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

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

The unconfined compressive strength (UCS) of intact rocks is crucial for engineering applications, but traditional laboratory testing is often impractical, especially for historic buildings lacking sufficient core samples. Non-destructive tests like the Schmidt hammer rebound number and compressional wave velocity offer solutions, but correlating these with UCS requires complex mathematical models. This paper introduces a novel approach using an artificial neural network (ANN) to simultaneously correlate UCS with three non-destructive test indexes: Schmidt hammer rebound number, compressional wave velocity, and open-effective porosity. The proposed ANN model outperforms existing methods, providing accurate UCS predictions for various rock types. Contour maps generated from the model offer practical tools for geotechnical and geological engineers, facilitating decision-making in the field and enhancing educational resources. This integrated approach promises to streamline UCS estimation, improving efficiency and accuracy in engineering assessments of intact rock materials.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

What is the role of complement in bystander hemolysis? Old concept, new insights

INTRODUCTION

Bystander hemolysis occurs when antigen-negative red blood cells (RBCs) are lysed by the complement system. Many clinical entities including passenger lymphocyte syndrome, hyperhemolysis following blood transfusion, and paroxysmal nocturnal hemoglobinuria are complicated by bystander hemolysis.

AREAS COVERED

The review provides data about the role of the complement system in the pathogenesis of bystander hemolysis. Moreover, future perspectives on the understanding and management of this syndrome are described.

EXPERT OPINION

Complement system can be activated via classical, alternative, and lectin pathways. Classical pathway activation is mediated by antigen-antibody (autoantibodies and alloantibodies against autologous RBCs, infectious agents) complexes. Alternative pathway initiation is triggered by heme, RBC microvesicles, and endothelial injury that is a result of intravascular hemolysis. Thus, C5b is formed, binds with C6-C9 compomers, and MAC (C5b-9) is formulated in bystander RBCs membranes, leading to cell lysis. Intravascular hemolysis, results in activation of the alternative pathway, establishing a vicious cycle between complement activation and bystander hemolysis. C5 inhibitors have been used effectively in patients with hyperhemolysis syndrome and other entities characterized by bystander hemolysis.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Association between loss of hypercoagulable phenotype, clinical features and complement pathway consumption in COVID-19

Background

Coronavirus disease 2019 (COVID-19) features a hypercoagulable state, but therapeutic anticoagulation effectiveness varies with disease severity. We aimed to evaluate the dynamics of the coagulation profile and its association with COVID-19 severity, outcomes, and biomarker trajectories.

Methods

This multicenter, prospective, observational study included patients with COVID-19 requiring respiratory support. Rotational thromboelastometry findings were evaluated for coagulation and fibrinolysis status. Hypercoagulable status was defined as supranormal range of maximum clot elasticity in an external pathway. Longitudinal laboratory parameters were collected to characterize the coagulation phenotype.

Results

Of 166 patients, 90 (54%) were severely ill at inclusion (invasive mechanical ventilation, 84; extracorporeal membrane oxygenation, 6). Higher maximum elasticity (P=0.02) and lower maximum lysis in the external pathway (P=0.03) were observed in severely ill patients compared with the corresponding values in patients on non-invasive oxygen supplementation. Hypercoagulability components correlated with platelet and fibrinogen levels. Hypercoagulable phenotype was associated with favorable outcomes in severely ill patients, while normocoagulable phenotype was not (median time to recovery, 15 days vs. 27 days, P=0.002), but no significant association was observed in moderately ill patients. In patients with severe COVID-19, lower initial C3, minimum C3, CH50, and greater changes in CH50 were associated with the normocoagulable phenotype. Changes in complement components correlated with dynamics of coagulation markers, hematocrit, and alveolar injury markers.

Conclusions

While hypercoagulable states become more evident with increasing severity of respiratory disease in patients with COVID-19, normocoagulable phenotype is associated with triggered by alternative pathway activation and poor outcomes.

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.

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.

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

INTRODUCTION

Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes.

METHODS

We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance.

RESULTS

Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators.

CONCLUSION

Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.

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.

Development of a population-level prediction model for intensive care unit (ICU) survivorship and mortality in older adults: A population-based cohort study

Background and Aims

Given the growing utilization of critical care services by an aging population, development of population-level risk models which predict intensive care unit (ICU) survivorship and mortality may offer advantages for researchers and health systems. Our objective was to develop a risk model for ICU survivorship and mortality among community dwelling older adults.

Methods

This was a population-based cohort study of 48,127 patients who were 50 years and older with at least one primary care visit between January 1, 2017, and December 31, 2017. We used electronic health record (EHR) data to identify variables predictive of ICU survivorship.

