Clinical evaluation of potential usefulness of serum lactate dehydrogenase (LDH) in 2019 novel coronavirus (COVID-19) pneumonia

Modulation in serum and hematological parameters as a prognostic indicator of COVID-19 infection in hypertension, diabetes mellitus, and different cardiovascular diseases

SARS-CoV-2 infection affects and modulates serum as well as hematological parameters. However, whether it modifies these parameters in the existing disease conditions, which help in the erection of specific treatments for the disease, is under investigation. Here, we aimed to determine whether serum and hematological parameters alteration in various diseases, diabetes mellitus (DM), hypertension (HTN), ischemic heart disease (IHD) and myocardial infarction (MI) conditions correlate and signal SARS-CoV-2 infection, which could be used as a rapid diagnosis tool for SARS-CoV-2 infection in disease conditions. To assess the projected goals, we collected blood samples of 1,113 male and female patients with solo and multiple disease conditions of DM/HTN/IHD/MI with severe COVID-19, followed by biochemical analysis, including COVID-19 virus detection by RT-qPCR. Furthermore, blood was collected from age-matched disease and healthy individuals 502 and 660 and considered as negative control. In our results, we examined higher levels of serum parameters, including D-dimer, ferritin, hs-CRP, and LDH, as well as hematological parameters, including TLC in sole and multiple diseases (DM/HTN/IHD/MI) conditions compared to the control subjects. Besides, the hematological parameters, including Hb, RBC, and platelet levels, decreased in the patients. In addition, we found declined levels of leukocyte count (%), lymphocyte (%), monocyte (%), and eosinophil (%), and elevated level of neutrophil levels (%) in all the disease patients infected with SARS-CoV-2. Besides, NLR and NMR ratios were also statistically significantly (p < 0.05) high in the patients with solo and multiple disease conditions of DM/HTN/IHD/MI infected with the SARS-CoV-2 virus. In conclusion, rapid alteration of sera and hematological parameters are associated with SARS-CoV-2 infections, which could help signal COVID-19 in respective disease patients. Moreover, our results may help to improve the clinical management for the rapid diagnosis of COVID-19 concurrent with respective diseases.

The relationship between lactate dehydrogenase to albumin ratio and all-cause mortality during ICU stays in patients with sepsis: A retrospective cohort study with propensity score matching

Background

Sepsis is a prevalent and severe medical condition which is frequently observed in the intensive care unit (ICU). Although numerous biomarkers have been identified to predict the prognosis of sepsis, the lactate dehydrogenase to albumin ratio (LDH/ALB ratio) has not been extensively investigated. The principal objective of this study is to assess the relationship between LDH/ALB ratio and all-cause mortality in patients with sepsis.

Methods

This study included all adult critically ill patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.0) database. Propensity score matching (PSM) analysis was conducted to mitigate bias, and Kaplan-Meier curves were performed to evaluate the cumulative survival across different groups. The association between the LDH/ALB ratio and mortality was examined through restricted cubic spline (RCS) analysis and Cox regression analysis. The robustness of the findings was confirmed through subgroup analyses. Additionally, the prognostic capability of the LDH/ALB ratio was further evaluated using receiver operating characteristic (ROC) curve analysis.

Results

There were 6059 adult patients with sepsis enrolled in the final analysis. RCS revealed a non-linear relationship between the LDH/ALB ratio and an increased risk of ICU all-cause mortality (χ2 = 46.900, P < 0.001). Following PSM analysis, 1553 matched pairs were obtained. As comparison to the low LDH/ALB ratio group, the mortality rate in the high LDH/ALB ratio group was significantly higher (P < 0.001). Kaplan-Meier curves, both before and after PSM, revealed that the ICU cumulative survival rate for patients with sepsis was significantly lower in the high LDH/ALB ratio group compared to the low LDH/ALB ratio group (χ2 = 93.360, P < 0.001; χ2 = 14.400, P < 0.001). Even after adjusting for a range of potential confounders, multivariate Cox regression analysis indicated that an elevated LDH/ALB ratio was a significant predictor of all-cause mortality in these patients. ROC curve analysis demonstrated that the LDH/ALB ratio had an area under the ROC curve (AUC) of 0.688 for predicting ICU mortality, with a sensitivity of 69.2% and a specificity of 58.6%.

Conclusions

An elevated LDH/ALB ratio (≥10.57) was associated with all-cause mortality in critically ill patients with sepsis, and it might serve as a prognostic marker. Clinicians should pay closer attention to sepsis patients presenting with an LDH/ALB ratio of 10.57 or higher.

Increased rates of indeterminate results in QuantiFERON-TB gold in-tube during the COVID-19 epidemic in Hunan province, China

Increased rates of indeterminate QuantiFERON-TB Gold In-Tube (QFT-GIT) results have been reported since the COVID-19 epidemic in Hunan Province, China. The indeterminate result (ITR) rate of QFT increased from an average of 5.2% to 12.4%, paralleling the first COVID-19 pandemic wave in the region. QFT-GIT results of 243 hospitalized patients with COVID-19 from January 2022 to April 2023 at Xiangya Hospital of Central South University were analyzed. Of the 243 patients, 71 (29.2%) had ITRs due to reduced interferon-gamma production in the positive control. Multiple factors are associated with ITRs, such as disease severity, respiratory failure incidence, immunosuppressant use, and prognosis. Additionally, interferon-gamma (Mitogen-Nil) levels differed significantly depending upon disease severity, prognosis, immunosuppressant use, sepsis symptoms, respiratory failure, or hyperlipidemia. An abnormal increase in the ITR rate in the QFT was observed after the COVID-19 pandemic, and an optimal machine learning predictive model for indeterminate QFT results was established.

Characterization of genetic and molecular tools for studying the endogenous expression of Lactate dehydrogenase in Drosophila melanogaster

Drosophila melanogaster larval development relies on a specialized metabolic state that utilizes carbohydrates and other dietary nutrients to promote rapid growth. One unique feature of the larval metabolic program is that Lactate Dehydrogenase (Ldh) activity is highly elevated during this growth phase when compared to other stages of the fly life cycle, indicating that Ldh serves a key role in promoting juvenile development. Previous studies of larval Ldh activity have largely focused on the function of this enzyme at the whole animal level, however, Ldh expression varies significantly among larval tissues, raising the question of how this enzyme promotes tissue-specific growth programs. Here we characterize two transgene reporters and an antibody that can be used to study Ldh expression in vivo. We find that all three tools produce similar Ldh expression patterns. Moreover, these reagents demonstrate that the larval Ldh expression pattern is complex, suggesting the purpose of this enzyme varies across cell types. Overall, our studies validate a series of genetic and molecular reagents that can be used to study glycolytic metabolism in the fly.

[The profile of serum inflammatory biomarkers in patients with SARS-CoV-2 infection: how well do they reflect the presence of pulmonary involvement?]

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with inflammatory and imaging alterations that vary depending on the disease severity.

OBJECTIVE

Monitoring changes in inflammatory biomarkers may offer insights into the extent of pulmonary alterations observed in chest-CT. This study aimed to evaluate the profile of different inflammatory biomarkers, widely available and routinely measured in COVID-19 patients, and to determine whether alterations in their activity at admission and discharge correlate with lung involvement assessed through CT scans.

