Phase II/III trial of hyperimmune anti-COVID-19 intravenous immunoglobulin (C-IVIG) therapy in severe COVID-19 patients: study protocol for a randomized controlled trial.

BACKGROUND: COVID-19 poses a global health challenge with more than 325 million cumulative cases and above 5 million cumulative deaths reported till January 17, 2022, by the World Health Organization. Several potential treatments to treat COVID-19 are under clinical trials including antivirals, steroids, immunomodulators, non-specific IVIG, monoclonal antibodies, and passive immunization through convalescent plasma. The need to produce anti-COVID-19 IVIG therapy must be continued, alongside the current treatment modalities, considering the virus is still mutating into variants of concern. In this context, as the present study will exploit pooled diversified convalescent plasma collected from recovered COVID-19 patients, the proposed hyperimmune Anti-COVID-19 intravenous immunoglobulin (C-IVIG) therapy would be able to counter new infectious COVID-19 variants by neutralizing the virus particles. After the successful outcome of the phase I/II clinical trial of C-IVIG, the current study aims to further evaluate the safety and efficacy of single low dose C-IVIG in severe COVID-19 patients for its phase II/III clinical trial. METHODS: This is a phase II/III, adaptive, multi-center, single-blinded, randomized controlled superiority trial of SARS-CoV-2 specific polyclonal IVIG (C-IVIG). Patients fulfilling the eligibility criteria will be block-randomized using a sealed envelope system to receive either 0.15 g/Kg C-IVIG with standard of care (SOC) or standard of care alone in 2:1 ratio. The patients will be followed-up for 28 days to assess the primary and secondary outcomes. DISCUSSION: This is a phase II/III clinical trial evaluating safety and efficacy of hyperimmune anti-COVID-19 intravenous immunoglobulin (C-IVIG) in severe COVID-19 patients. This study will provide clinical evidence to use C-IVIG as one of the first-line therapeutic options for severe COVID-19 patients. TRIAL REGISTRATION: Registered at clinicaltrial.gov with NCT number NCT04891172 on May 18, 2021.

The Chinese Carbon-Neutral Goal: Challenges and Prospects.

On 22 September 2020, within the backdrop of the COVID-19 global pandemic, China announced its climate goal for peak carbon emissions before 2030 and to reach carbon neutrality before 2060. This carbon-neutral goal is generally considered to cover all anthropogenic greenhouse gases. The planning effort is now in full swing in China, but the pathway to decarbonization is unclear. The needed transition towards non-fossil fuel energy and its impact on China and the world may be more profound than its reform and development over the past 40 years, but the challenges are enormous. Analysis of four representative scenarios shows significant differences in achieving the carbon-neutral goal, particularly the contribution of non-fossil fuel energy sources. The high target values for nuclear, wind, and bioenergy have approached their corresponding resource limitations, with solar energy being the exception, suggesting solar's critical role. We also found that the near-term policies that allow for a gradual transition, followed by more drastic changes after 2030, can eventually reach the carbon-neutral goal and lead to less of a reduction in cumulative emissions, thus inconsistent with the IPCC 1.5°C scenario. The challenges and prospects are discussed in the historical context of China's socio-economic reform, globalization, international collaboration, and development.

Publishing and Interlinking COVID-19 Data Using Linked Open Data Principles: Toward Effective Healthcare Planning and Decision-Making

The COVID-19 data is critical to support countries and healthcare organizations for effective planning and evidence-based practices to counter the pressures of cost reduction, improved coordination, and outcome and produce more with less. Several COVID-19 datasets are published on the web to support stakeholders in gaining valuable insights for better planning and decision-making in healthcare. However, the datasets are produced in heterogeneous proprietary formats, which create data silos and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is usually performed by the domain experts manually, which is time-consuming and error-prone. Therefore, an explicit, flexible, and widely acceptable methodology to represent, store, query, and visualize COVID-19 data is needed. In this paper, we have presented the design and development of the Linked Open COVID-19 Data system for structuring and transforming COVID-19 data into semantic format using explicitly developed ontology and publishing on the web using Linked Open Data (LOD) principles. The key motivation of this research is the evaluation of LOD technology as a potential option and application of the available Semantic Web tools (i.e., Protégé, Excel2RDF, Fuseki, Silk, and Sgvizler) for building LOD-based COVID-19 information systems. We have also underpinned several use-case scenarios exploiting the LOD format of the COVID-19 data, which could be used by applications and services for providing relevant information to the end-users. The effectiveness of the proposed methodology and system is evaluated using the system usability scale and descriptive statistical methods and results are found promising. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Chinese Carbon-Neutral Goal: Challenges and Prospects

