Correction: CT-Angiographic Aspects of Pulmonary Embolism on SARS COV-2.

[This corrects the article DOI: 10.5334/jbsr.3021.].

CT-Angiographic Aspects of Pulmonary Embolism on SARS COV-2.

Objectives: To study pulmonary embolism during COVID-19 pneumonia. Patients and Methods: This was a one-year retrospective and descriptive study of all patients from three imaging sites with SARS-CoV2 infection. Results: Two hundred and thirty-nine patients were included. The prevalence of pulmonary embolism was 18.4%. The average age was 55 years old. The sex ratio was 1.65. Dyspnea (58.6%), cough (56.1%), and chest pain (40.2%) were the most common reasons for consultation. In 151 patients (63.2%), chest computed tomography (CT) angiography was performed without checking level of D-dimer. The level of D-dimers was elevated in 47.8%. Grade 5 of CO-RADS accounted for 62.3%. In 70.5% of cases, the pulmonary embolism was bilateral with subsegmental involvement in 47.7%.Condensation in 'ground glass' with 'crazy paving' were the predominant typical parenchymal lesions with a frequency of 93.7% and 59.4%. In univariate analysis, D-dimers were significantly associated with the occurrence of pulmonary embolism (p < 0.001). Male sex was associated with a non-significantly higher Risk of having a pulmonary embolism (1.18 95% CI: 0.61-2.31, p = 0.622). The critical level increased the risk of pulmonary embolism in a non-significant way. Only the high level of D-dimers was and this, in a significant way. Conclusion: Pulmonary embolism was increased in the context of SARS-CoV2. The chest CT-angiography associated with the dosage of D-dimers constitutes a good diagnostic arsenal.

Studying the Effect of Glycosylation towards SARS-CoV-2 Spike RBD and ACE2 Interaction by Native Mass Spectrometry

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has serious consequences on global public health and social development. The binding of receptor binding domain (RBD) of spike protein to angiotensin converting enzyme 2 (ACE2) on the surface of SARS-CoV-2 host cell initiates the infection progress. Spike and ACE2 are both glycoproteins, the impact of glycosylation on protein structures and protein-protein interactions remains largely elusive. Characterizing the structural and dynamics of protein-protein binding progress will improve mechanism understanding of viral infection and facilitate targeted drug design. Structural mass spectrometry (MS) method is widely used in protein structural studies, providing complementary information to conventional biophysical methods, such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy (cryo-EM). Native mass spectrometry (native MS) is an emerging technology that enables the study of intact protein, non-covalent protein-protein, and protein-ligand complexes in their biological state, which can provide structural stability, binding stoichiometry, and spatial arrangement information. Here, native MS was used to examine the interaction between RBD and ACE2 as well as the impact of deglycosylation on the interaction stability of the RBD-ACE2 complex. The results revealed that both RBD and ACE2 are highly glycosylated, ACE2 presents as a dimer while RBD as a monomer, and they form a (RBD-ACE2)2 complex. The conditions of using PNGasc F to remove the N-glycan were optimized. At least two Oglycans including NcuAc(2) and GalNAcC 1) Gal( 1) NcuAc(2) or GlcNAcd ) Gal(l) NeuAc(2) were observed for the N-glycan removed RBD. Furthermore, the stability of the complexes formed by glycosylated and deglycosylated RBD with ACE2 was compared, and the results showed that the removal of N-glycan significantly drops the interaction stability of the RBD-ACE2 complex. Therefore, we recommend that glycosyla-tion should not be removed for structural and functional studies. Additional glycosyla-tion, structural and dynamics studies on Spike (including separated RBD) and ACE2 complexes would help us to understand the process of viral infection, advance drug design and vaccine developments. Nowadays, a comprehensive MS-based toolbox has been developed for the analysis of protein structure, function, and dynamics, including hydrogen-deuterium exchange MS (HDX-MS), native top-down (nTD) MS, cross-linking MS (XL-MS), and covalent labelling MS (CL-MS), etc. Through integrating structural MS methods, more detailed and comprehensive structural information about glycoproteins and their complexes will be uncovered. © 2022 Chinese Society for Mass Spectrometry. All rights reserved.