Results

ICU admission and mortality within 2 years after index primary care visit date were used to divide patients into three groups of "alive without ICU admission", "ICU survivors," and "death." Multinomial logistic regression was used to identify EHR predictive variables for the three patient outcomes. Cross-validation by randomly splitting the data into derivation and validation data sets (60:40 split) was used to identify predictor variables and validate model performance using area under the receiver operating characteristics (AUC) curve. In our overall sample, 92.2% of patients were alive without ICU admission, 6.2% were admitted to the ICU at least once and survived, and 1.6% died. Greater deciles of age over 50 years, diagnoses of chronic obstructive pulmonary disorder or chronic heart failure, and laboratory abnormalities in alkaline phosphatase, hematocrit, and albumin contributed highest risk score weights for mortality. Risk scores derived from the model discriminated between patients that died versus remained alive without ICU admission (AUC = 0.858), and between ICU survivors versus alive without ICU admission (AUC = 0.765).

Conclusion

Our risk scores provide a feasible and scalable tool for researchers and health systems to identify patient cohorts at increased risk for ICU admission and survivorship. Further studies are needed to prospectively validate the risk scores in other patient populations.

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Purpose

Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning.

Approach

The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not.

Results

Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images.

Conclusions

This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.

Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease

The development of new approaches allowing for the early assessment of COVID-19 cases that are likely to become critical and the discovery of new therapeutic targets are urgently required. In this prospective cohort study, we performed proteomic and laboratory profiling of plasma from 163 COVID-19 patients admitted to Bauru State Hospital (Brazil) between 4 May 2020 and 4 July 2020. Plasma samples were collected upon admission for routine laboratory analyses and shotgun quantitative label-free proteomics. Based on the course of the disease, the patients were divided into three groups: (a) mild (n = 76) and (b) severe (n = 56) symptoms, whose patients were discharged without or with admission to an intensive care unit (ICU), respectively, and (c) critical (n = 31), a group consisting of patients who died after admission to an ICU. Based on our data, potential therapies for COVID-19 should target proteins involved in inflammation, the immune response and complement system, and blood coagulation. Other proteins that could potentially be employed in therapies against COVID-19 but that so far have not been associated with the disease are CD5L, VDBP, A1BG, C4BPA, PGLYRP2, SERPINC1, and APOH. Targeting these proteins' pathways might constitute potential new therapies or biomarkers of prognosis of the disease.

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid's viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R² were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R² of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (-19.7 °C-70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids' dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

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.

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.

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 Variants and Protective Immunity against SARS-CoV-2

The novel coronavirus-19 (SARS-CoV-2), has infected numerous individuals worldwide, resulting in millions of fatalities. The pandemic spread with high mortality rates in multiple waves, leaving others with moderate to severe symptoms. Co-morbidity variables, including hypertension, diabetes, and immunosuppression, have exacerbated the severity of COVID-19. In addition, numerous efforts have been made to comprehend the pathogenic and host variables that contribute to COVID-19 susceptibility and pathogenesis. One of these endeavours is understanding the host genetic factors predisposing an individual to COVID-19. Genome-Wide Association Studies (GWAS) have demonstrated the host predisposition factors in different populations. These factors are involved in the appropriate immune response, their imbalance influences susceptibility or resistance to viral infection. This review investigated the host genetic components implicated at the various stages of viral pathogenesis, including viral entry, pathophysiological alterations, and immunological responses. In addition, the recent and most updated genetic variations associated with multiple host factors affecting COVID-19 pathogenesis are described in the study.

Efficacy of SARS-CoV-2 Vaccination in Dialysis Patients: Epidemiological Analysis and Evaluation of the Clinical Progress

This study investigated the impact of the fourth COVID-19 pandemic wave on dialysis patients of Romagna territory, assessing the associations of vaccination status with infection risk, clinical severity and mortality. From November 2021 to February 2022, an epidemiological search was conducted on 829 patients under dialysis treatment for at least one month. The data were then analyzed with reference to the general population of the same area. A temporal comparison was also carried out with the previous pandemic waves (from March 2020 to October 2021). The epidemiological evolution over time in the dialysis population and in Romagna citizens replicated the global trend, as the peak of the fourth wave corresponded to the time of maximum diffusion of omicron variant (B.1.1.529). Of 771 prevalent dialysis patients at the beginning of the study, 109 (14.1%) contracted SARS-CoV-2 infection during the 4-month observation period. Vaccine adherence in the dialysis population of the reference area was above 95%. Compared to fully or partially vaccinated subjects, the unvaccinated ones showed a significantly higher proportion of infections (12.5% vs. 27.0% p = 0.0341), a more frequent need for hospitalization (22.2% vs. 50.0%) and a 3.3-fold increased mortality risk. These findings confirm the effectiveness of COVID-19 vaccines in keeping infectious risk under control and ameliorating clinical outcomes in immunocompromised 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.

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.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.