METHODS

We conducted a retrospective observational study, wherein chest-CT scans were performed upon admission, and blood tests were conducted at admission and discharge. Treatment and monitoring adhered to national and international guidelines.

RESULTS

The profile of serum inflammatory markers (including values at admission and discharge, as well as their evolution during hospitalization) demonstrated a correlation with lung involvement as assessed by the total severity score. The high activity of serum inflammatory markers upon admission, accompanied by minimal changes during hospitalization, indicated a severe form of COVID-19 with notable lung involvement. While statistically significant differences were observed in C-reactive protein, fibrinogen, erythrocyte sedimentation rate, lactate dehydrogenase, and neutrophil-to-lymphocyte ratio, C-reactive protein emerged as the most reliable marker for assessing pulmonary involvement.

CONCLUSION

Changes in serum inflammatory markers during hospitalization exhibited a weak to moderate negative correlation with the severity of lung involvement. Orv Hetil. 2023; 164(41): 1607-1615.

Inflammasome activation by SARS-CoV-2 and its participation in COVID-19 exacerbation

COVID-19 is an infectious and inflammatory disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) that might progress to severe illness in humans, characterized by excessive pulmonary and systemic inflammation. Exacerbated production of inflammatory cytokines and cell death contributes to disease aggravation and the inflammasomes take a central stage in this process. Activation of the NLRP3 has been demonstrated in macrophages and monocytes infected in vitro, in mouse models of infection, and in cells and lungs of severe cases of COVID-19. It is still not clear how SARS-CoV-2 activates the NLRP3 inflammasome, and recent reports suggest that the virus engages the CASP4/11 (Caspase 4/11)-mediated noncanonical activation of NLRP3. In this review, we discuss the recent data regarding the activation of NLRP3 inflammasome by SARS-CoV-2 and their participation in the development of severe cases of COVID-19.

Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study

BACKGROUND

The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models.

METHODS

Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models' predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model's variables.

RESULTS

Through lasso regression, five features-hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)-were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810-1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings.

CONCLUSIONS

Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications.

Characterization of genetic and molecular tools for studying the endogenous expression of Lactate dehydrogenase in Drosophila melanogaster

Drosophila melanogaster larval development relies on a specialized metabolic state that utilizes carbohydrates and other dietary nutrients to promote rapid growth. One unique feature of the larval metabolic program is that Lactate Dehydrogenase (Ldh) activity is highly elevated during this growth phase when compared to other stages of the fly life cycle, indicating that Ldh serves a key role in promoting juvenile development. Previous studies of larval Ldh activity have largely focused on the function of this enzyme at the whole animal level, however, Ldh expression varies significantly among larval tissues, raising the question of how this enzyme promotes tissue-specific growth programs. Here we characterize two transgene reporters and an antibody that can be used to study Ldh expression in vivo . We find that all three tools produce similar Ldh expression patterns. Moreover, these reagents demonstrate that the larval Ldh expression pattern is complex, suggesting the purpose of this enzyme varies across cell types. Overall, our studies validate a series of genetic and molecular reagents that can be used to study glycolytic metabolism in the fly.

Photometric flow system for the determination of serum lactate dehydrogenase activity

The routine method for LDH (Lactate dehydrogenase) activity determination is to monitor the increase of NADH concentration at 340 nm. There are some inconvenience in taking measurements in the near-UV region, especially in the case of serum samples analysis. In this work, two modifications of the routine LDH activity assay based on the use of reducing properties of NADH have been compared. Both methods involved the reduction of compounds that can be easily determined by well-known methods, ferric ion (with ferrozine) and nitrotetrazolium blue (NBT). A fully-mechanized Multicommutated Flow Analysis-Paired Emitter Detector Diode (MCFA-PEDD) system based on solenoid devices was developed and applied for both methods. The linear ranges obtained for Fe-ferrozine and NBT methods are 6.0-200.0 U L^-1 and 10.0-250.0 U L^-1 with estimated detection limits at 0.2 U L^-1 and 4.5 U L^-1, respectively. The low LOQ values enabled 10-fold sample dilutions, which is advantageous for samples with limited available volume. The Fe-ferrozine method is more selective for LDH activity in the presence of glucose, ascorbic acid, albumin, bilirubin, copper and calcium ions than NBT method. To confirm the analytical usefulness of the proposed flow system, the analysis of real human serum samples was carried out. The statistic tests showed satisfactory correlation between the results obtained for both developed methods and those received using the reference method.

Susceptibility to SARS-CoV-2 and MERS-CoV in Beagle Dogs

The coronavirus disease 19 (COVID-19) pandemic, caused by the severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2), has resulted in unprecedented challenges to healthcare worldwide. In particular, the anthroponotic transmission of human coronaviruses has become a common concern among pet owners. Here, we experimentally inoculated beagle dogs with SARS-CoV-2 or Middle East respiratory syndrome (MERS-CoV) to compare their susceptibility to and the pathogenicity of these viruses. The dogs in this study exhibited weight loss and increased body temperatures and shed the viruses in their nasal secretions, feces, and urine. Pathologic changes were observed in the lungs of the dogs inoculated with SARS-CoV-2 or MERS-CoV. Additionally, clinical characteristics of SARS-CoV-2, such as increased lactate dehydrogenase levels, were identified in the current study.

Impact of extracorporeal membrane oxygenation in immunocompetent children with severe adenovirus pneumonia

BACKGROUND

Severe adenovirus (Adv.) pneumonia can cause significant mortality in young children. There has been no worldwide consensus on the impact of extracorporeal membrane oxygenation (ECMO) in immunocompetent children with severe Adv. pneumonia. This study aimed to assess the impact of ECMO in immunocompetent children with severe Adv. pneumonia.

METHODS

This study evaluated the medical records of 168 hospitalized children with severe Adv. pneumonia at the Guangzhou Women and Children's Medical Center between 2019 and 2020.Nineteen patients in the ECMO group and 149 patients in the non-ECMO group were enrolled.

RESULTS

Between these two groups, there were no differences in host factors such as sex, age (all P > 0.05). Significant differences were observed in shortness of breath/increased work of breathing; cyanosis; seizures; tachycardia; the partial pressure of oxygen in arterial blood (PO₂); the ratio of PaO₂ to the fraction concentration of oxygen in inspired air (FiO₂; P/F); white blood cell, lymphocyte, monocytes, lactate dehydrogenase (LDH), serum albumin, and procalcitonin levels; and, pulmonary consolidation (all P < 0.05). There were significant differences in the parameters of mechanical ventilation (MV) therapy and complications such as respiratory failure, acute respiratory distress syndrome, septic shock, length of hospitalization, and death (all P < 0.05). The maximum axillary temperatures, respiratory rates, heart rates and LDH levels after receiving ECMO were significantly lower than those before ECMO (all P < 0.05). Additionally, SPO₂, PO₂, and P/F were significantly higher than those before ECMO (all P < 0.05). In MV therapy, FiO₂, PIP, and PEEP were significantly lower than those before ECMO (all P < 0.05).