Studying the Effect of Glycosylation towards SARS-CoV-2 Spike RBD and ACE2 Interaction by Native Mass Spectrometry

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has serious consequences on global public health and social development. The binding of receptor binding domain (RBD) of spike protein to angiotensin converting enzyme 2 (ACE2) on the surface of SARS-CoV-2 host cell initiates the infection progress. Spike and ACE2 are both glycoproteins, the impact of glycosylation on protein structures and protein-protein interactions remains largely elusive. Characterizing the structural and dynamics of protein-protein binding progress will improve mechanism understanding of viral infection and facilitate targeted drug design. Structural mass spectrometry (MS) method is widely used in protein structural studies, providing complementary information to conventional biophysical methods, such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy (cryo-EM). Native mass spectrometry (native MS) is an emerging technology that enables the study of intact protein, non-covalent protein-protein, and protein-ligand complexes in their biological state, which can provide structural stability, binding stoichiometry, and spatial arrangement information. Here, native MS was used to examine the interaction between RBD and ACE2 as well as the impact of deglycosylation on the interaction stability of the RBD-ACE2 complex. The results revealed that both RBD and ACE2 are highly glycosylated, ACE2 presents as a dimer while RBD as a monomer, and they form a (RBD-ACE2)2 complex. The conditions of using PNGasc F to remove the N-glycan were optimized. At least two Oglycans including NcuAc(2) and GalNAcC 1) Gal( 1) NcuAc(2) or GlcNAcd ) Gal(l) NeuAc(2) were observed for the N-glycan removed RBD. Furthermore, the stability of the complexes formed by glycosylated and deglycosylated RBD with ACE2 was compared, and the results showed that the removal of N-glycan significantly drops the interaction stability of the RBD-ACE2 complex. Therefore, we recommend that glycosyla-tion should not be removed for structural and functional studies. Additional glycosyla-tion, structural and dynamics studies on Spike (including separated RBD) and ACE2 complexes would help us to understand the process of viral infection, advance drug design and vaccine developments. Nowadays, a comprehensive MS-based toolbox has been developed for the analysis of protein structure, function, and dynamics, including hydrogen-deuterium exchange MS (HDX-MS), native top-down (nTD) MS, cross-linking MS (XL-MS), and covalent labelling MS (CL-MS), etc. Through integrating structural MS methods, more detailed and comprehensive structural information about glycoproteins and their complexes will be uncovered. © 2022 Chinese Society for Mass Spectrometry. All rights reserved.

Statistics and Management COVID-19 in Kosovo-Case Study Hospital of Ferizaj

Infectious diseases are disorders caused by microorganisms such as bacteria, viruses, parasites or fungi. According to the World Health Organization, infectious diseases cause about 26% of the world's deaths and are the leading cause of death in people younger than 50 years old. Infectious diseases are classified into new infectious diseases, which include SARS-1, SARS-2, bird flu, etc., and recurrent diseases, which include pre-existing infections with known incidence and geographical spread. Coronaviruses are a family of viruses widely found in animals and humans, responsible for a variety of diseases, ranging from common cold to much more severe diseases which often lead to pneumonia. The group of new diseases also includes Coronavirus 2019 (COVID-19), which initially causes a respiratory infection with the continuation of other complications. This virus is a new type of beta-coronavirus, which first appeared in China and later spread very fast all over the world leading to a global pandemic. Disease monitoring should primarily include erythrocyte sedimentation rate, leukocyte count, leukocyte formula, C-reactive protein (CRP), determination of Troponin I (hsTnI) and T (cTnT), pro-B Type N-terminal natriuretic peptide (NT-proBNP), fibrinogen and D-dimer levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Statistical Processing of Relationship Between Biomarkers and Disease Severity Caused by COVID-19 Infection-Delta Variant

Severe infectious disease caused by acute respiratory syndrome, COVID-19 (SARS-CoV-2), spread rapidly worldwide, infecting several million people. According to scientific data, the disease develops through several different stages. After 2–4 days of infection and disease development, the lower respiratory tract is attacked and in a relatively short time interstitial pneumonia develops in a certain number of patients (with genetic predisposition between 5 and 10% of cases). Patients infected with COVID-19 have symptoms such as very high temperatures, fever, persistent cough, joint and bone pain, in some cases diarrhea, and loss of appetite and taste. Disease monitoring should primarily include erythrocyte sedimentation rate, leukocyte count, leukocyte count formula, C-reactive protein (CRP), determination of troponin I (hsTnI) and T (cTnT) levels, N-terminal pro-B natriuretic peptide (NT-proBNP), fibrinogen, and D-dimer level. Previous studies have shown that in pneumonia developed from chronic and acute obstructive pulmonary infections, high levels of D-dimer are observed in patients, and it is suggested that this parameter can be used as a specific prognostic biomarker, and the values higher than > 1000 ng/ml represent increased risk factors for mortality in patients with COVID-19. Because vascular thrombosis affects the promotion of an unfavorable clinical progression for the patient, the identification of early and accurate predictors of the worst outcome seems to be essential for timely and appropriate anticoagulant treatment in patients with SARS-CoV-2 infection. Overall, these data suggest that acute myocardial damage, or heart failure, may be an important indicator of disease severity and adverse prognosis in patients with COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Characteristics of Products of Fibrinogen Origin in the Presence of Anti- SARS-CoV-2 IgG in the Bloodstream.

