Statistical reproducibility of meta-analysis research claims for medical mask use in community settings to prevent COVID infection (preprint)

The coronavirus pandemic (COVID) has been an exceptional test of current scientific evidence that inform and shape policy. Many US states, cities, and counties implemented public orders for mask use on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. P-value plotting was used to evaluate statistical reproducibility of meta-analysis research claims of a benefit for medical (surgical) mask use in community settings to prevent COVID infection. Eight studies (seven meta-analyses, one systematic review) published between 1 January 2020 and 7 December 2022 were evaluated. Base studies were randomized control trials with outcomes of medical diagnosis or laboratory-confirmed diagnosis of viral (Influenza or COVID) illness. Self-reported viral illness outcomes were excluded because of awareness bias. No evidence was observed for a medical mask use benefit to prevent viral infections in six p-value plots (five meta-analyses and one systematic review). Research claims of no benefit in three meta-analyses and the systematic review were reproduced in p-value plots. Research claims of a benefit in two meta-analyses were not reproduced in p-value plots. Insufficient data were available to construct p-value plots for two meta-analyses because of overreliance on self-reported outcomes. These findings suggest a benefit for medical mask use in community settings to prevent viral, including COVID infection, is unproven.

Reproducibility of health claims in meta-analysis studies of COVID quarantine (stay-at-home) orders (preprint)

The coronavirus pandemic (COVID) has been an extraordinary test of modern government scientific procedures that inform and shape policy. Many governments implemented COVID quarantine (stay-at-home) orders on the notion that this nonpharmaceutical intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. The overall research capacity response to COVID since late 2019 has been massive. Given lack of research transparency, only a small fraction of published research has been judged by others to be reproducible before COVID. Independent evaluation of published meta-analysis on a common research question can be used to assess the reproducibility of a claim coming from that field of research. We used a p-value plotting statistical method to independently evaluate reproducibility of specific research claims made in four meta-analysis studies related to benefits/risks of COVID quarantine orders. Outcomes we investigated included: mortality, mental health symptoms, incidence of domestic violence, and suicidal ideation (thoughts of killing yourself). Three of the four meta-analyses that we evaluated (mortality, mental health symptoms, incidence of domestic violence) raise further questions about benefits/risks of this form of intervention. The fourth meta-analysis study (suicidal ideation) is unreliable. Given lack of research transparency and irreproducibility of published research, independent evaluation of meta-analysis studies using p-value plotting is offered as a way to strengthen or refute (falsify) claims made in COVID research.

ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes.

A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.

GOVERNING CLIMATE CHANGE AT THE G20 ROME AND UN GLASGOW SUMMITS AND BEYOND

How and why does the Group of 20 (G20) work, both alone and together with the United Nations (UN), to advance the effective global governance of climate change, especially in 2021 and beyond? G20 summit performance on climate change has increased since 2008 as measured by the six major dimensions of governance, but not by the results in net emissions reduced. G20 efforts to spur performance at subsequent UN climate summits has varied, from substantial at G20 Pittsburgh for UN Copenhagen in 2009, to limited at G20 Antalya for UN Paris in 2015, and to strong at G20 Rome for UN Glasgow in 2021. G20 efforts have been spurred by the physical climate shock-activated vulnerabilities experienced by G20 members in the lead-up to G20 and UN summits, especially from escalating extreme weather events, but have been constrained by diversionary shocks from finance in 2008–09, terrorism and migration in 2015, and COVID-19 in 2020–21. Also important were the personal commitments of, and domestic political support within, G20 and UN summit hosts, especially regarding the G20 and UN summits uniquely chaired by Group of 7 (G7) members Italy and the United Kingdom in 2021. Yet, the unprecedented combined G20-UN supply of global climate governance in 2021 fell even further behind the proliferating global demand to control climate change. To close the gap, the G20 should invite the heads of the major multilateral environmental organizations to participate in G20 summits, hold more environment ministers’ meetings each year, and mount an annual climate-focused summit at the UN General Assembly. © 2022. International Organisations Research Journal. All Rights Reserved.

Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.

Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( AUC = 0.67 [0.58 to 0.76]; accuracy = 77 % ). Model II performed the best ( AUC = 0.78 [0.71 to 0.86]; accuracy = 81 % ). Model III was equivalent ( AUC = 0.75 [0.67 to 0.84]; accuracy = 80 % ). Both models II and III outperformed model I ( AUC difference = 0.11 [0.02 to 0.19], p = 0.01 ; AUC difference = 0.08 [0.01 to 0.15], p = 0.04 , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

Risk factors for antimicrobial resistance among Staphylococcus isolated from pets living with a patient diagnosed with methicillin-resistant Staphylococcus aureus infection.

It has been suggested that pets play a critical role in the maintenance of methicillin-resistant (MR) and multidrug-resistant (MDR) Staphylococcus spp. in the household. We examined risk factors for carriage of antimicrobial-resistant coagulase-positive staphylococci, with particular attention to Staphylococcus aureus and Staphylococcus pseudintermedius isolated from pets living in households of people diagnosed with methicillin-resistant S. aureus (MRSA) skin or soft-tissue infection. We analyzed data collected cross-sectionally from a study conducted in 2012 that evaluated the transmission of MRSA and other staphylococci from humans, their pets and the environment (Pets and Environmental Transmission of Staphylococci [PETS] study). We used unadjusted and adjusted stratified logistic regression analyses with household-clustered standard errors to evaluate the association between demographic, healthcare-related, contact-related and environmental risk factors and MDR Staphylococcus spp. isolated from dogs and cats. Staphylococcal isolates obtained from dogs (n = 63) and cats (n = 47) were included in these analyses. The use of oral or injectable antimicrobials by the pets during the prior year was the main risk factor of interest. Based on our results, 50% (12/24) of S. aureus, 3.3% (1/30) of S. pseudintermedius and 25% (14/56) of other coagulase-positive staphylococci (CPS) were determined to be MDR. S. aureus isolates were more likely to be MDR compared with S. pseudintermedius. We did not find a significant statistical association between the use of oral or injectable antimicrobials in the prior year and the presence of MDR bacteria. The results suggest that drivers of antimicrobial resistance in household staphylococci may vary by bacterial species, which could have implications for one health intervention strategies for staphylococci and inform the investigation of other reverse zoonoses, such as COVID-19.

SARS-CoV-2 Environmental Contamination in Hospitalized COVID-19 Patients' Rooms

Background. The correlation between SARS-CoV-2 RNA and infectious viral contamination of the hospital environment is poorly understood. Methods. housed in a dedicated COVID-19 unit at an academic medical center. Environmental samples were taken within 24 hours of the first positive SARS-CoV-2 test (day 1) and again on days 3, 6, 10 and 14. Patients were excluded if samples were not obtained on days 1 and 3. Surface samples were obtained with flocked swabs pre-moistened with viral transport media from seven locations inside (bedrail, sink, medical prep area, room computer, exit door handle) and outside the room (nursing station computer). RNA extractions and RT-PCR were completed on all samples. RT-PCR positive samples were used to inoculate Vero E6 cells for 7 days and monitored for cytopathic effect (CPE). If CPE was observed, RT-PCR was used to confirm the presence of SARS-CoV-2. Results. We enrolled 14 patients (Table 1, Patient Characteristics) between October 2020 and May 2021. A total of 243 individual samples were obtained - 97 on day 1, 98 on day 3, 34 on day 6, and 14 on day 10. Overall, 18 (7.4%) samples were positive via RT-PCR - 9 from bedrails (12.9%), 4 from sinks (11.4%), 4 from room computers (11.4%) and 1 from the exit door handle (2.9%). Notably, all medical prep and nursing station computer samples were negative (Figure 1). Of the 18 positive samples, 5 were from day 1, 10 on day 3, 1 on day 6 and 2 on day 10. Only one sample, obtained from the bedrails of a symptomatic patient with diarrhea and a fever on day 3, was culture-positive (Figure 2). Conclusion. Overall, the amount of environmental contamination of viable SARS-CoV-2 virus in rooms housing COVID-19 infected patients was low. As expected, more samples were considered contaminated via RT-PCR compared to cell culture, supporting the conclusion that the discovery of genetic material in the environment is not an indicator of contamination with live infectious virus. More studies including RT-PCR and viral cell culture assays are needed to determine the significance of discovering SARS-CoV-2 RNA versus infectious virus in the clinical environment.