CONCLUSIONS

In our study, the clinical conditions of the patients in the ECMO group were much more severe than those in the non-ECMO group. Our study showed that ECMO might be beneficial for the patients with severe Adv. pneumonia.

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

INTRODUCTION

Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score.

METHODS

We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score.

RESULTS

Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively.

CONCLUSIONS

A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities.

Current Status and Future Perspectives of Lactate Dehydrogenase Detection and Medical Implications: A Review

The demand for glucose uptake and the accompanying enhanced glycolytic energy metabolism is one of the most important features of cancer cells. Unlike the aerobic metabolic pathway in normal cells, the large amount of pyruvate produced by the dramatic increase of glycolysis in cancer cells needs to be converted to lactate in the cytoplasm, which cannot be done without a large amount of lactate dehydrogenase (LDH). This explains why elevated serum LDH concentrations are usually seen in cancer patient populations. LDH not only correlates with clinical prognostic survival indicators, but also guides subsequent drug therapy. Besides their role in cancers, LDH is also a biomarker for malaria and other diseases. Therefore, it is urgent to develop methods for sensitive and convenient LDH detection. Here, this review systematically summarizes the clinical impact of lactate dehydrogenase detection and principles for LDH detection. The advantages as well as limitations of different detection methods and the future trends for LDH detection were also discussed.

Tumor Progression Locus 2 Protects against Acute Respiratory Distress Syndrome in Influenza A Virus-Infected Mice

Excessive inflammation in patients with severe influenza disease may lead to acute lung injury that results in acute respiratory distress syndrome (ARDS). ARDS is associated with alveolar damage and pulmonary edema that severely impair gas exchange, leading to hypoxia. With no existing FDA-approved treatment for ARDS, it is important to understand the factors that lead to virus-induced ARDS development to improve prevention, diagnosis, and treatment. We have previously shown that mice deficient in the serine-threonine mitogen-activated protein kinase, Tpl2 (MAP3K8 or COT), succumb to infection with a typically low-pathogenicity strain of influenza A virus (IAV; HKX31, H3N2 [x31]). The goal of the current study was to evaluate influenza A virus-infected Tpl2-/- mice clinically and histopathologically to gain insight into the disease mechanism. We hypothesized that Tpl2-/- mice succumb to IAV infection due to development of ARDS-like disease and pulmonary dysfunction. We observed prominent signs of alveolar septal necrosis, hyaline membranes, pleuritis, edema, and higher lactate dehydrogenase (LDH) levels in the lungs of IAV-infected Tpl2-/- mice compared to wild-type (WT) mice from 7 to 9 days postinfection (dpi). Notably, WT mice showed signs of regenerating epithelium, indicative of repair and recovery, that were reduced in Tpl2-/- mice. Furthermore, biomarkers associated with human ARDS cases were upregulated in Tpl2-/- mice at 7 dpi, demonstrating an ARDS-like phenotype in Tpl2-/- mice in response to IAV infection. IMPORTANCE This study demonstrates the protective role of the serine-threonine mitogen-activated protein kinase, Tpl2, in influenza virus pathogenesis and reveals that host Tpl2 deficiency is sufficient to convert a low-pathogenicity influenza A virus infection into severe influenza disease that resembles ARDS, both histopathologically and transcriptionally. The IAV-infected Tpl2-/- mouse thereby represents a novel murine model for studying ARDS-like disease that could improve our understanding of this aggressive disease and assist in the design of better diagnostics and treatments.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

A Review of Routine Laboratory Biomarkers for the Detection of Severe COVID-19 Disease

As the COVID-19 pandemic continues, there is an urgent need to identify clinical and laboratory predictors of disease severity and prognosis. Once the coronavirus enters the cell, it triggers additional events via different signaling pathways. Cellular and molecular deregulation evoked by coronavirus infection can manifest as changes in laboratory findings. Understanding the relationship between laboratory biomarkers and COVID-19 outcomes would help in developing a risk-stratified approach to the treatment of patients with this disease. The purpose of this review is to investigate the role of hematological (white blood cell (WBC), lymphocyte, and neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet, and red blood cell (RBC) count), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH)), and biochemical (Albumin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, D-dimer, total Cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL)) biomarkers in the pathogenesis of COVID-19 disease and how their levels vary according to disease severity.

Clinical Usefulness of Serum Lactate Dehydrogenase Levels in Mycoplasma pneumoniae Pneumonia in Children

OBJECTIVE

To elucidate the clinical usefulness of lactate dehydrogenase (LDH) levels in children with Mycoplasma pneumoniae (MP) pneumonia and identify cut-off levels in various clinical conditions.

METHODS

The study was retrospectively performed in 145 children who were hospitalized with MP pneumonia. Laboratory findings, including LDH levels at admission and clinical features were retrospectively reviewed from patients' electronic medical records.

RESULTS

The mean age of the study population was 5.9 y, and the mean value of LDH was 809.7 U/L. Poor response to treatment for MP pneumonia, respiratory virus coinfection, severe MP pneumonia, development of postinfectious bronchiolitis obliterans (PIBO) after MP pneumonia, pleural effusion, and oxygen need during illness were significantly associated with serum LDH levels. The cutoff levels of LDH for predicting poor response to treatment for MP pneumonia and respiratory virus coinfection were 1058 U/L [area under the curve (AUC), 0.729] and 803 U/L (AUC, 0.682), respectively. Those for pneumonic lesions involving at least one-third of the total lung volume, prediction of PIBO development, and extrapulmonary manifestations were 1098 U/L (AUC, 0.715), 676 U/L (AUC, 0.714), and 859 U/L (AUC, 0.710), respectively. The cutoff levels for pleural effusion during illness and for the prediction of oxygen need were 894 U/L (AUC, 0.699) and 1114 U/L (AUC, 0.771), respectively.

CONCLUSION

LDH levels are elevated in diverse clinical conditions in children with MP pneumonia and may be useful in the identification of severe clinical courses of MP pneumonia in children.

Correlation of Biomarkers in Severe COVID-19 Patients Cross-sectional Study

BACKGROUND: In patients with coronavirus disease-19 (COVID-19), serious biomarkers (liver function tests and renal functions tests) (urea, creatinine, GOT, GPT, and LDH) are determined immediate to assess in prognosis of the severity of disease. AIM: The aim of the study was to determine correlation between biomarkers (liver function tests and renal functions tests) (urea, creatinine, GOT, GPT, and LDH) among COVID-19 patients. METHODS: A cross-sectional study, a total of 90 COVID-19 patients who attending in the Al-Hussein Medical City in Karbala, Iraq, participated in the present study within a month’s time in late December 2021 to early January of 2022. All COVID-19 patients with positive SARS-COV-2 real-time RT-PCR results were reviewed. The patients were classifying according SPO2 into three groups (mild, moderate, and severe groups). The demographic data (sex, age, and SPO2) were collected while the biomarkers (liver function tests and renal functions tests) for all patients were done by bio-base instrument (ACCENT-200 ALAT KIT). RESULTS: The white blood cell “WBC” and neutrophil in moderate and severe groups had substantially greater counts (p = 0.005) when compared with mild group while lymphocytes were considerably decreased in the severe and moderate groups (p = 0.005). In the moderate group, there was positive significant correlation among neutrophils and serum LDH (r = 0.451*, p = 0.014). There was no significant correlation between neutrophils and liver function tests. Furthermore, in the moderate patient group, a strong positively correlating notably among lymphocytes and serum LDH. Moreover, the concentration of serum GOT, GPT, and LDH (p = 0.05, p = 0.08, and p = 0.5) was higher levels in severe group when compared to moderate and mild groups, on the other hand, the renal function tests (urea and creatinine) were high serum levels in severe group than mild and moderate groups. CONCLUSION: The serum concentration of urea, creatinine, GOPT, GPT, and LDH was high in severe COVID-19 patients group, although there was no statistically significant in ALP, GPT, and urea among COVID-19 patient’s groups (mild, moderate, and severe group). The present study found no significant correlation between biomarkers (liver function tests and renal function test).