BACKGROUND: The hemostasis system has been extensively investigated in patients in the acute phase of coronavirus disease 2019 (COVID-19). In contrast, the post-COVID syndrome is a poorly known entity, and there is a lack of information on the mechanisms underlying the hemostasis abnormalities in the post-COVID period. AIM: To analyze the potential changes in the parameters of the hemostasis system in the post- COVID period in the plasma of donors with different titers of anti-SARS-CoV-2 IgG. METHODS: The plasma from 160 donors who had recovered from COVID infection was used in the study. Based on the results of the Abbott SARS-CoV-2 IgG serological assay, all donors were divided into several groups: 5 ± 3 (n = 20); 55 ± 5 (n = 20); 65 ± 5 (n = 20); 75 ± 5 (n = 20); 85 ± 5 (n = 20); 95 ± 5 (n = 20); 125 ± 5 (n = 20); 175 ± 5 (n = 20) Index (S/C). A total of 20 healthy individuals without anti-SARS-CoV-2 IgG constituted the control group. Key laboratory parameters, such as fibrinogen concentrations, soluble fibrin monomer complex (SFMCs), and Ddimer, were investigated. In addition, the qualitative composition of the fraction of SFMCs was analyzed. RESULTS: The slight increase in the concentration of fibrinogen, SFMCs, and D-dimers in some donor groups have been found, which could cause the development of hemostasis disorders. In the fraction of SFMCs, the increase in the number of protein fragments with a molecular weight of less than 250 kDa and an increase in the level of proteins with a molecular weight of more than 270 kDa was revealed. CONCLUSION: The obtained results indicated the relationship between the changes in the parameters of the hemostasis system and the titers of anti-SARS-CoV-2 IgG in donors in the post-COVID period. It can be assumed that donors with higher titers of anti-SARS-CoV-2 IgG (>55 ± 5 Index (S/C)) are more prone to hemostasis abnormalities in the post-COVID period since a pronounced imbalance in the levels of SFMCs and D-dimer characterizes them. The appearance of protein fragments of different molecular weights in the fraction of SFMC points to uncontrolled activation of biochemical processes involving molecules of fibrinogenic origin. Additional studies are required to elucidate the role of anti-SARS-CoV-2 IgG in the post-COVID period.

The MaD-CLINYC score: An easy tool for the prediction of the outcome of hospitalized COVID-19 patients.

BACKGROUND: Most outcome-predictive models for COVID-19 patients use hospital admission data, offering a spontaneous mortality risk estimation. We aimed to elaborate on a tool that could be applied repeatedly, thus being more suitable for these patients' rapidly changing clinical course. METHODS: In this prospective study, we evaluated 560 samples derived from 156 patients hospitalized for COVID-19 in a single center. Age >61 years, male sex, comorbidities >2, need for intensive care unit admission, lactate dehydrogenase (LDH) >408 U/L, Neutrophil/Lymphocyte Ratio (NLR) >17, C-reactive protein (CRP) >10 mg/dl, and D-dimers >3,200 ng/ml were incorporated in an eight-scale score (MaD-CLINYC) after optimal scaling, ridge regression, and bootstrapping, which was documented to correlate with outcome independently of one or more samples analyzed, day from admission at sampling, and need for delivery. Validation process was performed over 574 samples derived from three centers. RESULTS: The developing and the validation cohort Area under Curve (AUC) was 0.90 (95 % Confidence Interval: 0.82-0.98) and 0.91 (0.88-0.94), respectively (p =0.822). A MaD-CLINYC score ≥4 had 75 % sensitivity and 81 % specificity to predict fatal outcome. CONCLUSIONS: MaD-CLINYC score is a powerful, feasible, easy-to-use, dynamic tool to assess the risk of the outcome, thus assisting clinicians in close monitoring and timely decisions in COVID-19 hospitalized patients. HIPPOKRATIA 2021, 25 (3):119-125.

