Advancement of Nonwoven Fabrics in Personal Protective Equipment.

While nonwoven fabrics have existed for several decades, their usage in personal protective equipment (PPE) has been met with a rapid surge of demands, in part due to the recent COVID-19 pandemic. This review aims to critically examine the current state of nonwoven PPE fabrics by exploring (i) the material constituents and processing steps to produce fibers and bond them, and (ii) how each fabric layer is integrated into a textile, and how the assembled textiles are used as PPE. Firstly, filament fibers are manufactured via dry, wet, and polymer-laid fiber spinning methods. Then the fibers are bonded via chemical, thermal, and mechanical means. Emergent nonwoven processes such as electrospinning and centrifugal spinning to produce unique ultrafine nanofibers are discussed. Nonwoven PPE applications are categorized as filters, medical usage, and protective garments. The role of each nonwoven layer, its role, and textile integration are discussed. Finally, the challenges stemming from the single-use nature of nonwoven PPEs are discussed, especially in the context of growing concerns over sustainability. Then, emerging solutions to address sustainability issues with material and processing innovations are explored.

Data capture and sharing in the COVID-19 pandemic: a cause for concern.

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Electrochemiluminescence in paired signal electrode (ECLipse) enables modular and scalable biosensing.

Electrochemiluminescence (ECL) has an inherently low background and enables precise chemical reactions through electrical control. Here, we report an advanced ECL system, termed ECLipse (ECL in paired signal electrode). We physically separated ECL generation from target detection: These two processes were carried out in isolated chambers and coupled through an electrode. The strategy allowed us to minimize cross-chemical reactions, design electrodes for high ECL signals, and integrate multiple sensors in a chip. As a proof of concept, we implemented an eight-plex ECLipse and applied it to detect host factors in human plasma. ECLipse achieved higher signal-to-noise ratio than conventional ECL assays and was >7000-fold more sensitive than enzyme-linked immunosorbent assay. In a pilot clinical study, we could detect septic conditions by measuring host factors [i.e., interleukin-3 (IL-3), IL-6, and procalcitonin (PCT)]. ECLipse assay further revealed distinct IL-3 and IL-6 patterns in patients with severe acute respiratory syndrome coronavirus 2 infection.

Data capture and sharing in the COVID-19 pandemic: a cause for concern

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of—or representing a decreased appetite for—digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Social and psychological impact of the COVID-19 pandemic on UK medical and nursing students: protocol for a national medical and nursing student survey.

INTRODUCTION: Healthcare students have played a significant role in the National Health Service during the COVID-19 pandemic. We captured data on the well-being of medical students during the acute phase of the pandemic with the Social and Psychological Impact of COVID-19 on medical students: a national survey Evaluation (SPICE-19) study. We will evaluate changes in mental health and well-being of medical and nursing students 1 year after SPICE-19, in a cross-sectional study, to understand the impact of the pandemic, and inform well-being policies. METHODS AND ANALYSIS: This study will be a national, multi-institution, cross-discipline study. An online 53-item survey of demographics, mental health and well-being will be used to record responses. Students studying for a medical or nursing degree at any UK universities will be eligible to participate. The survey will be advertised through the Neurology and Neurosurgery Interest Group national network. Participation is anonymous and voluntary, with relevant mental health resources made available to participants. ETHICS AND DISSEMINATION: Ethical approval was granted by the University of Oxford Central University Research Ethics Committee (R75719/RE001) on 21 May 2021. Study findings will be presented at national and international meetings, and submitted for publication in a peer-reviewed journal.

Estimating the global and regional burden of meningitis in children caused by Haemophilus influenzae type b: A systematic review and meta-analysis.