Mortality Predictive Value of APACHE II and SOFA Scores in COVID-19 Patients in the Intensive Care Unit

Background. COVID-19 pandemic has become a global dilemma since December 2019. Are the standard scores, such as acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) score, accurate for predicting the mortality rate of COVID-19 or the need for new scores? We aimed to evaluate the mortality predictive value of APACHE II and SOFA scores in critically ill COVID-19 patients. Methods. In a cohort study, we enrolled 204 confirmed COVID-19 patients admitted to the intensive care units at the Imam Khomeini hospital complex. APACHE II on the first day and daily SOFA scoring were performed. The primary outcome was the mortality rate in the nonsurvived and survived groups, and the secondary outcome was organ dysfunction. Two groups of survived and nonsurvived patients were compared by the chi-square test for categorical variables and an independent sample t-test for continuous variables. We used logistic regression models to estimate the mortality risk of high APACHE II and SOFA scores. Result. Among 204 severe COVID-19 patients, 114 patients (55.9%) expired and 169 patients (82.8%) had at least one comorbidity that 103 (60.9%) of them did not survive ( $P=0.002$ ). Invasive mechanical ventilation and its duration were significantly different between survived and nonsurvived groups ( $P\le 0.001$ and $P=0.002$ , respectively). Mean APACHE II and mean SOFA scores were significantly higher in the nonsurvived than in the survived group (14.4 ± 5.7 vs. 9.5 ± 5.1, $P\le 0.001$ , 7.3 ± 3.1 vs. 3.1 ± 1.1, $P\le 0.001$ , respectively). The area under the curve was 89.5% for SOFA and 73% for the APACHE II score. Respiratory diseases and malignancy were risk factors for the mortality rate ( $P=0.004$ and $P=0.007$ , respectively) against diabetes and hypertension. Conclusion. The daily SOFA was a better mortality predictor than the APACHE II in critically ill COVID-19 patients. But they could not predict death with high accuracy. We need new scoring with consideration of the prognostic factors and daily evaluation of changes in clinical conditions.

The Clinical Risk Factors of Adenovirus Pneumonia in Children Based on the Logistic Regression Model: Correlation with Lactate Dehydrogenase

Methods

Children with bacterial pneumonia (41 cases) and adenovirus pneumonia (179 cases) hospitalized in Tianjin Children's Hospital from January to October 2020 were selected. The differences in baseline and clinical characteristics between children with two pneumonias, respectively, were compared via the chi-square test and Wilcox test. The Least Absolute Shrinkage and Selection Operator (LASSO) model was applied to screen the pneumonia type-related characteristics. Patients were randomly divided into the training set (n = 154) and test set (n = 66). The logistic model was constructed using the screened characteristics in the training set to predict whether the cases are bacterial pneumonia or adenovirus pneumonia. Finally, the model was validated by receiver operating characteristic (ROC) curve and area under curve (AUC) in the test set.

Results

The age (p < 0.001), hospital stay (p < 0.001), tonsil condition (p < 0.001), interleukin-6 (IL-6; p=0.033), and lactate dehydrogenase (LDH; p < 0.001) between children with bacterial pneumonia and adenovirus pneumonia were significantly different. Sex, tonsil condition, age, hospital stay, r-glutamyltransferase (r-GT), and LDH levels were the factors associated with the types of pneumonia. Compared with bacterial pneumonia, children with adenovirus pneumonia were younger (OR = 0.207, 95% CI: 0.041-0.475), with longer hospital stay (OR = 7.974, 95% CI: 2.626-74.354) and higher LDH expression level (OR = 1.025, 95% CI: 1.006-1.060). 92.4% types of pneumonia were correctly predicted, and the AUC value of the model was 0.981.

Conclusion

The LDH level was the associated factor to predict the types of pneumonia. Adenovirus pneumonia was associated with earlier age and longer hospital stay than bacterial pneumonia. The established model can well predict the types of pneumonia in children and provide clinical basis for guiding the individualized treatment of children.

Cardiovascular Dysfunction in COVID-19: Association Between Endothelial Cell Injury and Lactate

Coronavirus disease 2019 (COVID-19), an infectious respiratory disease propagated by a new virus known as Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has resulted in global healthcare crises. Emerging evidence from patients with COVID-19 suggests that endothelial cell damage plays a central role in COVID-19 pathogenesis and could be a major contributor to the severity and mortality of COVID-19. Like other infectious diseases, the pathogenesis of COVID-19 is closely associated with metabolic processes. Lactate, a potential biomarker in COVID-19, has recently been shown to mediate endothelial barrier dysfunction. In this review, we provide an overview of cardiovascular injuries and metabolic alterations caused by SARS-CoV-2 infection. We also propose that lactate plays a potential role in COVID-19-driven endothelial cell injury.

Fluorescence determination of lactate dehydrogenase activity based on silicon quantum dots

Silicon quantum dots (SiQDs) synthesized based on 3-aminopropyltrimethoxysilane (ATPMS) as silicon source were used to detect the activity of lactate dehydrogenase (LDH) through changes of fluorescence intensity of SiQDs. In this system, the fluorescence of SiQDs was first quenched by nicotinamide adenine dinucleotide (NADH), and then recovered with the addition of LDH, as NADH was consumed by catalytic reaction of LDH. A linear calibration chart of LDH is obtained in the range of 0.77-385 U/mL. The assay displays high selectivity towards LDH detection, and was successfully applied to the analysis of LDH in human serum samples. This assay has great prospects for the diagnosis and prognosis of various diseases, especially melanoma.

Tranilast as an Adjunctive Therapy in Hospitalized Patients with Severe COVID- 19: A Randomized Controlled Trial

Background

Tranilast is a potential NLRP3 inflammasome inhibitor that may relieve progressive inflammation due to COVID-19.

Aim of the study

To evaluate the therapeutic effects of Tranilast in combination with antiviral drugs in non-ICU-admitted hospitalized patients with COVID-19.

Methods

This study was an open-label clinical trial that included 72 hospitals admitted patients with severe COVID-19 at Razi Hospital, Ahvaz, Iran, from July 2020–August 2020. These patients were randomly assigned in a 1:1 ratio to control (30) and intervention groups (30). Patients in the control group received antiviral therapy, while patients in the intervention group received Tranilast (300 mg daily) in addition to the antiviral drugs for Seven days. The collected data, including the expression of inflammatory cytokine, laboratory tests, and clinical findings, was used for intragroup comparisons.

