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Background: To investigate the efficacy of different doses of corticosteroids in treating severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: Between May 01, 2023, and June 20, 2023, 48 patients with severe COVID-19 pneumonia were treated at the Department of Respiratory and Critical Care Medicine of Jinan Fourth People's Hospital. The observation group (21 patients) received standard care and high-dose corticosteroids, (high-dose group). The control group (27 patients) received standard care and low-dose corticosteroids (low-dose group). We collected baseline data and recorded inflammatory marker levels after 3 days of treatment, body temperature recovery time, length of stay, and 28-day all-cause mortality. The results of outpatient follow-up were recorded after 1 month. Results: There were no significant differences in 28-day mortality and length of stay. The number of days it took for body temperature to return to normal in the high-dose group was less than in the low-dose group. The high-dose group had significantly more reduced inflammatory factors (C-reactive protein (CRP), interleukin-6 (IL-6). A total of 20 discharged patients were given 8–16 mg of methylprednisolone, depending on chest computed tomography (CT) and clinical symptoms after 1 month; in all discharged patients using oral corticosteroids, CT features improved. Conclusion: High-dose corticosteroids had a significantly positive effect on the reduction of inflammatory factors and shortening body temperature recovery time. In the treatment of severe COVID-19 pneumonia, early administration of high-dose, short-course corticosteroids should be implemented. Highlights: The highlights of the paper are: 1. High-dose corticosteroid can shorten the recovery time of body temperature. 2. High-dose corticosteroid can better reduce inflammatory factors (CRP IL-6). 3. Oral hormones in discharged patients with severe COVID-19 pneumonia are beneficial. 4. Initial dosage and course of corticosteroid might be varied case by case. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24-48 h of their test result.Methods: A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24-48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology.Results: Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached.Conclusions: This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions.Trial Registration: PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22 . 26/03/2020 ISRCTN registry: ISRCTN86534580 . 20/03/2020 REC number: 20/SC/058 IRAS number: 281958. [ABSTRACT FROM AUTHOR]

Background: Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. Methods: We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. Results: The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. Conclusion: This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses. [ABSTRACT FROM AUTHOR]

Background: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam.Method: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity.Result: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities.Conclusion: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has affected the world extraordinarily. This disease has a potential to cause a significantly severe course of disease leading to respiratory complications, multiple organ failure and possibly death. In the fight against this pandemic-causing disease, medical professionals around the world are searching for pharmacological agents that could treat and prevent disease progression and mortality. To speed the search of promising treatment options, already existing pharmacological agents are repurposed for the potential treatment of COVID-19 and tested in clinical trials. The aim of this literature review is to investigate the efficacy and safety of repurposed pharmacological agents for the treatment of COVID-19 at different pathophysiologic stages of the disease. For this literature review, online-databases PubMed and Google Scholar were utilised. Keywords "COVID-19", "SARS-CoV-2", "pathogenesis", "drug targets", "pharmacological treatment", "cytokine storm", "coagulopathy" and individual drug names were used. Scientific articles, including reviews, clinical trials, and observational cohorts, were collected and analysed. Furthermore, these articles were examined for references to find more clinical trials testing for the potential treatment of COVID-19. In total, 97 references were used to conduct this research paper.Results: The most beneficial pharmacological agent for the treatment of COVID-19 are corticosteroids, especially dexamethasone, for the treatment of mechanically ventilated COVID-19 patients. Other promising agents are remdesivir for the treatment of patients with COVID-19 pneumonia requiring minimal supplemental oxygen therapy, and IL-6 receptor antagonist monoclonal antibodies in severe COVID-19. Lopinavir/ritonavir, as well as chloroquine or hydroxychloroquine with or without azithromycin demonstrate the least efficacy in the treatment of COVID-19. The clinical benefits of the treatment of a COVID-19-specific coagulopathy with increased dosing of anticoagulation need further research and confirmation of randomised controlled trials.Conclusion: The search for pharmacological treatment of COVID-19 has elicited great controversy. Whereas drugs like chloroquine, hydroxychloroquine, and lopinavir/ritonavir have not shown proven benefit, the agents remdesivir and dexamethasone are recommended for clinical use for the treatment of COVID-19. Further randomised trials for other pharmacological treatment strategies are awaited. [ABSTRACT FROM AUTHOR]

Novel coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2. The virus causes an exaggerated immune response, resulting in a cytokine storm and acute respiratory distress syndrome, the leading cause of COVID-19-related mortality and morbidity. So far, no therapies have succeeded in circumventing the exacerbated immune response or cytokine storm associated with COVID-19. Mesenchymal stem cells (MSCs), through their immunomodulatory and regenerative activities, mostly mediated by their paracrine effect and extracellular vesicle production, have therapeutic potential in many autoimmune, inflammatory, and degenerative diseases. In this paper, we review clinical studies on the use of MSCs for COVID-19 treatment, including the salutary effects of MSCs on the pathophysiology of COVID-19 and the immunomodulation of the cytokine storm. Ongoing clinical trial designs, cell sources, dose and administration, and populations are summarized, and the paracrine mode of benefit is discussed. We also offer suggestions for optimizing MSC-based therapies, including genetic engineering, strategies for cell surface modification, nanotechnology applications, and combination therapies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has the potential to cause high morbidity and mortality in crisis-affected populations. Delivering COVID-19 treatment services in crisis settings will likely entail complex trade-offs between offering services of clinical benefit and minimising risks of nosocomial infection, while allocating resources appropriately and safeguarding other essential services. This paper outlines considerations for humanitarian actors planning COVID-19 treatment services where vaccination is not yet widely available. We suggest key decision-making considerations: allocation of resources to COVID-19 treatment services and the design of clinical services should be based on community preferences, likely opportunity costs, and a clearly articulated package of care across different health system levels. Moreover, appropriate service planning requires information on the expected COVID-19 burden and the resilience of the health system. We explore COVID-19 treatment service options at the patient level (diagnosis, management, location and level of treatment) and measures to reduce nosocomial transmission (cohorting patients, protecting healthcare workers). Lastly, we propose key indicators for monitoring COVID-19 health services. [ABSTRACT FROM AUTHOR]