On 22 September 2020, within the backdrop of the COVID-19 global pandemic, China announced its climate goal for peak carbon emissions before 2030 and to reach carbon neutrality before 2060. This carbon-neutral goal is generally considered to cover all anthropogenic greenhouse gases. The planning effort is now in full swing in China, but the pathway to decarbonization is unclear. The needed transition towards non-fossil fuel energy and its impact on China and the world may be more profound than its reform and development over the past 40 years, but the challenges are enormous. Analysis of four representative scenarios shows significant differences in achieving the carbon-neutral goal, particularly the contribution of non-fossil fuel energy sources. The high target values for nuclear, wind, and bioenergy have approached their corresponding resource limitations, with solar energy being the exception, suggesting solar's critical role. We also found that the near-term policies that allow for a gradual transition, followed by more drastic changes after 2030, can eventually reach the carbon-neutral goal and lead to less of a reduction in cumulative emissions, thus inconsistent with the IPCC 1.5°C scenario. The challenges and prospects are discussed in the historical context of China's socio-economic reform, globalization, international collaboration, and development. Electronic supplementary material Supplementary material is available in the online version of this article at 10.1007/s00376-021-1313-6.

Forecasting daily new infections, deaths and recovery cases due to COVID-19 in Pakistan by using Bayesian Dynamic Linear Models.

The COVID-19 has caused the deadliest pandemic around the globe, emerged from the city of Wuhan, China by the end of 2019 and affected all continents of the world, with severe health implications and as well as financial-damage. Pakistan is also amongst the top badly effected countries in terms of casualties and financial loss due to COVID-19. By 20th March, 2021, Pakistan reported 623,135 total confirmed cases and 13,799 deaths. A state space model called 'Bayesian Dynamic Linear Model' (BDLM) was used for the forecast of daily new infections, deaths and recover cases regarding COVID-19. For the estimation of states of the models and forecasting new observations, the recursive Kalman filter was used. Twenty days ahead forecast show that the maximum number of new infections are 4,031 per day with 95% prediction interval (3,319-4,743). Death forecast shows that the maximum number of the deaths with 95% prediction interval are 81 and (67-93), respectively. Maximum daily recoveries are 3,464 with 95% prediction interval (2,887-5,423) in the next 20 days. The average number of new infections, deaths and recover cases are 3,282, 52 and 1,840, respectively, in the upcoming 20 days. As the data generation processes based on the latest data has been identified, therefore it can be updated with the availability of new data to provide latest forecast.

Hyperimmune anti-COVID-19 IVIG (C-IVIG) treatment in severe and critical COVID-19 patients: A phase I/II randomized control trial.

BACKGROUND: Hyperimmune anti-COVID-19 Intravenous Immunoglobulin (C-IVIG) is an unexplored therapy amidst the rapidly evolving spectrum of medical therapies for COVID-19 and is expected to counter the three most life-threatening consequences of COVID-19 including lung injury by the virus, cytokine storm and sepsis. METHODS: A single center, phase I/II, randomized controlled, single-blinded trial was conducted at Dow University of Health Sciences, Karachi, Pakistan. Participants were COVID-19 infected individuals, classified as either severely or critically ill with Acute Respiratory Distress Syndrome (ARDS). Participants were randomized through parallel-group design with sequential assignment in a 4:1 allocation to either intervention group with four C-IVIG dosage arms (0.15, 0.20, 0.25, 0.30 g/kg), or control group receiving standard of care only (n = 10). Primary outcomes were 28-day mortality, patient's clinical status on ordinal scale and Horowitz index (HI), and were analysed in all randomized participants that completed the follow-up period (intention-to-treat population). The trial was registered at clinicaltrials.gov (NCT04521309). FINDINGS: Fifty participants were enrolled in the study from June 19, 2020 to February 3, 2021 with a mean age of 56.54±13.2 years of which 22 patients (44%) had severe and 28 patients (56%) had critical COVID-19. Mortality occurred in ten of 40 participants (25%) in intervention group compared to six of ten (60%) in control group, with relative risk reduction in intervention arm I (RR, 0.333; 95% CI, 0.087-1.272), arm II (RR, 0.5; 95% CI, 0.171-1.463), arm III (RR, 0.167; 95% CI, 0.024-1.145), and arm IV (RR, 0.667; 95% CI, 0.268-1.660). In intervention group, median HI significantly improved to 359 mmHg [interquartile range (IQR) 127-400, P = 0.009)] by outcome day, while the clinical status of intervention group also improved as compared to control group, with around 15 patients (37.5%) being discharged by 7th day with complete recovery. Additionally, resolution of chest X-rays and restoration of biomarkers to normal levels were also seen in intervention groups. No drug-related adverse events were reported during the study. INTERPRETATION: Administration of C-IVIG in severe and critical COVID-19 patients was safe, increased the chance of survival and reduced the risk of disease progression. FUNDING: Higher Education Commission (HEC), Pakistan (Ref no. 20-RRG-134/RGM/R&D/HEC/2020).

Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world. Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases. In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July. For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied. EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs). For individual IMFs modelling, we use the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates. Our analyses reveal that the number of recoveries, new cases, and deaths are increasing in Pakistan exponentially. Based on the selected EEMD-ARIMA model, the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020, which is an increase of almost 1.46 times with a 95% prediction interval of 246,529 to 376,379. The 95% prediction interval for recovery is 162,414 to 224,579, with an increase of almost two times in total from 100802 to 193495 by 31 July 2020. On the other hand, the deaths are expected to increase from 4395 to 6751, which is almost 1.54 times, with a 95% prediction interval of 5617 to 7885. Thus, the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020. They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19, and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios. The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.