Diagnostic Value of Hematological Parameters in COVID-19 Patients

Background: Covid-19 was fulminant and had a rapid spread in China and many other areas around the globe. This is a life threatening problem at present as it causes the severe acute respiratory syndrome corona virus (SARS-CoV). Aim(s): To explore the diagnostic value of hematological parameters in COVID-19 patients. Study Design: Comparative Cross-Sectional Study. Methodology: Patients (n=200) having COVID-19 were enrolled. All patients had CBC and inflammatory markers. Various hematological markers were used as prognostic markers. SPSS software, v 23 analyzed data. Independent t-test and Chi square were applied and p value of <0.05 was taken significant. Result(s): Mean age for patients having COVID was 47+/- 15.48 years. Mean values of hematological parameters and platelet count were significantly low among COVID patients when compared with non-COVID patients thus having significant difference. Practical Implication: This study highlighted simple, cost-effective hematological parameters that can be useful diagnostic tools for COVI-19. This study indicated that routine tests can guide towards disease like COVID-19. Conclusion(s): We concluded that hematological parameters (TLC, ANC, AMC, NLR and platelet count) play a vital role as diagnostic tool for COVID-19 patients. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

White blood count and inflammatory markers in the patients with COVID-19 infection and severity of illness

Objective: To assess whether hematological and inflammatory parameters can be useful in assessing severity of disease and predict mortality in patients with COVID-19. Study Design: Cross Sectional Comparative Retrospective. Setting: COVID ward and ICU, Central Park Teaching Hospital Lahore. Period: 23rd April 2021 to 23rd June 2021. Material & Methods: The study population was selected by convenient sampling and all patients admitted to the COVID ward and ICU during this time span of two months with positive COVID PCR were included in the study. Results: Among the study population 29(58%) were males while 21(42%) were females with male to female ratio of 1.38:1 and the difference was not found to be statistically significant. The mean age of the patients was 65.2±08 years. The complete blood parameterswhite blood count, absolute neutrophil count, lymphocyte count and neutrophil lymphocyte ratio were compared between groups and it was found that white blood count, absolute neutrophil count and neutrophil lymphocyte ratio was significantly different between different groups. Inflammatory markers like IL-6, ferritin, C-reative protein and d-dimers were also assessed in different severity groups and all were found to be significantly high in severe/critical group. Conclusion: It is concluded that simple inexpensive parameters like white blood count, neutrophil lymphocyte ratio and neutrophil count can be used to evaluate the severity of disease and to predict those patients who are at increased risk of mortality. In the similar way inflammatory markers like C-reactive protein, D-dimers, IL-6 and ferritin can be used to group the disease severity and also to monitor the disease. [ FROM AUTHOR]

Role of Hematological Parameters in Predicting COVID-19 Disease Severity

Background: Nowadays there is a global crises due to the emergence and spread of corona virus (CoV-19). This is a life threatening problem at present as it causes the severe acute respiratory syndrome corona virus (SARS-CoV). Aim(s): To explore the value of changes in routine hematological parameters for prediction of COVID-19 disease severity among admitted patients. Study design: Experimental study. Methodology: Patients (n=222) having COVID-19 were enrolled. They were divided into two groups depending on the severity of disease. They were admitted into ITC and non-ITC. All patients underwent CBC and inflammatory markers. Various hematological markers were used as prognostic markers. Independent t-test and Chi square were applied and p value of <0.05 was taken significant. Result(s): Mean age for ITC patients was 49.40+/-16.26 while the mean age for patients with mild disease was 40.88+/-15.48. NLR was significantly increased in ITC patients (p value<0.0001). Among biochemical parameters, serum ferritin, CRP and LDH were significantly increased in patients with severe disease (p value<0.001). D-Dimers were elevated in 68.75% patients of group-A and 17% patients in group-B with p-value<0.0001. Conclusion(s): We concluded that NLR and d-Dimers are the best hematology parameters in order to predict severity of disease. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Biomarker Changes in Pediatric Patients With COVID-19: A Retrospective Study from a Single Center Database.

