Protein C and S activities in COVID-19: A systematic review and meta-analysis.

COVID-19 has been associated with alterations in coagulation. Recent reports have shown that protein C and S activities are altered in COVID-19. This may affect the complications and outcome of the disease. However, their exact role in COVID-19 remains uncertain. The aim of the current study was therefore to analyze all papers in the literature on protein C and S activities in COVID-19. We searched three medical electronic databases. Of the 2442 papers, 28 studies were selected for the present meta-analysis. For the meta-analysis, means ± standard deviations with 95% confidence intervals (CI) for protein C and S activities were extracted. Pooled p values were calculated using STATA software. Protein C and S activities were significantly lower in COVID-19 patients than in healthy controls (pooled p values: 0.04 and 0.02, respectively). Similarly, protein C activities were considerably lower in nonsurviving patients (pooled p value = 0.00). There was no association between proteins C or S and thrombosis risk or ICU admission in COVID-19 patients (p value > 0.05). COVID-19 patients may exhibit lower activities of the C and S proteins, which might affect disease outcome; however, additional attention should be given when considering therapeutic strategies for these patients.

Prognostic significance of ASXL1 mutations in acute myeloid leukemia: A systematic review and meta-analysis.

Background: Although genetic mutations in additional sex-combs-like 1 (ASXL1) are prevalent in acute myeloid leukemia (AML), their exact impact on the AML prognosis remains uncertain. Hence, the present article was carried out to explore the prognostic importance of ASXL1 mutations in AML. Methods: We thoroughly searched electronic scientific databases to find eligible papers. Twenty-seven studies with an overall number of 8,953 participants were selected for the current systematic review. The hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS), event-free survival (EFS), and relapse-free survival (RFS) were extracted from all studies with multivariate or univariate analysis. Pooled HRs and p-values were also calculated as a part of our work. Results: The pooled HR for OS in multivariable analysis indicated that ASXL1 significantly diminished survival in AML patients (pooled HR: 1.67; 95% CI: 1.342-2.091). Conclusions: ASXL1 mutations may confer a poor prognosis in AML. Hence, they may be regarded as potential prognostic factors. However, more detailed studies with different ASXL1 mutations are suggested to shed light on this issue.

A novel physical activity recognition approach using deep ensemble optimized transformers and reinforcement learning.

In recent years, human physical activity recognition has increasingly attracted attention from different research fields such as healthcare, computer-human interaction, lifestyle monitoring, and athletics. Deep learning models have been extensively employed in developing physical activity recognition systems. To improve these models, their hyperparameters need to be initialized with optimal values. However, tuning these hyperparameters manually is time-consuming and may lead to inaccurate results. Moreover, the application of these models to different data resources and the integration of their results into the overall data processing pipeline are challenging issues in physical activity recognition systems. In this paper, we propose a novel ensemble method for physical activity recognition based on a deep transformer-based time-series classification model that uses heart rate, speed, and distance time-series data to recognize physical activities. In particular, we develop a modified arithmetic optimization algorithm to automatically adjust the optimal values of the classification models' hyperparameters. Moreover, a reinforcement learning-based ensemble approach is proposed to optimally integrate the results of the classification models obtained using heart rate, speed, and distance time-series data and, subsequently, recognize the physical activities. Experiments performed on a real-world dataset demonstrated that the proposed method achieves promising efficiency in comparison to other state-of-the-art models. More specifically, the proposed method increases the performance compared to the second-best performer by around 3.44 %, 9.45 %, 5.43 %, 2.54 %, and 7.53 % based on accuracy, precision, recall, specificity, and F1-score evaluation metrics, respectively.

Health-aware food recommendation system with dual attention in heterogeneous graphs.

Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyses recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.

Evaluation of the expression of LC3-II and BECLIN1 genes of autophagy pathway in patients with hematological malignancies.