Results

The intervention group showed significantly lower levels of NLR (p = 0.001), q-CRP (p = 0.002), IL-1 (p = 0.001), TNF (p = 0.001), and LDH (p = 0.046) in comparison with the control group. The effect of intervention was significant in increasing the o2 saturation (F = 7.72, p = 0.007). Long hospitalization (four days or above) was 36.6% in the Tranilast and 66.6% in the control group (RR = 0.58; 95% CI: 0.38–1.06, p = 0.045). In the Tranilst and control groups, one and four deaths or hospitalization in ICU were observed respectively (RR = 0.31; 95% CI: 0.03–2.88, p = 0.20).

Conclusions

Tranilast might be used as an effective and safe adjuvant therapy and enhance the antiviral therapy's efficacy for managing patients with COVID-19.

Emerging Insights on Caspases in COVID-19 Pathogenesis, Sequelae, and Directed Therapies

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a significant global health emergency with new variants in some cases evading current therapies and approved vaccines. COVID-19 presents with a broad spectrum of acute and long-term manifestations. Severe COVID-19 is characterized by dysregulated cytokine release profile, dysfunctional immune responses, and hypercoagulation with a high risk of progression to multi-organ failure and death. Unraveling the fundamental immunological processes underlying the clinical manifestations of COVID-19 is vital for the identification and design of more effective therapeutic interventions for individuals at the highest risk of severe outcomes. Caspases are expressed in both immune and non-immune cells and mediate inflammation and cell death, including apoptosis and pyroptosis. Here we review accumulating evidence defining the importance of the expression and activity of caspase family members following SARS-CoV-2 infection and disease. Research suggests SARS-CoV-2 infection is linked to the function of multiple caspases, both mechanistically in vitro as well as in observational studies of individuals with severe COVID-19, which may further the impact on disease severity. We also highlight immunological mechanisms that occur in severe COVID-19 pathology upstream and downstream of activated caspase pathways, including innate recognition receptor signaling, inflammasomes, and other multiprotein complex assembly, inflammatory mediators IL-1β and IL-18, and apoptotic and pyroptotic cell death. Finally, we illuminate discriminate and indiscriminate caspase inhibitors that have been identified for clinical use that could emerge as potential therapeutic interventions that may benefit clinical efforts to prevent or ameliorate severe COVID-19.

Major coagulation disorders and parameters in COVID-19 patients

Coronavirus disease 2019 (COVID-19), with a high prevalence rate, has rapidly infected millions of people around the world. Since viral infections can disrupt the coagulation and homeostasis cascades, various inflammatory and coagulation problems occur due to COVID-19 infection, similar to coronavirus epidemics in 2003 and 2004. According to multiple previous studies, in the present research, we reviewed the most commonly reported problems of COVID-19 patients, such as venous thromboembolism, pulmonary embolism, disseminated intravascular coagulation, etc. and investigated the causes in these patients. Coagulation and inflammatory markers, such as platelets and fibrinogen, C-reactive protein, lactate dehydrogenase, d-dimer, prothrombin time, etc., were also discussed, and the treatment options were briefly reviewed. In addition to coagulation treatments, regular examination of coagulation parameters and thrombotic complications can be helpful in the timely treatment of patients. Therefore, it is helpful to review the coagulation problems in COVID-19 patients. Although all mentioned problems and markers are important in COVID-19, some of them are more valuable in terms of diagnosis and prognosis.

Role of Polypeptide Inflammatory Biomarkers in the Diagnosis and Monitoring of COVID-19

The COVID-19 (coronavirus disease 2019) pandemic that took over the world in December 2019 has had everlasting devastating impacts on the lives of people globally. It manifests a huge symptom spectrum ranging from asymptomatic to critically ill patients with an unpredictable outcome. Timely diagnosis and assessment of disease severity is imperative for effective treatment. Possibilities exist that by the time symptoms appear the viral load might increase beyond control. However, it is advisable to get adequately diagnosed as soon as the first symptom appears. There is an immediate requirement of reliable biomarkers of COVID-19 manifesting an early onset for effective clinical management, stratification of high risk patients and ensuring ideal resource allocation. In this review, we attempt to explore and describe important polypeptide inflammatory biomarkers, namely C-reactive protein, Procalcitonin, Ferritin, Lactate Dehydrogenase, Serum amyloid A, Interleukin-6, Tumor necrosis factor-alpha and LIGHT used in the detection and management of COVID-19. Viral pathogenesis and the role of these inflammatory biomarkers is highlighted, based on the evidences available till date. An integrative data monitoring along with their correlation with the natural disease progression is of utmost importance in the management of COVID-19. So further research and in-depth analysis of these biomarkers is warranted in the present scenario.

Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section

Background. COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a virus belonging to the Coronaviridae family. This disease has spread rapidly around the world and soon became an international public health emergency leading to an unpredicted pressure on the hospital emergency units. Early routine blood biomarkers could be key predicting factors of COVID-19 morbidity and mortality as suggested for C-reactive protein (CRP), IL-6, prothrombin and D-dimer. This study aims to identify other early routine blood biomarkers for COVID-19 severity prediction disclosed directly into the emergency section. Methods. Our research was conducted on 156 COVID-19 patients hospitalized at the Sapienza University Hospital “Policlinico Umberto I” of Rome, Italy, between March 2020 and April 2020 during the paroxysm’s initial phase of the pandemic. In this retrospective study, patients were divided into three groups according to their outcome: (1) emergency group (patients who entered the emergency room and were discharged shortly after because they did not show severe symptoms); (2) intensive care unit (ICU) group (patients who attended the ICU after admission to the emergency unit); (3) the deceased group (patients with a fatal outcome who attended the emergency and, afterward, the ICU units). Routine laboratory tests from medical records were collected when patients were admitted to the emergency unit. We focused on Aspartate transaminase (AST), Alanine transaminase (ALT), Lactate dehydrogenase (LDH), Creatine kinase (CK), Myoglobin (MGB), Ferritin, CRP, and D-dimer. Results. As expected, ANOVA data show an age morbidity increase in both ICU and deceased groups compared with the emergency group. A main effect of morbidity was revealed by ANOVA for all the analyzed parameters with an elevation between the emergency group and the deceased group. Furthermore, a significant increase in LDH, Ferritin, CRP, and D-dimer was also observed between the ICU group and the emergency group and between the deceased group and ICU group. Receiver operating characteristic (ROC) analyses confirmed and extended these findings. Conclusions. This study suggests that the contemporaneous presence of high levels of LDH, Ferritin, and as expected, CRP, and D-dimer could be considered as potential predictors of COVID-19 severity and death.

A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19

BACKGROUND

Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers.

METHODS

We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index.

RESULTS

Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9.

CONCLUSIONS

to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints.

A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers

The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77–0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84–0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84–0.86, and the NPV of 0.94, 95% CI: 0.92–0.96 for predicting in-hospital 30-day mortality.