From Silos to Synergies: G20 Governance of the SDGs, Climate Change & Digitalization

How well and why have Group of 20 (G20) summits advanced Agenda 2030's sustainable development goals (SDGs) in a synergistic way, with climate change and digitization at the core? An answer to this urgent, indeed existential, question comes from a systematic analysis of G20 summit governance of the SDGs, climate change and digitization to assess the ambition and appropriateness of advances within each pillar and the synergistic links among them. This analysis examines G20 governance of the SDGs, sustainable development, climate change and digitization across the major dimensions of performance and evaluates how performance has changed and become synergistic with the advent of the SDGs in 2015 and the shock of the COVID-19 crisis in 2020. The latter has shown the need to prevent global ecological crises and spurred the digitization of the economy, society and health. Yet, G20 summit governance has largely remained in separate silos, doing little to use the digital revolution to address climate change or reach the SDGs. This highlights the need for G20 leaders to forge links at their future summits by mainstreaming the SDGs and mobilizing the digital revolution and climate action for future health and well-being.

Prediction and analysis of functional RNA structures within the integrative genomics viewer.

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results-in conjunction with probing data and other annotations-in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast 'omics' resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

The impact of school opening model on SARS-CoV-2 community incidence and mortality.

The role that traditional and hybrid in-person schooling modes contribute to the community incidence of SARS-CoV-2 infections relative to fully remote schooling is unknown. We conducted an event study using a retrospective nationwide cohort evaluating the effect of school mode on SARS-CoV-2 cases during the 12 weeks after school opening (July-September 2020, before the Delta variant was predominant), stratified by US Census region. After controlling for case rate trends before school start, state-level mitigation measures and community activity level, SARS-CoV-2 incidence rates were not statistically different in counties with in-person learning versus remote school modes in most regions of the United States. In the South, there was a significant and sustained increase in cases per week among counties that opened in a hybrid or traditional mode versus remote, with weekly effects ranging from 9.8 (95% confidence interval (CI) = 2.7-16.1) to 21.3 (95% CI = 9.9-32.7) additional cases per 100,000 persons, driven by increasing cases among 0-9 year olds and adults. Schools can reopen for in-person learning without substantially increasing community case rates of SARS-CoV-2; however, the impacts are variable. Additional studies are needed to elucidate the underlying reasons for the observed regional differences more fully.

Early Experience With Neutralizing Monoclonal Antibody Therapy For COVID-19 (preprint)

Importance Neutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 and/or hospitalization. Objective To describe our experience and patient outcomes of almost 3,000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York. Design, Setting, and Participants This is a descriptive study of adult patients, who received MAB therapy between November 20, 2020, to January 31, 2021, and a retrospective cohort survival analysis comparing patients who received MAB therapy prior to admission versus those who did not. Main outcomes and measures The primary outcome was in-hospital mortality; additional evaluations included ED utilization and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy. Results During the study period, 2818 (1412 [50.1%]) male, (1406 [49.9%] female) adult patients median age 67 years (IQR 58-74) with symptomatic COVID-19 received MAB infusion. Following therapy and within 28 days of COVID-19 test, 145 patients (5.1%) were hospitalized and compared with 200 controls who were eligible for but did not receive MAB therapy, and were hospitalized. In the MAB group, 16 (11%) patients met the primary outcome, versus 21 (10.5%) in the control group (not significant, log rank p-value = 0.41). In an unadjusted proportional hazards model, no significant association was found between pre-hospitalization MAB use and time to the primary end point (HR 1.38, 95% CI 0.696-2.719) as well as in models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (CCI) (HR 1.22, 95% CI 0.573-2.59), and with inverse probability weighting according to propensity scores (HR 1.19, 95% CI 0.619-2.29). Reduced hospitalization rate was related to the time between symptom onset and MAB therapy (4.4% within 0-4 days, 5% within 5-7 days, and 6.1% within [≥]8 days) however, this finding did not reach statistical significance (p-value = 0.15). Conclusions and relevance Establishing the capability to provide MAB infusion therapy requires significant planning and coordination. While this therapy may be an important treatment option for early mild to moderate COVID-19 in high-risk patients, further investigations are needed to define the optimal timing of MAB treatment.

Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND METHODS: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. RESULTS: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively. CONCLUSIONS: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth (preprint)

Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% [11.05%-12.47%] and 0.77 [0.70-0.82] for average of expert readers, and 9.56%-9.78% [8.83%-10.22%] and 0.78-0.81 [0.73-0.85] for the CNN, respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.