Background: Autophagy is a pathway for the degradation of cytoplasmic components, which plays an essential role in various cellular and physiological processes, including cell renewal and survival, and immune responses. While recent studies have shown that they can play a role in cancer treatment, the precise mechanisms of autophagy in leukemogenesis are not fully understood. We have assessed the expression levels of LC3 and BECLIN1 as two crucial autophagy mediators in patients with leukemia. Methods: This cross-sectional study was performed on bone marrow or peripheral blood samples of 61 leukemia patients (24 AML, 20 ALL, and 17 CML) and compared to 18 healthy controls. Real-time PCR was used to quantitate gene expression. SPSS statistics 16.0 and Graph Pad Prism 8.4.2 software were applied for statistical analysis. Results: While BECLIN1 expression was significantly lower in AML, ALL, and CML patients as compared to the control group (p < 0.05), LC3 showed significantly different expression only in the AML patients (P= 0.03). There was no significant correlation between the expression levels of BECLIN1 with LC3 (p> 0.05). Whilst the AML LC3high group had a significantly lower lymphocyte count (P= 0.023), the AML BECLIN1low group had a significantly higher MPV levels (P= 0.044). Furthermore, ALL LC3high group indicated a significantly lower HCT count (P= 0.017). Conclusion: Significant changes in the expression levels of BECLINI and LC3 in hematologic malignancies may indicate a possible role for autophagy in their pathogenesis. However, further studies are warranted to confirm these findings.

Significance of heparin induced thrombocytopenia (HIT) in COVID-19: a systematic review and meta-analysis.

Heparin-induced thrombocytopenia (HIT) occurs in approximately 3% of patients receiving heparinoids. About 30-75% of patients with type 2 of HIT develop thrombosis as a result of platelet activation. The most important clinical symptom is thrombocytopenia. Patients with severe COVID-19 are among those receiving heparinoids. This meta-analysis performed to picture the current knowledge and results of published studies in this field. Three search engines were searched and 575 papers were found. After evaluation, 37 articles were finally selected of which 13 studies were quantitatively analyzed. The pooled frequency rate of suspected cases with HIT in 13 studies with 11,241 patients was 1.7%. The frequency of HIT was 8.2% in the extracorporeal membrane oxygenation subgroup with 268 patients and 0.8% in the hospitalization subgroup with 10,887 patients. The coincidence of these two conditions may increase the risk of thrombosis. Of the 37 patients with COVID-19 and confirmed HIT, 30 patients (81%) were treated in the intensive care unit or had severe COVID-19. The most commonly used anticoagulants were UFH in 22 cases (59.4%). The median platelet count before treatment was 237 (176-290) x 103/µl and the median nadir platelet count was 52 (31-90.5) x 103/µl.

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments.

OBJECTIVE: Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers. METHODS: Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories. RESULTS: Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data. CONCLUSION: AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.

Replacing sedentary time with physical activity and sleep: Associations with cardiometabolic health markers in adults.

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 h of movement and non-movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4585 members of the Northern Finland Birth Cohort 1966, who wore a hip-worn accelerometer at the age of 46 years for 14 consecutive days. Time spent in sedentary behaviors, light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) were determined from the accelerometer and combined with self-reported sleep duration to obtain the 24-h time use. CRF was estimated from the peak heart rate in a submaximal step test. An isotemporal substitution paradigm was used to examine how sedentary time and substituting sedentary time with an equal amount of LPA, MVPA, or sleep were associated with adiposity markers, blood lipid levels, and fasting glucose and insulin. Sedentary time was independently and adversely associated with the markers of cardiometabolic health, even after adjustment for CRF, but not in partition models including LPA, MVPA, sleep, and CRF. Substituting 60, 45, 30, and 15 min/day of sedentary time with LPA or MVPA was associated with 0.2%-13.7% favorable differences in the cardiometabolic health markers after accounting for LPA, MVPA, sleep, CRF, and other confounders. After adjustment for movement and non-movement behaviors within the 24-h cycle, reallocating additional time to both LPA and MVPA was beneficially associated with markers of cardiometabolic health in middle-aged adults regardless of their CRF level.