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Algorithm using 9 laboratory markers & age may predict severity in patients with COVID-19

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Model was trained and tested on a multicenter sample of 10,937 patients

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Algorithm can predict ventilator use (NPV, 0.86) and mortality (NPV, 0.94)

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High NPV suggests utility as an adjunct to aid in triaging of patients with COVID-19

Chest CT manifestations with emphasis on the role of CT scoring and serum ferritin/lactate dehydrogenase in prognosis of coronavirus disease 2019 (COVID-19)

Background

In March 2020, the World Health Organization announced coronavirus disease 2019 (COVID-19) a pandemic, and because of the primary pulmonary manifestations of the disease, chest CT is essential in the evaluation of those patients. The aim of the study was to evaluate the role of chest CT findings and chest CT scoring along with serum ferritin and LDH in the prognosis of COVID-19 patients in a cohort of the Egyptian population.

Results

This retrospective study included 250 patients with positive RT-PCR for COVID-19, 138 males [55.2%] and 112 females [44.8%], age range 17–82 years with median 49.5. Two hundred patients had a positive significant correlation between age, serum ferritin, serum LDH, and CT score. Bilateral affection was 88% while unilaterality was 12%, and peripheral chest CT findings were stratified as follows: mild [score from 1 to 10], 114 patients [57%]; moderate [score from 11 to 19], 65 patients [32.5%]; and severe [score from 20 to 25], 21 patients [10.5%]. In severe cases, males constitute 85.7% while females were only 14.3%. Statistical and central distribution was 67%, peripheral was 31%, and central was 2%. Ground glass opacity (GGO) was the highest pattern 39.2%, consolidation 31.2%, fibrosis 15.2%, and CP 13.7%, with lymph nodes only 0.6%. Fifteen cases [6%] were critical; all showed severe scores ranging from 21 to 23 with three times increase in serum ferritin and four times increase in LDH. A follow-up study done to 8 cases [3.2%] showed an increase in CT scoring, serum ferritin, and serum LDH.

Conclusion

Chest CT findings are crucial for early diagnosis of COVID-19 disease especially for asymptomatic patients with old age and male sex considered risk factors for poor prognosis. Chest CT score, serum ferritin, and serum LDH help in predicting the short-term outcome of the patients aiming to decrease both morbidity and mortality.

Lactate dehydrogenase to albumin ratio as a prognostic factor in lower respiratory tract infection patients

PURPOSE

To verify the role of lactate dehydrogenase to albumin (LDH/ALB) ratio as an independent prognostic factor for mortality due to the lower respiratory tract infection (LRTI) in the emergency department (ED).

METHODS

We reviewed the electronic medical records of patients who were admitted to the ED for the management of LRTI between January 2018 and December 2020. Initial vital signs, laboratory data, and patient severity scores in the ED were collected. The LDH/ALB ratio was compared to other albumin-based ratios (blood urea nitrogen to albumin ratio, C-reactive protein to albumin ratio, and lactate to albumin ratio) and severity scales (pneumonia severity index, modified early warning score, CURB-65 scores), which are being used as prognostic factors for in-hospital mortality. Multivariable logistic regression was performed to identify independent risk factors.

RESULTS

The LDH/ALB ratio was higher in the non-survivor group than in the survivor group (median [interquartile range]: 217.6 [160.3;312.0] vs. 126.4 [100.3;165.1], p < 0.001). In the comparison of the area under the receiver operating characteristic curve (AUC) for predicting in-hospital mortality, the AUC of the LDH/ALB ratio (0.808, 95% confidence interval: 0.757-0.842, p < 0.001) was wider than other albumin-based ratios and severity scales, except the blood urea nitrogen to albumin ratio. In the multivariable logistic regression analysis, the LDH/ALB ratio independently affected in-hospital mortality.

CONCLUSION

The LDH/ALB ratio may serve as an independent prognostic factor for in-hospital mortality in patients with LRTI.

Inflammasome activation at the crux of severe COVID-19

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), results in life-threatening disease in a minority of patients, especially elderly people and those with co-morbidities such as obesity and diabetes. Severe disease is characterized by dysregulated cytokine release, pneumonia and acute lung injury, which can rapidly progress to acute respiratory distress syndrome, disseminated intravascular coagulation, multisystem failure and death. However, a mechanistic understanding of COVID-19 progression remains unclear. Here we review evidence that SARS-CoV-2 directly or indirectly activates inflammasomes, which are large multiprotein assemblies that are broadly responsive to pathogen-associated and stress-associated cellular insults, leading to secretion of the pleiotropic IL-1 family cytokines (IL-1β and IL-18), and pyroptosis, an inflammatory form of cell death. We further discuss potential mechanisms of inflammasome activation and clinical efforts currently under way to suppress inflammation to prevent or ameliorate severe COVID-19.

Lactate dehydrogenase to albumin ratio as a prognostic factor for patients with severe infection requiring intensive care

This study was performed to verify whether lactate dehydrogenase to albumin (LDH/ALB) ratio could be used as an independent prognostic factor in patients with severe infection requiring intensive care.We reviewed electronic medical records of patients hospitalized to the intensive care unit via the emergency department with a diagnosis of infection between January 2014 and December 2019. From the collected data, ALB-based ratios (LDH/ALB, blood urea nitrogen to albumin, C-reactive protein to albumin, and lactate to albumin ratios) and some severity scores (modified early warning score, mortality in emergency department sepsis score [MEDS], and Acute Physiology And Chronic Health Evaluation II [APACHE II] score) were calculated. LDH/ALB ratio for predicting the in-hospital mortality was compared with other ALB-based ratios and severity scales by univariable and receiver-operating characteristics curve analysis. Modified severity scores by LDH/ALB ratio and multivariable logistic regression were used to verify the independence and usefulness of the LDH/ALB ratio.The median LDH/ALB ratio was higher in non-survivors than survivors (166.9 [interquartile range: 127.2-233.1] vs 214.7 [interquartile range: 160.2-309.7], P < .001). The area under the receiver-operating characteristics curve of the LDH/ALB ratio (0.642, 95% confidence interval: 0.602-0.681, P < .001) was not lower than that of other ALB-based ratios and severity scores. From multivariable logistic regression, LDH/ALB ratio was independently associated with in-hospital mortality (odds ratio = 1.001, 95% confidence interval: 1.000-1.002, P = .047). Area under the receiver-operating characteristics curves of MEDS and APACHE II scores were improved by modification with LDH/ALB ratio (MEDS: 0.643 vs 0.680, P < .001; APACHE II score: 0.675 vs 0.700, P = .003).LDH/ALB ratio may be useful as the prognostic factor in patients with severe infection requiring intensive care.

Prospective selected biomarkers in COVID-19 diagnosis and treatment

COVID-19 has become a global health concern, due to the high transmissible nature of its causal agent and lack of proper treatment. Early diagnosis and nonspecific medical supports of the patients appeared to be effective strategy so far to combat the pandemic caused by COVID-19 outbreak. Biomarkers can play pivotal roles in timely and proper diagnosis of COVID-19 patients, as well as for distinguishing them from other pulmonary infections. Besides, biomarkers can help in reducing the rate of mortality and evaluating viral pathogenesis with disease prognosis. This article intends to provide a broader overview of the roles and uses of different biomarkers in the early diagnosis of COVID-19, as well as in the classification of COVID-19 patients into multiple risk groups.