Background: Haemophilus influenzae Type B (Hib) meningitis caused significant public health concern for children. Recent assessment in 2015 suggests vaccination has virtually eliminated invasive Hib diseases. However, many countries launched their programs after 2010, and few are yet to establish routine Hib immunisations. We therefore aimed to update the most recent global burden of Hib meningitis before the impact of COVID-19 pandemic, from 2010 to 2020, in order to aid future public health policies on disease management and prevention. Methods: Epidemiological data regarding Hib meningitis in children <5 years old were systematically searched and evaluated from PubMed and Scopus in August, 2020. We included studies published between 2010 and 2019 that reported incidence, prevalence, mortality, or case-fatality-ratio (CFR), and confirmation of meningitis by cerebrospinal fluid culture, with a minimum one year study period and ten cases. Each data was stratified by one study-year. Median study-year was used if information was not available. Quality of all studies were assessed using our adapted assessment criteria from Grading of Recommendations Assessment, Development and Evaluation (GRADE) and Study Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from National Heart, Lung and Blood Institute (NHLBI). We constructed and visually inspected a funnel plot of standard error by the incidence rate and performed an Egger's regression test to statistically assess publication bias. To ascertain incidence and CFR, we performed generalised linear mixed models on crude individual study estimates. Heterogeneity was assessed using I-squared statistics whilst further exploring heterogeneity by performing subgroup analysis. Results: 33 studies were identified. Pooled incidence of global Hib meningitis in children was 1.13 per 100 000-child-years (95% confidence interval (CI) = 0.80-1.59). Southeast Asian Region (SEAR) of World Health Organisation (WHO) region reported the highest incidence, and European Region (EUR) the lowest. Considering regions with three or more data, Western Pacific Region (WPR) had the highest incidence rate of 5.22 (95% CI = 3.12-8.72). Post-vaccination incidence (0.67 cases per 100 000-child-years, 95% CI = 0.48-0.94) was dramatically lower than Pre-vaccination incidence (4.84 cases per 100 000-child-years, 95% CI = 2.95-7.96). Pooled CFR in our meta-analysis was 11.21% (95% CI = 7.01-17.45). Eastern Mediterranean Region (EMR) had the highest CFR (26.92, 95% CI = 13.41-46.71) while EUR had the lowest (4.13, 95% CI = 1.73-9.54). However, considering regions with three or more data, African Region (AFR) had the highest CFR at 21.79% (95% CI = 13.65-32.92). Before the coronavirus disease 2019 (COVID-19) impact, the estimation for global Hib meningitis cases in 2020 is 7645 and 857 deaths. Conclusions: Global burden of Hib meningitis has markedly decreased, and most regions have implemented vaccination programs. Extrapolating population-at-risk from studies has possibly led to an underestimation. Continuous surveillance is necessary to monitor vaccination impact, resurgence, vaccine failures, strain variance, COVID-19 impact, and to track improvement of regional and global Hib meningitis mortality.

How to Use and Interpret the Results of a Platform Trial: Users' Guide to the Medical Literature.

Platform trials are a type of randomized clinical trial that allow simultaneous comparison of multiple intervention groups against a single control group that serves as a common control based on a prespecified interim analysis plan. The platform trial design enables introduction of new interventions after the trial is initiated to evaluate multiple interventions in an ongoing manner using a single overarching protocol called a master (or core) protocol. When multiple treatment candidates are available, rapid scientific therapeutic discoveries may be made. Platform trials have important potential advantages in creating an efficient trial infrastructure that can help address critical clinical questions as the evidence evolves. Platform trials have recently been used in investigations of evolving therapies for patients with COVID-19. The purpose of this Users' Guide to the Medical Literature is to describe fundamental concepts of platform trials and master protocols and review issues in the conduct and interpretation of these studies. This Users' Guide is intended to help clinicians and readers understand articles reporting on interventions evaluated using platform trial designs.

The Need for a Practical Approach to Evaluate the Effectiveness of COVID-19 Vaccines for Low- and Middle-Income Countries.