Dysregulation of miRNA expression in patients with chronic myelogenous leukemia at diagnosis: a systematic review.

Aim: The present systematic review aimed to explore miRNAs as a potential biomarker for early diagnosis of chronic myeloid leukemia (CML). Materials & methods: A systematic search was conducted in three electronic databases, including Web of Science, Scopus and PubMed, to obtain relevant articles investigating the alteration of miRNA expression in patients with CML. Results: The authors found miRNAs whose expression changes are effective in the induction of CML disease. Among them, miR-21 and miR-155 were identified as the most common miRNAs with increased expression and miR-150 and miR-146 as the most common miRNAs with decreased expression. Conclusion: miRNAs can be used as an indicator for the early detection and treatment of CML phase.

Deregulation of the Expression of Beclin1 and Light Chain 3(LC3), Autophagy-Related Genes, in COVID-19 Patients.

Background: The autophagy machinery is reported to be employed by Coronaviruses during their replication. Beclin-1 (BECN1) and protein 1 light chain 3 (LC3) are two key elements in the autophagy process, and their inhibition can prevent the replication of some coronaviruses in vitro. Here, we aimed to investigate the expression levels of Beclin-1 and LC3 in COVID-19 patients and healthy controls, hoping to find new therapeutic targets. Methods: This cross-sectional study was conducted in Imam Reza and Ghaem University Hospitals, Mashhad, Iran. Nasopharyngeal samples of 68 consecutive Covid-19 patients and 61 healthy controls, who have been referred to the laboratories for COVID-19 PCR testing between 21 March to 21 September 2021, were used in order to evaluate the expression of BECN1 and LC3 genes using the Real-time quantitative PCR method. Demographic and other laboratory findings of patients were extracted from the hospital electronic system. SPSS Statistics 16.0 and Graph Pad Prism 8.4.2 soft wares were used for statistical analysis. Non-parametric tests were used. Results: BECN1 expression was significantly higher in COVID-19 patients compared to the controls (14.37±18.84 vs. 4.26±7.39, p=0.001). The expression of LC3 gene was significantly lower in patients compared to the controls (1.01±1.06 vs. 1.49±1.12, p=0.007). There was no significant correlation between the expression levels of BECN1 and LC3. Patients with lower BECN1 expression showed significantly higher RBC counts, higher Urea and lower HCO3 levels. The patients in LC3Low group showed significantly lower MCH, MCHC and PH levels compared to the others. Conclusion: Regarding the significant difference in the expression of BECN1 and LC3 in COVID-19 patients compared to the controls, these molecules may have a role in the pathogenesis of this disease. In case of further confirmation of this role, these molecules may be used as possible therapeutic targets.

Joint Profiles of Sedentary Time and Physical Activity in Adults and Their Associations with Cardiometabolic Health.

PURPOSE: This study aimed to identify and characterize joint profiles of sedentary time and physical activity among adults and to investigate how these profiles are associated with markers of cardiometabolic health. METHODS: The participants included 3702 of the Northern Finland Birth Cohort 1966 at age 46 yr, who wore a hip-worn accelerometer during waking hours and provided seven consecutive days of valid data. Sedentary time, light-intensity physical activity, and moderate- to vigorous-intensity physical activity on each valid day were obtained, and a data-driven clustering approach ("KmL3D") was used to characterize distinct joint profiles of sedentary time and physical activity intensities. Participants self-reported their sleep duration and performed a submaximal step test with continuous heart rate measurement to estimate their cardiorespiratory fitness (peak heart rate). Linear regression was used to determine the association between joint profiles of sedentary time and physical activities with cardiometabolic health markers, including adiposity markers and blood lipid, glucose, and insulin levels. RESULTS: Four distinct groups were identified: "active couch potatoes" ( n = 1173), "sedentary light movers" ( n = 1199), "sedentary exercisers" ( n = 694), and "movers" ( n = 636). Although sufficiently active, active couch potatoes had the highest daily sedentary time (>10 h) and lowest light-intensity physical activity. Compared with active couch potatoes, sedentary light movers, sedentary exercisers, and movers spent less time in sedentary by performing more physical activity at light-intensity upward and had favorable differences in their cardiometabolic health markers after accounting for potential confounders (1.1%-25.0% lower values depending on the health marker and profile). CONCLUSIONS: After accounting for sleep duration and cardiorespiratory fitness, waking activity profiles characterized by performing more physical activity at light-intensity upward, resulting in less time spent in sedentary, were associated with better cardiometabolic health.

