The Evaluation of Hematological Parameters and Their Correlation with Disease Prognosis in COVID-19 Disease in Iran

Background: Since 2019, Coronavirus has been a highly contagious disease. The COVID-19 outbreak was declared a pandemic by the World Health Organization in March 2020. Variable laboratory findings are reported in COVID-19 patients, among which elevated levels of D-dimer, lactate dehydrogenase, as well as lymphopenia, have been reported to be associated with increased severity of disease symptoms requiring ventilator support, intensive care unit admission, and mortality. Material(s) and Method(s): In the current study, inclusion criteria were: patient age above 18 years and hospitalization in the Imam Khomeini hospital with COVID-19 disease confirmed with nasopharyngeal swab polymerase chain reaction tests. Levels of white blood cells, neutrophils, lymphocytes, hemoglobin, platelets, D-dimer, C-reactive protein, LDH, and ferritin were measured and their correlation with the final patients' outcome was evaluated. Result(s): A total of 208 patients were included in the present study. Higher neutrophil to lymphocyte ratio, (WBC count excluding lymphocyte)/lymphocyte, LDH, platelet to lymphocyte ratio, ferritin, and D-dimer were significantly related to O2 dependency. Neutrophil to lymphocyte ratio, (WBC count excluding lymphocyte)/lymphocyte and LDH were significantly related to higher rates of mortality. Higher Hb and lymphocyte count were significantly related to higher rates of survival. Conclusion(s): Hematological parameters including neutrophil to lymphocyte ratio, (WBC count excluding lymphocyte)/lymphocyte, LDH, platelet to lymphocyte ratio, ferritin, D-dimer, Hb, and lymphocyte count were significantly related to the prognosis of patients with COVID-19 disease. This could help decide which COVID-19 patients have priority for hospitalization and intensive medical care, particularly when the pandemic disease causes limitations in healthcare service.Copyright © 2023 Tehran University of Medical Sciences.

ADPT: An Automated Disease Prognosis Tool Towards Classifying Medical Disease Using Hybrid Architecture of Deep Learning Paradigm

The Covid 19 beta coronavirus, commonly known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the most significant RNA-type viruses in human health. However, more such epidemics occurred beforehand because they were not limited. Much research has recently been carried out on classifying the disease. Still, no automated diagnostic tools have been developed to identify multiple diseases using X-ray, Computed Tomography (CT) scan, or Magnetic Resonance Imaging (MRI) images. In this research, several Tate-of-the-art techniques have been applied to the Chest-Xray, CT scan, and MRI segmented images' datasets and trained them simultaneously. Deep learning models based on VGG16, VGG19, InceptionV3, ResNet50, Capsule Network, DenseNet architecture, Exception and Optimized Convolutional Neural Network (Optimized CNN) were applied to the detecting of Covid-19 contaminated situation, Alzheimer's disease, and Lung infected tissues. Due to efforts taken to reduce model losses and overfitting, the models' performances have improved in terms of accuracy. With the use of image augmentation techniques like flip-up, flip-down, flip-left, flip-right, etc., the size of the training dataset was further increased. In addition, we have proposed a mobile application by integrating a deep learning model to make the diagnosis faster. Eventually, we applied the Image fusion technique to analyze the medical images by extracting meaningful insights from the multimodal imaging modalities. © 2022 IEEE.

Potential predictive value of CT radiomics features for treatment response in patients with COVID-19.

INTRODUCTION: This study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients. METHODS: Data were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response. RESULTS: Among 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration. CONCLUSION: This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.

Epigenetic immune monitoring for COVID-19 disease course prognosis.

Background: The course of COVID-19 is associated with severe dysbalance of the immune system, causing both leukocytosis and lymphopenia. Immune cell monitoring may be a powerful tool to prognosticate disease outcome. However, SARS-CoV-2 positive subjects are isolated upon initial diagnosis, thus barring standard immune monitoring using fresh blood. This dilemma may be solved by epigenetic immune cell counting. Methods: In this study, we used epigenetic immune cell counting by qPCR as an alternative way of quantitative immune monitoring for venous blood, capillary blood dried on filter paper (dried blood spots, DBS) and nasopharyngeal swabs, potentially allowing a home-based monitoring approach. Results: Epigenetic immune cell counting in venous blood showed equivalence with dried blood spots and with flow cytometrically determined cell counts of venous blood in healthy subjects. In venous blood, we detected relative lymphopenia, neutrophilia, and a decreased lymphocyte-to-neutrophil ratio for COVID-19 patients (n =103) when compared with healthy donors (n = 113). Along with reported sex-related differences in survival we observed dramatically lower regulatory T cell counts in male patients. In nasopharyngeal swabs, T and B cell counts were significantly lower in patients compared to healthy subjects, mirroring the lymphopenia in blood. Naïve B cell frequency was lower in severely ill patients than in patients with milder stages. Conclusions: Overall, the analysis of immune cell counts is a strong predictor of clinical disease course and the use of epigenetic immune cell counting by qPCR may provide a tool that can be used even for home-isolated patients.

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.

Background: Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies. Methods: We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688). Findings: The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores. Interpretation: Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials. Funding: This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

The healthier healthcare management models for COVID-19.

The worldwide pandemic situation of COVID-19 generates a situation in which healthcare resources such as diagnostic kits, drugs and basic healthcare infrastructure were on shortage throughout the period, along with negative impact on socio-economic system. Standardized public healthcare models were missing in pandemic situation, covering from hospitalized patient care to local resident's healthcare managements in terms of monitoring, assess to diagnosis and medicines. This exploratory and intervention-based study with the objective of proposing COVID-19 Care Management Model representing comprehensive care of society including patients (COVID-19 and other diseases) and healthy subjects under integrated framework of healthier management model. Shifting policy towards technology-oriented models with well-aligned infrastructure can achieve better outcomes in COVID-19 prevention and care. The planned development of technical healthcare models for prognosis and improved treatment outcomes that take into account not only genomics, proteomics, nanotechnology, materials science perspectives but also the possible contribution of advanced digital technologies is best strategies for early diagnosis and infections control. In view of current pandemic, a Healthier Healthcare Management Model is proposed here as a source of standardized care having technology support, medical consultation, along with public health model of sanitization, distancing and contact less behaviours practices. Effective healthcare managements have been the main driver of healthier society where, positive action at identified research, technology and management segment more specifically public health, patient health, technology selection and political influence has great potential to enhanced the global response to COVID-19. The implementation of such practices will deliver effective diagnosis and control mechanism and make healthier society.

An inter-correlated cytokine network identified at the center of cytokine storm predicted COVID-19 prognosis.

The hyper-inflammatory response is thought to be a major cause of acute respiratory distress syndrome (ARDS) in patients with COVID-19. Although multiple cytokines are reportedly associated with disease severity, the key mediators of SARS-CoV-2 induced cytokine storm and their predictive values have not been fully elucidated. The present study analyzed maximal and early (within 10 days after disease onset) concentrations of 12-plex cytokines in plasma. We found consistently elevated plasma levels of IL-6, IL-8 and IL-5 in patients who were deceased compared with those who had mild/moderate or severe disease. The early plasma concentrations of IFN-a and IL-2 positively correlated with the length of the disease course. Moreover, correlation network analysis showed that IL-6, IL-8, and IL-5 located at the center of an inter-correlated cytokine network. These findings suggested that IL-8, IL-6, IL-5 might play central roles in cytokine storms associated with COVID-19 and that the early detection of multiple plasma cytokines might help to predict the prognosis of this disease.