Indeterminate QuantiFERON Gold Plus Results Reveal Deficient Interferon Gamma Responses in Severely Ill COVID-19 Patients

SARS-CoV-2 is a novel positive-sense single-stranded RNA virus that has caused a recent pandemic. Most patients have a mild disease course, while approximately 20% have moderate to severe disease, often requiring hospitalization and, in some cases, care in the intensive care unit. By investigating a perceived increased rate of indeterminate QuantiFERON-TB Gold Plus results in hospitalized COVID patients, we demonstrate that severely ill COVID-19 patients have at least a 6-fold reduction of interferon gamma (IFN-γ) levels compared to control patients. What is more, over 60% of these severely ill COVID-19 patients' peripheral T cells were found to be unable to produce measurable IFN-γ when stimulated with phytohemagglutinin (PHA), a potent IFN-γ mitogen, reflected by an indeterminate QuantiFERON-TB Gold Plus result. This defect of IFN-γ production was independent of absolute lymphocyte counts and immunosuppressive therapy. It was associated with increased levels of interleukin-6 (IL-6), which was a predictor of patient outcomes for our cohort when measured early in the course of disease. Finally, in a subset of COVID-19 patients, we found elevated IL-10 levels in addition to IL-6 elevation. In addition to finding a significant limitation of interferon-gamma release assay (IGRA) testing in severely ill COVID-19 patients, these data provide evidence that many of these patients demonstrate a focused Th2 immune response with inhibition of IFN-γ signaling and, in many cases, significant elevations of IL-6.

Kidney allograft infarction associated with transplant renal artery stenosis in a COVID-19 kidney transplant recipient

Kidney allograft infarction is rare, but an urgent condition that requires prompt intervention to avoid allograft loss. Renal artery thrombosis is the leading cause of infarction. Apart from traditional risk factors for thrombosis, emerging SARS-CoV-2 predisposes patients to thrombotic diseases both in arterial and venous vasculatures. We report a case of kidney transplant recipient with known transplant renal artery stenosis (TRAS) status post angioplasty with severe COVID-19, complicated by oliguric acute kidney injury requiring continuous renal replacement therapy (CRRT). She did not have a history of thromboembolic disease. The hospital course was complicated by new-onset atrial and ventricular fibrillation and cardiac arrest requiring multiple rounds of cardiopulmonary resuscitation. She had no signs of renal recovery, and an abdominal CT scan showed evidence of allograft infarcts. She underwent an allograft nephrectomy. Pathology revealed diffuse thrombotic microangiopathy involving glomeruli, arterioles, and arteries associated with diffuse cortical infarction with negative SARS-CoV-2 immunostain and in situ hybridization. This is the first case of kidney allograft infarct with a history of TRAS in a COVID-19 patient. Underlying TRAS and COVID-19-associated thrombosis in this patient are unique and likely play a key role in allograft infarction from arterial thrombosis. Recognizing risk factors and early therapy for allograft infarction may improve transplant outcomes.

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study

Background:

In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.

Methods:

We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using

An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.

Results:

We observed that

Conclusions:

Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis

The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.

Roles of certain biochemical and hematological parameters in predicting mortality and ICU admission in COVID-19 patients

OBJECTIVE

In this study, we aimed to retrospectively analyze the roles of certain hematological and biochemical parameters in predicting mortality and intensive care unit admission in patients diagnosed with coronavirus disease 2019 (COVID-19).

METHODS

We analyzed the complete blood count and biochemical parameters of 186 COVID-19 patients by using the polymerase chain reaction test. Whether these parameters can be used to predict intensive care unit admission and mortality in the COVID-19 patients was investigated.

RESULTS

The complete blood count and biochemical parameters of COVID-19 patients and in those admitted to intensive care unit were compared. The red cell distribution width, ferritin, lactate dehydrogenase, D-dimer, C-reactive protein, prothrombin time, and creatinine levels were found to be the most significant parameters. We found that these parameters are significant for predicting not only intensive care unit admission, but also the mortality of the patients admitted to the intensive care unit.

CONCLUSIONS

We determined that the most effective parameters to predict intensive care unit admission and mortality in COVID-19 patients are ferritin, lactate dehydrogenase, D-dimer, C-reactive protein, red cell distribution width, creatinine, and intensive care unit. Close monitoring of these parameters and early intervention in alterations are of vital importance.

Alterations of lipid metabolism provide serologic biomarkers for the detection of asymptomatic versus symptomatic COVID-19 patients

COVID-19 pandemic exerts a health care emergency around the world. The illness severity is heterogeneous. It is mostly unknown why some individuals who are positive for SARS-CoV-2 antibodies stay asymptomatic while others show moderate to severe disease symptoms. Reliable biomarkers for early detection of the disease are urgently needed to attenuate the virus's spread and help make early treatment decisions. Bioactive sphingolipids play a crucial role in the regulation of viral infections and pro-inflammatory responses involved in the severity of COVID-19. However, any roles of sphingolipids in COVID-19 development or detection remain unknown. In this study, lipidomics measurement of serum sphingolipids demonstrated that reduced sphingosine levels are highly associated with the development of symptomatic COVID-19 in the majority (99.24%) SARS-CoV-2-infected patients compared to asymptomatic counterparts. The majority of asymptomatic individuals (73%) exhibited increased acid ceramidase (AC) in their serum, measured by Western blotting, consistent with elevated sphingosine levels compared to SARS-CoV-2 antibody negative controls. AC protein was also reduced in almost all of the symptomatic patients' serum, linked to reduced sphingosine levels, measured in longitudinal acute or convalescent COVID-19 samples. Thus, reduced sphingosine levels provide a sensitive and selective serologic biomarker for the early identification of asymptomatic versus symptomatic COVID-19 patients.

Comprehensive Review of Cardiovascular Involvement in COVID-19

COVID-19 has emerged as one of the most devastating and clinically significant infectious diseases of the last decade. It has reached global pandemic status at an unprecedented pace and has placed significant demands on health care systems worldwide. Although COVID-19 primarily affects the lungs, epidemiologic reports have shown that the disease affects other vital organs of the body, including the heart, vasculature, kidneys, brain, and the hematopoietic system. Of importance is the emerging awareness of the effects of COVID-19 on the cardiovascular system. The current state of knowledge regarding cardiac involvement in COVID-19 is presented in this article, with particular focus on the cardiovascular manifestations and complications of COVID-19 infection. The mechanistic insights of disease causation and the relevant pathophysiology involved in COVID-19 as they affect the heart are explored and described. Relevant practice essentials and clinical management implications for patients with COVID-19 with a cardiac pathology are presented in light of recent evidence.

Predictors of Clinical Outcomes in Adult COVID-19 Patients Admitted to a Tertiary Care Hospital in India: an analytical cross-sectional study

BACKGROUND

The outbreak ofsevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has resulted inexponential rise in the number of patients getting hospitalised with corona virus disease 2019 (COVID-19). There is a paucity of data from South East Asian Region related to the predictors of clinical outcomes in these patients. This formed the basis of conducting our study.