Trend of fluctuations of antithrombin in plasma of patients with COVID-19: a meta-analysis and systematic review.

BACKGROUND: Antithrombin is considered as one of the accused markers for the development of thrombosis in patients with COVID-19. Because plasma levels of antithrombin vary in patients with COVID-19, a meta-analysis was performed to determine the trend of antithrombin levels in patients with COVID-19. RESEARCH DESIGN AND METHODS: A literature search was performed on PubMed, Scopus, and the Web of Science to find papers on antithrombin levels in patients with COVID-19. After removing duplicate papers, inclusion and exclusion criteria were applied. The full texts of the articles were read to select relevant articles and then to identify the data needed. All meta-analyses were performed using Stata software v16.0. RESULTS: Testing for differences between subgroups showed a significant difference between ICU and non-ICU patients. Analysis showed a significant decrease in antithrombin level in patients with severe COVID-19. Analysis showed that the mean value of antithrombin level was 89.65% in all patients. The antithrombin level was significantly lower in the non-survivor group (87.52%) than in the survivor group (92.38%). CONCLUSION: Determination of antithrombin may be useful to determine the susceptibility of COVID-19 patients to hypercoagulability and to indicate the severity of COVID-19 infection.

Graph-based relevancy-redundancy gene selection method for cancer diagnosis.

Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority.

Evaluating the frequency, prognosis and survival of RUNX1 and ASXL1 mutations in patients with acute myeloid leukaemia in northeastern Iran.

To evaluate the frequency and prognosis of runt-related transcription factor 1 (RUNX1) and additional sex combs like-1 (ASXL1) mutations in acute myeloid leukaemia (AML) patients in northeastern Iran. This cross-sectional study was performed on 40 patients with AML (including 35 patients with denovo AML and five patients with secondary AML) from February 2018 to February 2021. All patients were followed up for 36 months. We evaluated the frequency and survival rate of RUNX1 and ASXL1 mutations in AML patients. To detect mutations, peripheral blood samples and bone marrow aspiration were taken from all participants. One male patient (2.5%) had RUNX1 mutations and four cases (10%; 3 females vs. 1 male) had ASXL1 mutations. The survival rates of AML patients after 1, 3, 6, 9, 12, 24 and 36 months were 98%, 90%, 77%, 62%, 52%, 27% and 20%, respectively. There was a significant relationship between the occurrence of ASXL1 mutations and the survival of patients with AML (p = 0.027). Also, there was a significant relationship between the incidence of death and haemoglobin levels in patients with AML (p = 0.045). Thus, with an increase of one unit in patients' haemoglobin levels, the risk of death is reduced by 16.6%. Patients with AML had a high mortality rate, poor therapy outcome and low survival rate. ASXL1 and RUNX1 mutations are associated with a worse prognosis in patients with newly diagnosed AML. Also, we witnessed that the prevalence of ASXL1 to RUNX1 mutations was higher in northeastern Iran compared with other regions.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.

Altered expression of NEAT1 variants and P53, PTEN, and BCL-2 genes in patients with acute myeloid leukemia.