The global demand for coronavirus disease 2019 (COVID-19) vaccines currently far outweighs the available global supply and manufacturing capacity. As a result, securing doses of vaccines for low- and middle-income countries has been challenging, particularly for African countries. Clinical trial investigation for COVID-19 vaccines has been rare in Africa, with the only randomized clinical trials (RCTs) for COVID-19 vaccines having been conducted in South Africa. In addition to addressing the current inequities in the vaccine roll-out for low- and middle-income countries, there is a need to monitor the real-world effectiveness of COVID-19 vaccines in these regions. Although RCTs are the superior method for evaluating vaccine efficacy, the feasibility of conducting RCTs to monitor COVID-19 vaccine effectiveness during mass vaccine campaigns will likely be low. There is still a need to evaluate the effectiveness of mass COVID-19 vaccine distribution in a practical manner. We discuss how target trial emulation, the application of trial design principles from RCTs to the analysis of observational data, can be used as a practical, cost-effective way to evaluate real-world effectiveness for COVID-19 vaccines. There are several study design considerations that need to be made in the analyses of observational data, such as uncontrolled confounders and selection biases. Target trial emulation accounts for these considerations to improve the analyses of observational data. The framework of target trial emulation provides a practical way to monitor the effectiveness of mass vaccine campaigns for COVID-19 using observational data.

Moving forward in clinical research with master protocols.

With billions of dollars in research and development (R&D) funding continuing to be invested, the novel coronavirus disease 2019 (COVID-19) has become into a singular focus for the scientific community. However, the collective response from the scientific communities have seen poor return on investment, particularly for therapeutic research for COVID-19, revealing the existing weaknesses and inefficiencies of the clinical trial enterprise. In this article, we argue for the importance of structural changes to existing research programs for clinical trials in light of the lessons learned from COVID-19.

Effect of Early Treatment With Hydroxychloroquine or Lopinavir and Ritonavir on Risk of Hospitalization Among Patients With COVID-19: The TOGETHER Randomized Clinical Trial.

Importance: Data on the efficacy of hydroxychloroquine or lopinavir-ritonavir for the treatment of high-risk outpatients with COVID-19 in developing countries are needed. Objective: To determine whether hydroxychloroquine or lopinavir-ritonavir reduces hospitalization among high-risk patients with early symptomatic COVID-19 in an outpatient setting. Design, Setting, and Participants: This randomized clinical trial was conducted in Brazil. Recently symptomatic adults diagnosed with respiratory symptoms from SARS-CoV-2 infection were enrolled between June 2 and September 30, 2020. The planned sample size was 1476 patients, with interim analyses planned after 500 patients were enrolled. The trial was stopped after the interim analysis for futility with a sample size of 685 patients. Statistical analysis was performed in December 2020. Interventions: Patients were randomly assigned to hydroxychloroquine (800 mg loading dose, then 400 mg daily for 9 days), lopinavir-ritonavir (loading dose of 800 mg and 200 mg, respectively, every 12 hours followed by 400 mg and 100 mg, respectively, every 12 hours for the next 9 days), or placebo. Main Outcomes and Measures: The primary outcomes were COVID-19-associated hospitalization and death assessed at 90 days after randomization. COVID-19-associated hospitalization was analyzed with a Cox proportional hazards model. The trial included the following secondary outcomes: all-cause hospitalization, viral clearance, symptom resolution, and adverse events. Results: Of 685 participants, 632 (92.3%) self-identified as mixed-race, 377 (55.0%) were women, and the median (range) age was 53 (18-94) years. A total of 214 participants were randomized to hydroxychloroquine; 244, lopinavir-ritonavir; and 227, placebo. At first interim analysis, the data safety monitoring board recommended stopping enrollment of both hydroxychloroquine and lopinavir-ritonavir groups because of futility. The proportion of patients hospitalized for COVID-19 was 3.7% (8 participants) in the hydroxychloroquine group, 5.7% (14 participants) in the lopinavir-ritonavir group, and 4.8% (11 participants) in the placebo group. We found no significant differences between interventions for COVID-19-associated hospitalization (hydroxychloroquine: hazard ratio [HR], 0.76 [95% CI, 0.30-1.88]; lopinavir-ritonavir: HR, 1.16 [95% CI, 0.53-2.56] as well as for the secondary outcome of viral clearance through day 14 (hydroxychloroquine: odds ratio [OR], 0.91 [95% CI, 0.82-1.02]; lopinavir-ritonavir: OR, 1.04 [95% CI, 0.94-1.16]). At the end of the trial, there were 3 fatalities recorded, 1 in the placebo group and 2 in the lopinavir-ritonavir intervention group. Conclusions and Relevance: In this randomized clinical trial, neither hydroxychloroquine nor lopinavir-ritonavir showed any significant benefit for decreasing COVID-19-associated hospitalization or other secondary clinical outcomes. This trial suggests that expedient clinical trials can be implemented in low-income settings even during the COVID-19 pandemic. Trial Registration: ClinicalTrials.gov Identifier: NCT04403100.