Production of hyperimmune anti-SARS-CoV-2 intravenous immunoglobulin from pooled COVID-19 convalescent plasma.

Background: This study assesses the feasibility of producing hyperimmune anti-COVID-19 intravenously administrable immunoglobulin (C-IVIG) from pooled convalescent plasma (PCP) to provide a safe and effective passive immunization treatment option for COVID-19. Materials & methods: PCP was fractionated by modified caprylic acid precipitation followed by ultrafiltration/diafiltration to produce hyperimmune C-IVIG. Results: In C-IVIG, the mean SARS-CoV-2 antibody level was found to be threefold (104 ± 30 cut-off index) that of the PCP (36 ± 8.5 cut-off index) and mean protein concentration was found to be 46 ± 3.7 g/l, comprised of 89.5% immunoglobulins. Conclusion: The current method of producing C-IVIG is feasible as it uses locally available PCP and simpler technology and yields a high titer of SARS-CoV-2 antibody. The safety and efficacy of C-IVIG will be evaluated in a registered clinical trial (NCT04521309).

Hyperimmune anti-COVID-19 IVIG (C-IVIG) Therapy for Passive Immunization of Severe and Critically Ill COVID-19 Patients: A structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: The aim of this trial is to investigate the safety and clinical efficacy of passive immunization therapy through Hyperimmune anti-COVID-19 Intravenous Immunoglobulin (C-IVIG: 5% liquid formulation), on severe and critically ill patients with COVID-19. TRIAL DESIGN: This is a phase I/II single centre, randomised controlled, single-blinded, superiority trial, through parallel-group design with sequential assignment. Participants will be randomised either to receive both C-IVIG and standard care or only standard care (4:1). PARTICIPANTS: The study is mono-centric with the participants including COVID19 infected individuals (positive SARS-CoV-2 PCR on nasopharyngeal and/or oropharyngeal swabs) admitted in institute affiliated with Dow University Hospital, Dow University of Health Sciences, Karachi, Pakistan. Consenting patients above 18 years that are classified by the treating physician as severely ill i.e. showing symptoms of COVID-19 pneumonia; dyspnea, respiratory rate ≥30/min, blood oxygen saturation ≤93%, PaO2/FiO2 <300, and lung infiltrates >50% on CXR; or critically ill i.e. respiratory failure, septic shock, and multiple organ dysfunction or failure. Patients with reported IgA deficiency, autoimmune disorder, thromboembolic disorder, and allergic reaction to immunoglobulin treatment were excluded from study. Similarly, pregnant females, patients requiring two or more inotropic agents to maintain blood pressure and patients with acute or chronic kidney injury/failure, were also excluded from the study. INTERVENTION AND COMPARATOR: The study consists of four interventions and one comparator arm. All participants receive standard hospital care which includes airway support, anti-viral medication, antibiotics, fluid resuscitation, hemodynamic support, steroids, painkillers, and anti-pyretics. Randomised test patients will receive single dose of C-IVIG in following four dosage groups: Group 1: 0.15g/Kg with standard hospital care Group 2: 0.2g/Kg with standard hospital care Group 3: 0.25g/Kg with standard hospital care Group 4: 0.3g/Kg with standard hospital care Group 5 (comparator) will receive standard hospital care only MAIN OUTCOMES: The primary outcomes are assessment and follow-up of participants to observe 28-day mortality and, • the level and duration of assisted ventilation during hospital stay, • number of days to step down (shifting from ICU to isolation ward), • number of days to hospital discharge, • adverse events (Kidney failure, hypersensitivity with cutaneous or hemodynamic manifestations, aseptic meningitis, hemolytic anemia, leuko-neutropenia, transfusion related acute lung injury (TRALI)) during hospital stay, • change in C-Reactive Protein (CRP) levels, • change in neutrophil lymphocyte ratio to monitor inflammation. RANDOMISATION: Consenting participants who fulfill the criteria are allocated to either intervention or comparator arm with a ratio of 4:1, using sequentially numbered opaque sealed envelope simple randomization method. The participant allocated for intervention will be sequentially assigned dosage group 1-4 in ascending order. Participants will not be recruited in the next dosage group before a set number of participants in one group (10) are achieved. BLINDING (MASKING): Single blinded study, with participants blinded to allocation. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): Total 50 patients are randomised. The intervention arms consist of 40 participants divided in four groups of 10 participants while the comparator group consists of 10 patients. TRIAL STATUS: Current version of the protocol is "Version 2" dated 29th September, 2020. Participants are being recruited. Recruitment started on June, 2020 and is estimated to primarily end on January, 2021. TRIAL REGISTRATION: This trial was registered at ClinicalTrials.gov, NCT04521309 on 20 August 2020 and is retrospectively registered. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1).

Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan.

COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013-8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112-233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182-6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future.