METHODS

This was an analytical cross-sectional study. Demographic, clinical, radiological and laboratory data of 125 patients was collected on admission. The study outcome was death or discharge after recovery. For univariate analysis, unpaired t-test, Chi-square and Fisher's Exact test were used. Receiver operating characteristic (ROC) curves were plotted for Sequential Organ Failure Assessment (SOFA) score and few laboratory parameters. Logistic regression was applied for multivariate analysis.

RESULTS

Elderly age, ischemic heart disease and smoking were significantly associated with mortality. Elevated levels of D-dimer and lactate dehydrogenase (LDH) and reduced lymphocyte counts were the predictors of mortality. The ROCs for SOFA score curve showed a cut-off value ≥ 3.5 (sensitivity- 91.7% and specificity- 87.5%), for IL-6 the cut-off value was ≥ 37.9 (sensitivity- 96% and specificity- 78%) and for lymphocyte counts, a cut off was calculated to be less than and equal to 1.46 x 109per litre (sensitivity-75.2%and specificity- 83.3%).

CONCLUSION

Old age, smoking history, ischemic heart disease and laboratory parameters including elevated D-dimer, raised LDH and low lymphocyte counts at baseline are associated with COVID-19 mortality. A higher SOFA score at admission is a poor prognosticator in COVID-19 patients.

The Interrelationship between Liver Function Test and the Coronavirus Disease 2019: A Systematic Review and Meta-Analysis

Background

The outbreak of the coronavirus disease-2019 (COVID-19) has become a global public health challenge. Assessing the effect of COVID-19 on liver injury is of great importance. A systematic review and meta-analysis were conducted to establish the characteristics of liver function tests in COVID-19 patients.

Methods

A systematic search of publications from December 2019 up to April 2020 in Web of Science, Scopus, and Medline (via PubMed) databases was performed. Both cross-sectional and case series studies reporting an association between liver injury and COVID-19 infection were included. The data were analyzed using the STATA software (version 11.0) and the random-effects model for I2>50% was used to pool the results.

Results

In this meta-analysis, 42 articles comprising a total of 6,557 COVID-19 patients were studied. The prevalence of increase in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels was 30% and 21% in non-severe patients and 38% and 48% in severe patients, respectively. Patients with severe COVID-19 infection were 4.22, 4.96, and 4.13 times more likely to have elevated AST, ALT, and lactate dehydrogenase (LDH) levels, respectively.

Conclusion

Elevation in liver function tests was higher in patients with severe than non-severe COVID-19 infection. Given the widespread use of drugs that increases the risk of hepatotoxicity, healthcare providers should be aware of changes in liver enzymes in COVID-19 patients. The inclusion of other studies from outside China could confirm the pattern of elevation in liver function tests in COVID-19 patients across the globe. Preprint of this article is available on medRxiv, https://www.medrxiv.org/content/10.1101/2020.05.20.20108357v1.

Glycophosphopeptical AM3 Food Supplement: A Potential Adjuvant in the Treatment and Vaccination of SARS-CoV-2

The world is currently experiencing the coronavirus disease 2019 (COVID-19) pandemic caused by Severe Acute Respiratory Syndrome-2 (SARS-CoV-2). Its global spread has resulted in millions of confirmed infections and deaths. While the global pandemic continues to grow, the availability of drugs to treat COVID-19 infections remains limited to supportive treatments. Moreover, the current speed of vaccination campaigns in many countries has been slow. Natural substrates with biological immunomodulatory activity, such as glucans, may represent an adjuvant therapeutic agent to treat SARS-CoV-2. AM3, a natural glycophosphopeptical, has previously been shown to effectively slow, with no side effects, the progression of infectious respiratory diseases by regulating effects on innate and adaptive immunity in experimental models. No clinical studies, however, exist on the use of AM3 in SARS-CoV-2 infected patients. This review aims to summarize the beneficial effects of AM3 on respiratory diseases, the inflammatory response, modulation of immune response, and attenuation of muscle. It will also discuss its potential effects as an immune system adjuvant for the treatment of COVID-19 infections and adjuvant for SARS-CoV-2 vaccination.

The contributory role of lymphocyte subsets, pathophysiology of lymphopenia and its implication as prognostic and therapeutic opportunity in COVID-19

The incidence of the novel coronavirus disease (COVID-19) outbreak caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought daunting complications for people as well as physicians around the world. An ever-increasing number of studies investigating the characteristics of the disease, day by day, is shedding light on a new feature of the virus with the hope that eventually these efforts lead to the proper treatment. SARS-CoV-2 activates antiviral immune responses, but in addition may overproduce pro-inflammatory cytokines, causing uncontrolled inflammatory responses in patients with severe COVID-19. This condition may lead to lymphopenia and lymphocyte dysfunction, which in turn, predispose patients to further infections, septic shock, and severe multiple organ dysfunction. Therefore, accurate knowledge in this issue is important to guide clinical management of the disease and the development of new therapeutic strategies in patients with COVID-19. In this review, we provide a piece of valuable information about the alteration of each subtype of lymphocytes and important prognostic factors associated with these cells. Moreover, through discussing the lymphopenia pathophysiology and debating some of the most recent lymphocyte- or lymphopenia-related treatment strategies in COVID-19 patients, we tried to brightening the foreseeable future for COVID-19 patients, especially those with severe disease.

Risk factors for persistent abnormality on chest radiographs at 12-weeks post hospitalisation with PCR confirmed COVID-19

Background

The long-term consequences of COVID-19 remain unclear. There is concern a proportion of patients will progress to develop pulmonary fibrosis. We aimed to assess the temporal change in CXR infiltrates in a cohort of patients following hospitalisation for COVID-19.

Methods

We conducted a single-centre prospective cohort study of patients admitted to University Hospital Southampton with confirmed SARS-CoV2 infection between 20th March and 3rd June 2020. Patients were approached for standard-of-care follow-up 12-weeks after hospitalisation. Inpatient and follow-up CXRs were scored by the assessing clinician for extent of pulmonary infiltrates; 0–4 per lung (Nil = 0, < 25% = 1, 25–50% = 2, 51–75% = 3, > 75% = 4).

Results

101 patients with paired CXRs were included. Demographics: 53% male with a median (IQR) age 53.0 (45–63) years and length of stay 9 (5–17.5) days. The median CXR follow-up interval was 82 (77–86) days with median baseline and follow-up CXR scores of 4.0 (3–5) and 0.0 (0–1) respectively. 32% of patients had persistent CXR abnormality at 12-weeks. In multivariate analysis length of stay (LOS), smoking-status and obesity were identified as independent risk factors for persistent CXR abnormality. Serum LDH was significantly higher at baseline and at follow-up in patients with CXR abnormalities compared to those with resolution. A 5-point composite risk score (1-point each; LOS ≥ 15 days, Level 2/3 admission, LDH > 750 U/L, obesity and smoking-status) strongly predicted risk of persistent radiograph abnormality (0.81).

Conclusion

Persistent CXR abnormality 12-weeks post COVID-19 was common in this cohort. LOS, obesity, increased serum LDH, and smoking-status were risk factors for radiograph abnormality. These findings require further prospective validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01750-8.