How COVID-19 has fundamentally changed clinical research in global health.

COVID-19 has had negative repercussions on the entire global population. Despite there being a common goal that should have unified resources and efforts, there have been an overwhelmingly large number of clinical trials that have been registered that are of questionable methodological quality. As the final paper of this Series, we discuss how the medical research community has responded to COVID-19. We recognise the incredible pressure that this pandemic has put on researchers, regulators, and policy makers, all of whom were doing their best to move quickly but safely in a time of tremendous uncertainty. However, the research community's response to the COVID-19 pandemic has prominently highlighted many fundamental issues that exist in clinical trial research under the current system and its incentive structures. The COVID-19 pandemic has not only re-emphasised the importance of well designed randomised clinical trials but also highlighted the need for large-scale clinical trials structured according to a master protocol in a coordinated and collaborative manner. There is also a need for structures and incentives to enable faster data sharing of anonymised datasets, and a need to provide similar opportunities to those in high-income countries for clinical trial research in low-resource regions where clinical trial research receives considerably less research funding.

Clinical Trial Data Sharing for COVID-19-Related Research.

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

The Landscape of Emerging Randomized Clinical Trial Evidence for COVID-19 Disease Stages: A Systematic Review of Global Trial Registries.

PURPOSE: A multitude of randomized controlled trials (RCTs) have emerged in response to the novel coronavirus disease (COVID-19) pandemic. Understanding the distribution of trials among various settings is important to guide future research priorities and efforts. The purpose of this review was to describe the emerging evidence base of COVID-19 RCTs by stages of disease progression, from pre-exposure to hospitalization. METHODS: We collated trial data across international registries: ClinicalTrials.gov; International Standard Randomised Controlled Trial Number Registry; Chinese Clinical Trial Registry; Clinical Research Information Service; EU Clinical Trials Register; Iranian Registry of Clinical Trials; Japan Primary Registries Network; German Clinical Trials Register (up to 7 October 2020). Active COVID-19 RCTs in international registries were eligible for inclusion. We extracted trial status, intervention(s), control, sample size, and clinical context to generate descriptive frequencies, network diagram illustrations, and statistical analyses including odds ratios and the Mann-Whitney U-test. RESULTS: Our search identified 11503 clinical trials registered for COVID-19 and identified 2388 RCTs. After excluding 45 suspended RCTs and 480 trials with unclear or unreported disease stages, 1863 active RCTs were included and categorized into four broad disease stages: pre-exposure (n=107); post-exposure (n=208); outpatient treatment (n=266); hospitalization, including the intensive care unit (n=1376). Across all disease stages, most trials had two arms (n=1500/1863, 80.52%), most often included (hydroxy)chloroquine (n=271/1863, 14.55%) and were US-based (n=408/1863, 21.90%). US-based trials had lower odds of including (hydroxy)chloroquine than trials in other countries (OR: 0.63, 95% CI: 0.45-0.90) and similar odds of having two arms compared to other geographic regions (OR: 1.05, 95% CI: 0.80-1.38). CONCLUSION: There is a marked difference in the number of trials across settings, with limited studies on non-hospitalized persons. Focus on pre- and post-exposure, and outpatients, is worthwhile as a means of reducing infections and lessening the health, social, and economic burden of COVID-19.

