Predictive monitoring and therapeutic immune biomarkers in the management of clinical complications of COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Fouladseresht H
PROVIDER: S-EPMC7544568 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Severe coronavirus (SARS-COV-2) infection often leads to systemic inflammation accompanied by cardiovascular complications including venous thromboembolism (VTE). However, it is largely undefined if inflammatory markers such as lipocalin-2 (LNC2), calprotectin (S100A8/A9), and cystatin C (CST3), previously linked with VTE, play roles in cardiovascular complications and advancement of COVID-19 severity. To investigate the same, hospitalized moderate and severe (presented pneumonia and required intensive care) COVID-19 patients were recruited. The levels of plasma LNC2, S100A8/A9, CST3, myoglobin, and cardiac Troponin I (cTnI) were assessed through enzyme-linked immunosorbent assay (ELISA). The investigation revealed a significantly upregulated level of plasma LNC2 at the moderate stage of SARS-CoV-2 infection. In contrast, the levels of S100A8/A9 and CST3 in moderate patients were comparable to healthy controls; however, a profound induction was observed only in severe COVID-19 patients. The tissue injury marker myoglobin was unchanged in moderate patients; however, a significantly elevated level was observed in the critically ill COVID-19 patients. In contrast, cTnI level was unchanged both in moderate and severe patients. Analysis revealed a positive correlation between the levels of S100A8/A9 and CST3 with myoglobin in COVID-19. In silico analysis predicted interactions of S100A8/A9 with toll-like receptor 4 (TLR-4), MyD88 LY96, and LCN2 with several other inflammatory mediators including MMP2, MMP9, TIMP1, and interleukins (IL-6, IL-17A, and IL-10). In summary, early induction of LCN2 likely plays a role in advancing the COVID-19 severity. A positive correlation of S100A8/A9 and CST3 with myoglobin suggests that these proteins may serve as predictive biomarkers for thromboembolism and tissue injury in COVID-19.
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Project description:The global pandemic COVID-19, caused by novel coronavirus SARS-CoV-2, has emerged as severe public health issue crippling world health care systems. Substantial knowledge has been generated about the pathophysiology of the disease and possible treatment modalities in a relatively short span of time. As of August 19, 2020, there is no approved drug for the treatment of COVID-19. More than 600 clinical trials for potential therapeutics are underway and the results are expected soon. Based on early experience, different treatment such as anti-viral drugs (remdesivir, favipiravir, lopinavir/ritonavir), corticosteroids (methylprednisolone, dexamethasone) or convalescent plasma therapy are recommended in addition to supportive care and symptomatic therapy. There are several treatments currently being investigated to address the pathological conditions associated with COVID-19. This review provides currently available information and insight into pathophysiology of the disease, potential targets, and relevant clinical trials for COVID-19.
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Project description:The outbreak of coronavirus disease 2019 (COVID-19), an infectious disease with severe acute respiratory syndrome, has now become a worldwide pandemic. Despite the respiratory complication, COVID-19 is also associated with significant multiple organ dysfunction, including severe cardiac impairment. Emerging evidence reveals a direct interplay between COVID-19 and dire cardiovascular complications, including myocardial injury, heart failure, heart attack, myocarditis, arrhythmias as well as blood clots, which are accompanied with elevated risk and adverse outcome among infected patients, even sudden death. The proposed pathophysiological mechanisms of myocardial impairment include invasion of SARS-CoV-2 virus via angiotensin-converting enzyme 2 to cardiovascular cells/tissue, which leads to endothelial inflammation and dysfunction, de-stabilization of vulnerable atherosclerotic plaques, stent thrombosis, cardiac stress due to diminish oxygen supply and cardiac muscle damage, and myocardial infarction. Several promising therapeutics are under investigation to the overall prognosis of COVID-19 patients with high risk of cardiovascular impairment, nevertheless to date, none have shown proven clinical efficacy. In this comprehensive review, we aimed to highlight the current integrated therapeutic approaches for COVID-19 and we summarized the potential therapeutic options, currently under clinical trials, with their mechanisms of action and associated adverse cardiac events in highly infectious COVID-19 patients.
| S-EPMC7554795 | biostudies-literature
Project description:Since December 2019, SARS-CoV-2 (COVID-19), novel corona virus has caused pandemic globally, with rise in the number of cases and death of the patients. Vast majority of the countries that are dealing with rise in the active cases and death of patients suffering from novel corona viruses COVID-19 are trying to content the virus by isolating the patients and treating them with the approved antiviral that have been previously used in treating SARS, MERS, and drugs that are used to treat other viral infections. Some of these are under clinical trials. At present there are no therapeutically effective antiviral present and there are no vaccines or drugs available that are clinically approved for treating the corona virus. The current strategy is to re-purpose the available drugs or antiviral that can minimise or reduce the burden of the health care emergencies. In this article the reuse of antiviral, US-FDA approved drugs, plant based therapeutic, anti-malarial, anti-parasitic, anti-HIV drugs and the traditional medicines that are being currently used in treating the symptoms of COVID-19 patients is discussed emphasis is also given on the treatment using monoclonal antibodies. The present article provides the therapeutic strategies that will qualify as one of the best available treatment for the better management of the COVID-19 patients in order to achieve medical benefits.
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Project description:Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
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Project description:With the advent of targeted therapies, there has been a revolution in the treatment of cancer across multiple histologies. Immune checkpoint blockade has made it possible to take advantage of receptor-ligand interactions between immune and tumor cells in a wide spectrum of malignancies. Toxicity in healthy tissue, however, can limit our use of these agents. Immune checkpoint blockade has been approved in advanced melanoma, renal cell cancer, non-small cell lung cancer, relapsed refractory Hodgkin's lymphoma, and urothelial cancer. Though FDA-approved indications for use of some of these novel agents depend on current protein-based programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) assays, detection methods come with several caveats. Additional predictive tools must be interrogated to discern responders from non-responders. Some of these include measurement of microsatellite instability, PD-L1 amplification, cluster of differentiation 8 (CD8) infiltrate density, and tumor mutational burden. This review serves to synthesize biomarker detection at the DNA, RNA, and protein level to more accurately forecast benefit from these novel agents.
| S-EPMC5385298 | biostudies-other
Project description:BACKGROUND:COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE:The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS:Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS:Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS:Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.
| S-EPMC7541040 | biostudies-literature
Project description:BackgroundImmunocompromised individuals are expected to be more prone to severe diseases and, subsequently, death. Genetic disorders and polymorphisms in genes involved in the immune system, such as human leukocyte antigen (HLA), inflammatory cytokines, and killer-cell immunoglobulin-like receptors, can be involved in the immune system's response to various pathogens. In the current survey, the data were received from the world health organization, collected around the world.ResultsSpearman's coefficient correlation test for evaluating the relationship between the Daily Death Rates (DDR) and immunological variables showed a statistically significant correlation between the DDR and all immunological variables except TNFa857T, TNFa863A IL2330G, and IL2166T (P < 0.001). Also, there was a statistically significant correlation between the DDR and some HLA markers.ConclusionThis meta-analysis study shows that predictive biomarkers and mortality of COVID-19 are associated with HLA markers. However, these results should be confirmed in a more structured agreement. It is worth noting that the design of new studies should consider potential diseases with poor prognoses because they are related to these immune genetic markers.Supplementary informationThe online version contains supplementary material available at 10.1186/s42269-022-00844-7.
| S-EPMC9152825 | biostudies-literature