BACKGROUND/AIM: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for generating a global pandemic with deadly consequences and life changes worldwide. With the appearance of the new variants of the virus, clinical manifestations have been reported in the pediatric population, some with severe evolution. The aim of this study was to identify the laboratory parameters necessary to establish an effective therapy. PATIENTS AND METHODS: In the period from August 2020 to September 2021, 234 pediatric patients met the inclusion criteria and were selected for the study. After confirming the COVID-19 diagnosis, laboratory parameters were analyzed and compared to the severity of the illness. RESULTS: Thrombocytopenia (p<0.001), leukocytosis (p<0.001), and lymphopenia (p<0.001) correlated with the severity of the disease. Also, D-dimer values were closely monitored due to the high association of this parameter with an unsatisfactory prognosis and a severe form of the disease. CONCLUSION: The D-dimer values and complete blood count are useful parameters in COVID-19 evaluation in children.

Using D-dimer as a Biomarker to Predict COVID-19 Disease Severity from Clinical Data of Hospitalized Patients: A Machine Learning Approach

Previous studies have documented an association of D-dimer levels with COVID-19 severity. Elevated D-dimer is reported to be associated with patient demographics, comorbidities, lab results, and overall higher incidence of critical illness. However, due to small sample sizes, limited availability of data on essential covariates, and lack of standardization of the admission laboratory protocol, the role of D-dimer in the progression of COVID-19 remains uncertain and needs further investigation using data from larger cohorts. The objectives of this study were to study the factors predicting elevated D-dimer level and to characterize the risk factors that predict D-dimer elevation over the course of inpatient admission. We used statistical modeling, applying machine learning methods to maximally leverage all the available clinical and care variables without being limited by the assumptions of traditional regression analysis methods. Our sample consisted of 1005 COVID-19 inpatients admitted to a large US hospital from March 2020 to July 2020, using detailed data on various clinical and biochemical laboratory test results at admission and throughout the course of hospital stay. Analytic methods used in this study included a) descriptive statistics at baseline using chi-square tests to compare patients with normal and elevated D-dimer at baseline, b) adjusted multivariable regression modeling, and c) evaluation of importance of each feature using two decision-tree-based supervised machine learning algorithms, random forest and XGBoost methods. Results show that machine learning methods could identify 20 important features that predict D-dimer some of which could be used to prevent the processes that lead to D-dimer elevation. Our study suggests that continual laboratory monitoring of D-dimer levels from the time of detection of COVID-19 infection, and monitoring of selected risk factors out of the panel of identified risk factors may enable clinicians to triage patients into risk levels, initiate appropriate therapeutic strategies, and tailor care management to each patient in order to minimize the morbidity and mortality of COVID-19. © 2022 IEEE.

D-Dimer Elevation Matters to Predict COVID-19 Severity: A Machine Learning Approach

Although previous studies using limited data have documented an association of D-dimer levels with COVID-19 severity, the role of D-dimer in the progression of COVID-19 remains unclear and requires further investigation using data from larger cohorts. We used traditional statistical modeling and machine learning methods to examine critical factors influencing the D-dimer elevation and to characterize associated risk factors of D-dimer elevation over the course of inpatient admission. We identified 20 important features to predict D-dimer levels, some of which could be used to predict and prevent the D-dimer elevation. Laboratory monitoring of D-dimer level and its risk factors at early stage can mitigate severe or death cases in COVID-19. © 2022 IEEE.

Unsupervised Machine Learning to Identify Convalescent COVID-19 Phenotypes

After the acute disease, post-COVID-19 patients may present several and persistent symptoms, known as the new paradigm of 'post-acute COVID-19 syndrome'. This necessitates a multidisciplinary rehabilitation that has been proposed but whose effectiveness is still to be assessed. In this study, convalescent COVID-19 patients undergoing pulmonary rehabilitation (PR) after reporting long-term symptoms were consecutively enrolled. Then, they were grouped by laboratory parameters at admission through an unsupervised Machine Learning (ML) approach. We aimed to identify potential indicators that could discriminate several phenotypes leading to a different responsiveness to the rehabilitation program. A k-means clustering method was performed;then, statistical analysis was employed to compare clinical and hematochemical parameters of the obtained clusters. The dataset consisted of 78 patients (84.8% males, mean age 60.72 years). The optimal number for clustering was boldsymbol{mathrm{k}=2} with a silhouette coefficient of 0.85, and D-Dimer resulted the most discriminating parameter, thus confirming its role as a marker of inflammation. The phenotypes exhibited statistically significant differences in terms of age boldsymbol{(mathrm{p}=0.007)}, packs of cigarettes per year boldsymbol{(mathrm{p}=0.003)}, uricemia boldsymbol{(mathrm{p}=0.010)}, PCR boldsymbol{(mathrm{p}=0.026)}, D-Dimer boldsymbol{(mathrm{p} < 0.001)}, red blood cells boldsymbol{(mathrm{p}=0.005)}, hemoglobin boldsymbol{(mathrm{p}=0.039)}, hematocrit boldsymbol{(mathrm{p}=0.026), text{PaO}_{2} (mathrm{p}=0.006)},boldsymbol{text{SpO}_{2} (mathrm{p}=0.011)}. Overall, our findings suggest the effectiveness of ML in identifying personalized prevention, interventional and rehabilitation strategies. © 2022 IEEE.