The Factors That Influenced Medical Students' Decision to Work Within the NHS During the COVID-19 Pandemic-A National, Cross-sectional Study.

PURPOSE: Medical students across the United Kingdom helped the National Health Service (NHS) with the increased workload caused by the COVID-19 pandemic. This study was conducted to better understand the implications of COVID-19 on the intricate relationship between the psychological wellbeing of students and working within the NHS. METHOD: This was a cross-sectional, national UK study analyzing the self-reported pandemic anxiety scale (PAS) of participants during the pandemic, using an online questionnaire. RESULTS: 25.2% of participants worked within the NHS. Working significantly reduced anxiety levels of participants. Concerns around the training and information provided on personal protective equipment (PPE) (odds ratio [OR] 2.57, 95% confidence interval [CI] OR: 1.93, 3.43) (P < 0.001) and Ethnicity (OR 2.15, 95% CI OR: 1.54, 2.99) (P < 0.001) were the most significant covariates affecting the likelihood of working. CONCLUSION: Working during the pandemic was influenced by age, ethnicity, and information surrounding PPE. On average those who worked experienced less anxiety and were more cheerful and energetic.

Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Correction to: Early Treatment of COVID-19 Disease: A Missed Opportunity.

[This corrects the article DOI: 10.1007/s40121-020-00349-8.].

Early Treatment of COVID-19 Disease: A Missed Opportunity.

Antivirals have demonstrated efficacy in treating other infectious diseases in early stages of disease, reducing morbidity, mortality, and the likelihood of onward transmission. At the time of writing, more than 1900 clinical trials are registered globally to assess the efficacy and safety of candidate therapeutics for COVID-19. The majority of these trials are designed to evaluate the comparative efficacy and safety of candidate therapeutics for the treatment of COVID-19 to prevent death among populations of hospitalized patients with advanced disease. Yet, emerging epidemiological evidence now indicates that the majority of those infected with the SARS-CoV-2, while still infectious, experience minimal or mild disease symptomology. Like HIV and hepatitis C that pioneered treatment as prevention, there is a missed opportunity for trials of early pharmaceutical intervention for COVID-19 disease evaluating not only reductions in morbidity and mortality but also transmissibility. We discuss this clinical research gap within an historical context of viral treatment as prevention for HIV and hepatitis C, and comment on the challenges and opportunities for clinical research of candidate therapeutics for early COVID-19 disease.

Accelerating Clinical Evaluation of Repurposed Combination Therapies for COVID-19.

As the global COVID-19 pandemic continues, unabated and clinical trials demonstrate limited effective pharmaceutical interventions, there is a pressing need to accelerate treatment evaluations. Among options for accelerated development is the evaluation of drug combinations in the absence of prior monotherapy data. This approach is appealing for a number of reasons. First, combining two or more drugs with related or complementary therapeutic effects permits a multipronged approach addressing the variable pathways of the disease. Second, if an individual component of a combination offers a therapeutic effect, then in the absence of antagonism, a trial of combination therapy should still detect individual efficacy. Third, this strategy is time saving. Rather than taking a stepwise approach to evaluating monotherapies, this strategy begins with testing all relevant therapeutic options. Finally, given the severity of the current pandemic and the absence of treatment options, the likelihood of detecting a treatment effect with combination therapy maintains scientific enthusiasm for evaluating repurposed treatments. Antiviral combination selection can be facilitated by insights regarding SARS-CoV-2 pathophysiology and cell cycle dynamics, supported by infectious disease and clinical pharmacology expert advice. We describe a clinical evaluation strategy using adaptive combination platform trials to rapidly test combination therapies to treat COVID-19.