BACKGROUND: Newly discovered evidence showed that long non-coding RNAs (lncRNAs) can play crucial roles in the development of cancer therapies. Nuclear Enriched Abundant Transcripts 1 (NEAT1) is one of the lncRNAs the expression of which changes in different cancers. The role of NEAT1 in apoptosis is discussed in various malignancies. This study aimed to determine the NEAT1 expression and its correlation with P53, PTEN, and BCL-2 genes expression in acute myeloid leukemia (AML) patients. METHOD: In this study, using quantitative real-time polymerase chain reaction (qRT-PCR), we analyzed the expression of NEAT1, P53, PTEN, and BCL-2 genes in 21 AML patients. Moreover, relative quantification analysis was performed by delta-delta CT method (2 -ΔΔCT) method. RESULTS: Our results showed that NEAT1 expression was significantly lower in AML patients compared to healthy controls (P-value < 0.05). While a significant correlation was observed between the expression levels of NEAT1 and PTEN genes in AML patients (P-value < 0.05), there was no correlation between the expression levels of NEAT1 with P53 and BCL-2 (P-value > 0.05). CONCLUSION: According to the results, increased expression of NEAT1 gene may play a role in the apoptosis of AML cells, despite the oncogenic role in most solid tumors.

Gene selection for microarray data classification via multi-objective graph theoretic-based method.

In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.

High levels of Von Willebrand factor markers in COVID-19: a systematic review and meta-analysis.

The SARS-CoV-2 virus has spread to all corners of the world. Thrombosis is the cause of organ failure and subsequent death in COVID-19. The pathophysiology of thrombosis in COVID-19 needs to be further explored to shed light on its downside. For this reason, this meta-analysis of Von Willebrand Factor profile (VWF: Ag, VWF: activity, VWF: RCo), ADAMTS-13, and factor VIII levels in COVID-19 was performed. To obtain data on the status of the aforementioned hemostatic factors, a systematic literature review and meta-analysis were performed on COVID-19. After reviewing the evaluation of 348 papers, 28 papers included in the meta-analysis, which was performed using STATA. The analysis showed an increase in VWF: Ag levels in COVID-19 patients. VWF: Ac was higher in all COVID-19 patients, while it was lower in the COVID-19 ICU patients. The pooled mean of VWF: RCO in all patients with COVID-19 was 307.94%. In subgroup analysis, VWF: RCO was significantly higher in ICU patients than in all COVID-19 patients. The pooled mean of ADAMTS-13 activity was 62.47%, and 58.42% in ICU patients. The pooled mean of factor VIII level was 275.8%, which was significantly higher in ICU patients with COVID-19 than all patients with COVID-19. Levels of VWF: Ag, VWF: activity, VWF: ristocetin, and factor VIII are increased in patients with COVID-19. The elevated levels in ICU patients with COVID-19 suggest that these markers may have prognostic value in determining the severity of COVID-19. New therapeutic programs can be developed as a result.

Hemostatic System (Fibrinogen Level, D-Dimer, and FDP) in Severe and Non-Severe Patients With COVID-19: A Systematic Review and Meta-Analysis.

SARS-CoV-2 in COVID-19 triggers abnormalities in coagulation parameters that can contribute to thrombosis. The goals of this research were to determine the levels of fibrinogen, D-dimer and FDP in COVID-19 patients. Following a systematic study, among 1198 articles, 35 studies were included in the meta-analysis of fibrinogen levels in both severe and non-severe groups. The funnel plot, Egger's regression asymmetry test, and Begg's test used to measure the bias of publications. All meta-analysis performed by comprehensive meta-analysis version 2 (CMA2). The pooled findings of fibrinogen levels revealed a significant rise in fibrinogen levels in severe COVID-19 than non-severe patients with COVID-19. The D-dimer and FDP levels were significantly higher in severe patients than non-severe patients with COVID-19 were. The levels of fibrinogen, D-dimer, and FDP have increased significantly in ICU patients compared to non-ICU patients. Although, levels of clotting parameters do not always correlate with the severity of disease, these findings showed the diagnostic importance for fibrinogen, D-dimer, and FDP in COVID-19. The presence of a continuous rise in serial measurements of fibrinogen, D-dimer, and FDP may predict that patients with COVID-19 may become critically ill.