Brain-Derived Neurotrophic Factor Expression in Patients with Acute Pulmonary Embolism Compared to the General Population: Diagnostic and Prognostic Implications.

(1) Background: Pulmonary embolism (PE) is a severe condition, representing the third most important cardiovascular cause of death after myocardial infarction and stroke. Despite the use of clinical pre-test probability scores, D-dimer measuring, and computer tomography pulmonary angiography (CTPA), PE diagnosis remains a challenge. Brain-derived neurotrophic factor (BDNF) is the most important member of the neurotrophin family, which has also been shown to be involved in the physiopathology of cardiovascular conditions such as heart failure and myocardial infarction. In this study, we aimed to assess the BDNF expression in patients with acute PE compared to the general population, and to also investigate its diagnostic and prognostic role. (2) Methods: We conducted a single center prospective study, which included 90 patients with PE and 55 healthy volunteers. Clinical and paraclinical parameters, together with plasma levels of BDNF, were evaluated in all patients after admission. (3) Results: The plasma levels of BDNF were significantly lower in the PE patients compared with the control group (403 vs. 644 pg/mL, p < 0.001). ROC analysis revealed an AUC of 0.806 (95% CI 0.738-0.876, p < 0.001) and a cut-off value of 564 pg/mL, which associated a sensitivity of 74.4% and a specificity of 78.2% for PE. Low BDNF levels also correlated with prognostic markers of PE, such as PESI score (p = 0.023), NT-proBNP (p < 0.01), right ventricular diameter (p = 0.029), and tricuspid annular plane systolic elevation (p = 0.016). Moreover, we identified a decreased BDNF expression in patients with high-risk PE (p < 0.01), thrombolytic treatment (p = 0.01), and patients who died within 30 days (p = 0.05). (4) Conclusions: Our study revealed that plasma BNDF is significantly lower in patients with PE when compared with the general population, and may be considered as a promising biomarker in complementing the current diagnostic tools for PE. Furthermore, low levels of BDNF might also be used to predict a poor outcome of this condition.

Allosteric Inhibition of the SARS-CoV-2 Main Protease: Insights from Mass Spectrometry Based Assays

The SARS-CoV-2 main protease (Mpm) cleaves along the two viral polypeptides to release non-structural proteins required for viral replication. MPro is an attractive target for antiviral therapies to combat the coronavirus-2019 disease. Here, we used native mass spectrometry to characterize the functional unit of Mpro. Analysis of the monomer/dimer equilibria reveals a dissociation constant of Kd = 0.14± 0.03 pM, indicating MPro has a strong preference to dimerize in solution. We characterized substrate turnover rates by following temporal changes in the enzyme-substrate com- plexes, and screened small molecules, that bind distant from the active site, for their ability to modulate activity. These compounds, including one proposed to disrupt the dimer, slow the rate of substrate processing by ~35 %. This informa- tion, together with analysis of the x-ray crystal structures, provides a starting point for the development of more potent molecules that allosterically regulate MPro activity. . © 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Artificial intelligence in identifying COVID-19 patients based on inflammatory parameters

The virus SARS-Co V -2 that has caused a pandemic of COVID-19 in 2019 is still a major concern for health care systems. The reason for this is the fact that the outcome of the disease is difficult to predict, as deadly complications can occur in all people. Diagnosing COVID-19 relies on polymerase chain reaction (PCR) testing and antigen testing, both of which require special referral. The aim of this study was to develop artificial intelligence (AI) expert system which will facilitate COVID-19 diagnosis based on parameters that can be readily collected from blood specimens. The database contains 1000 samples, divided into 2 categories: (1) healthy and (2) sick subjects The following parameters were used: CRP, LDH, SE, AST, ALT, D-dimer and IL-6. The sensitivity of the developed system was 100%, specificity 98.33%, and accuracy 99.67%, on the basis of which we can conclude that the use of AI in the diagnosis of COVID19 has a significant potential. © 2022 IEEE.

