pre-Eclampsia, and Complement System

COVID-19 is characterized by virus-induced injury leading to multi-organ failure, together with inflammatory reaction, endothelial cell (EC) injury, and prothrombotic coagulopathy with thrombotic events. Complement system (C) via its cross-talk with the contact and coagulation systems contributes significantly to the severity and pathological consequences due to SARS-CoV-2 infection. These immunopathological mechanisms overlap in COVID-19 and pre-eclampsia (PE). Thus, mothers contracting SARS-CoV-2 infection during pregnancy are more vulnerable to developing PE. SARS-CoV-2 infection of ECs, via its receptor ACE2 and co-receptor TMPRSS2, can provoke endothelial dysfunction and disruption of vascular integrity, causing hyperinflammation and hypercoagulability. This is aggravated by bradykinin increase due to inhibition of ACE2 activity by the virus. C is important for the progression of normal pregnancy, and its dysregulation can impact in the form of PE-like syndrome as a consequence of SARS-CoV-2 infection. Thus, there is also an overlap between treatment regimens of COVID-19 and PE. C inhibitors, especially those targeting C3 or MASP-2, are exciting options for treating COVID-19 and consequent PE. In this review, we examine the role of C, contact and coagulation systems as well as endothelial hyperactivation with respect to SARS-CoV-2 infection during pregnancy and likely development of PE.

Pathogenic Basis of Thromboinflammation and Endothelial Injury in COVID-19: Current Findings and Therapeutic Implications

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic with a great impact on social and economic activities, as well as public health. In most patients, the symptoms of COVID-19 are a high-grade fever and a dry cough, and spontaneously resolve within ten days. However, in severe cases, COVID-19 leads to atypical bilateral interstitial pneumonia, acute respiratory distress syndrome, and systemic thromboembolism, resulting in multiple organ failure with high mortality and morbidity. SARS-CoV-2 has immune evasion mechanisms, including inhibition of interferon signaling and suppression of T cell and B cell responses. SARS-CoV-2 infection directly and indirectly causes dysregulated immune responses, platelet hyperactivation, and endothelial dysfunction, which interact with each other and are exacerbated by cardiovascular risk factors. In this review, we summarize current knowledge on the pathogenic basis of thromboinflammation and endothelial injury in COVID-19. We highlight the distinct contributions of dysregulated immune responses, platelet hyperactivation, and endothelial dysfunction to the pathogenesis of COVID-19. In addition, we discuss potential therapeutic strategies targeting these mechanisms.

Update on endothelial dysfunction in COVID-19: severe disease, long COVID-19 and pediatric characteristics

Objectives

To review current literature on the role of endothelial dysfunction in coronavirus disease-2019 (COVID-19) infection in terms of pathophysiology, laboratory features and markers, clinical phenotype in adults and children, as well as long COVID-19.

Content

We conducted a thorough assessment of the literature and critically analyzed current data, mostly utilizing the PubMed and Medline search engines to find original studies published in the previous decade.

Summary and Outlook

Accumulating evidence suggests that endothelial dysfunction may be a common denominator of severe COVID-19 in adults and children, as well as long COVID-19, implicating mutual pathophysiological pathways. This narrative review summarizes the up-to-date knowledge of endothelial dysfunction caused by COVID-19, including novel aspects of long COVID-19 and pediatric disease. This knowledge is important in order not only to understand the multisystemic attack of COVID-19, but also to improve patient management and prognosis.

Advancing therapeutic complement inhibition in hematologic diseases: PNH and beyond

Complement is an elaborate system of the innate immunity. Genetic variants and autoantibodies leading to excessive complement activation are implicated in a variety of human diseases. Among them, the hematologic disease paroxysmal nocturnal hemoglobinuria (PNH) remains the prototype model of complement activation and inhibition. Eculizumab, the first-in-class complement inhibitor, was approved for PNH in 2007. Addressing some of the unmet needs, a long-acting C5 inhibitor, ravulizumab, and a C3 inhibitor, pegcetacoplan have been also now approved with PNH. Novel agents, such as factor B and factor D inhibitors, are under study with very promising results. In this era of several approved targeted complement therapeutics, selection of the proper drug needs to be based on a personalized approach. Beyond PNH, complement inhibition has also shown efficacy and safety in cold agglutinin disease (CAD), primarily with the C1s inhibitor of the classical complement pathway, sutimlimab, but also with pegcetacoplan. Furthermore, C5 inhibition with eculizumab and ravulizumab, as well as inhibition of the lectin pathway with narsoplimab, are investigated in transplant-associated thrombotic microangiopathy (TA-TMA). With this revolution of next-generation complement therapeutics, additional hematologic entities, such as delayed hemolytic transfusion reaction (DHTR) or immune thrombocytopenia (ITP), might also benefit from complement inhibitors. Therefore, this review aims to describe state-of-the-art knowledge of targeting complement in hematologic diseases focusing on: a) complement biology for the clinician, b) complement activation and therapeutic inhibition in prototypical complement-mediated hematologic diseases, c) hematologic entities under investigation for complement inhibition, and d) other complement-related disorders of potential interest to hematologists.

Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

In this research, a new machine-learning approach was proposed to evaluate the effects of eight input parameters (surface area, relative compactness, wall area, overall height, roof area, orientation, glazing area distribution, and glazing area) on two output parameters, namely, heating load (HL) and cooling load (CL), of the residential buildings. The association strength of each input parameter with each output was systematically investigated using a variety of basic statistical analysis tools to identify the most effective and important input variables. Then, different combinations of data were designed using the intelligent systems, and the best combination was selected, which included the most optimal input data for the development of stacking models. After that, various machine learning models, i.e., XGBoost, random forest, classification and regression tree, and M5 tree model, were applied and developed to predict HL and CL values of the energy performance of buildings. The mentioned techniques were also used as base techniques in the forms of stacking models. As a result, the XGboost-based model achieved a higher accuracy level (HL: coefficient of determination, R2 = 0.998; CL: R2 = 0.971) with a lower system error (HL: root mean square error, RMSE = 0.461; CL: RMSE = 1.607) than the other developed models in predicting both HL and CL values. Using new stacking-based techniques, this research was able to provide alternative solutions for predicting HL and CL parameters with appropriate accuracy and runtime.

Complement activation and increased expression of Syk, mucin-1 and CaMK4 in kidneys of patients with COVID-19

Acute and chronic kidney failure is common in hospitalized patients with COVID-19, yet the mechanism of injury and predisposing factors remain poorly understood. We investigated the role of complement activation by determining the levels of deposited complement components (C1q, C3, FH, C5b-9) and immunoglobulin along with the expression levels of the injury-associated molecules spleen tyrosine kinase (Syk), mucin-1 (MUC1) and calcium/calmodulin-dependent protein kinase IV (CaMK4) in the kidney tissues of people who succumbed to COVID-19. We report increased deposition of C1q, C3, C5b-9, total immunoglobulin, and high expression levels of Syk, MUC1 and CaMK4 in the kidneys of COVID-19 patients. Our study provides strong rationale for the expansion of trials involving the use of inhibitors of these molecules, in particular C1q, C3, Syk, MUC1 and CaMK4 to treat patients with COVID-19.

Does ABO Blood Groups Affect Outcomes in Hospitalized COVID-19 Patients?

Background

Blood group type A has been associated with increased susceptibility for coronavirus disease 2019 (COVID-19) infection when compared to group O. The aim of our study was to examine outcomes in hospitalized COVID-19 patients among blood groups A and O.

Methods

This is an observational study. Kruskal-Wallis and Chi-square tests were used to compare continuous and categorical variables. Multivariable logistic regression models were used to examine association of blood groups with rates of mortality and severity of disease. All adult patients (> 18 years) admitted with COVID-19 infection between March 1, 2020 and March 10, 2021 at a large community hospital in Northeast Georgia were included. We compared mortality, severity of disease (use of mechanical ventilation, vasopressor, and acute renal failure), rates of venous thromboembolism and inflammatory markers between the blood groups. We used multivariable logistic regression model to adjust for demographical and clinical characteristics, use of COVID-19 medications and severity.

Results

A total of 3,563 of 5,204 admitted patients had information on blood groups. Of these, 1,301 (36.5%) were group A, 377 (10.6 %) were group B, 133 (3.7%) were group AB and 1,752 (49.2%) were group O. On adjusted analysis, there were no significant differences in rates of intensive care unit (ICU) admissions, mechanical ventilation, vasopressors, acute renal failure, venous thromboembolism and readmission rate between the blood groups A and O. In-hospital mortality was also not statistically different among the blood groups A and O (17.5% vs. 20.1%; P = 0.07). On adjusted analysis, in-hospital mortality was not lower in blood groups O (odds ratio (OR): 1.06; 95% confidence interval (CI): 0.80 - 1.40, P = 0.70).

Conclusions

Once hospitalized with COVID-19 infection, blood groups A and O are not associated with increased severity or in-hospital mortality.

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.