Thrombotic events following tocilizumab therapy in critically ill COVID-19 patients: a Façade for prognostic markers.

BACKGROUND: Hospitals in the Middle East Gulf region have experienced an influx of COVID-19 patients to their medical wards and intensive care units. The hypercoagulability of these patients has been widely reported on a global scale. However, many of the experimental treatments used to manage the various complications of COVID-19 have not been widely studied in this context. The effect of the current treatment protocols on patients' diagnostic and prognostic biomarkers may thus impact the validity of the algorithms adopted. CASE PRESENTATION: In this case series, we report four cases of venous thromboembolism and 1 case of arterial thrombotic event, in patients treated with standard or intensified prophylactic doses of unfractionated heparin or low molecular weight heparin at our institution. Tocilizumab has been utilized as an add-on therapy to the standard of care to treat patients with SARS-CoV-2 associated acute respiratory distress syndrome, in order to dampen the hyperinflammatory response. It is imperative to be aware that this drug may be masking the inflammatory markers (e.g. IL6, CRP, fibrinogen, and ferritin), without reducing the risk of thrombotic events in this population, creating instead a façade of an improved prognostic outcome. However, the D-dimer levels remained prognostically reliable in these cases, as they were not affected by the drug and continued to be at the highest level until event occurrence. CONCLUSIONS: In the setting of tocilizumab therapy, traditional prognostic markers of worsening infection and inflammation, and thus potential risk of acute thrombosis, should be weighed carefully as they may not be reliable for prognosis and may create a façade of an improved prognostic outcome insteasd. Additionally, the fact that thrombotic events continued to be observed despite decrease in inflammatory markers and the proactive anticoagulative approach adopted, raises more questions about the coagulative mechanisms at play in COVID-19, and the appropriate management strategy.

Meta-Analysis and Systematic Review of Coagulation Disbalances in COVID-19: 41 Studies and 17,601 Patients.

Introduction: Coagulation parameters are important determinants for COVID-19 infection. We conducted meta-analysis to assess the association between early hemostatic parameters and infection severity. Methods: Electronic search was made for papers that addressed clinical characteristics of COVID-19 patients and disease severity. Results were filtered using exclusion and inclusion criteria and then pooled into a meta-analysis to estimate the standardized mean difference (SMD) with 95% confidence interval (CI) for D-dimers, fibrinogen, prothrombin time, platelet count (PLT), activated partial thromboplastin time. To explore the heterogeneity and robustness of our fundings, sensitivity and subgroup analyses were conducted. Publication bias was assessed with contour-enhanced funnel plots and Egger's test by linear regression. Coagulation parameters data from retrospective cohort study of 451 patients with COVID-19 at National Research Center for Cardiac Surgery were included in meta-analysis of published studies. Results: Overall, 41 original studies (17,601 patients) on SARS-CoV-2 were included. For the two groups of patients, stratified by severity, we identified that D-dimers, fibrinogen, activated partial thromboplastin time, and prothrombin time were significantly higher in the severe group [SMD 0.6985 with 95%CI (0.5155; 0.8815); SMD 0.661 with 95%CI (0.3387; 0.9833); SMD 0.2683 with 95%CI (0.1357; 0.4009); SMD 0.284 with 95%CI (0.1472; 0.4208)]. In contrast, PLT was significantly lower in patients with more severe cases of COVID-19 [SMD -0.1684 with 95%CI (-0.2826; -0.0542)]. Neither the analysis by the leave-one-out method nor the influence diagnostic have identified studies that solely cause significant change in the effect size estimates. Subgroup analysis showed no significant difference between articles originated from different countries but revealed that severity assessment criteria might have influence over estimated effect sizes for platelets and D-dimers. Contour-enhanced funnel plots and the Egger's test for D-dimers and fibrinogen revealed significant asymmetry that might be a sign of publication bias. Conclusions: The hemostatic laboratory parameters, with exception of platelets, are significantly elevated in patients with severe COVID-19. The two variables with strongest association to disease severity were D-dimers and fibrinogen levels. Future research should aim outside conventional coagulation tests and include analysis of clotting formation and platelet/platelet progenitors characteristics.