Highlighting the Compound Risk of COVID-19 and Environmental Pollutants Using Geospatial Technology

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.The authors acknowledge financial support from the Spanish Government, Grant RTI2018-354 094336-B-I00 (MCIU/AEI/FEDER, UE), the Spanish Carlos III Health Institute, COV 20/01213, and the Basque Government, Grant IT1207-19

Severe Acute Respiratory Infection-Preparedness: Protocol for a Multicenter Prospective Cohort Study of Viral Respiratory Infections

OBJECTIVES: Respiratory virus infections cause significant morbidity and mortality ranging from mild uncomplicated acute respiratory illness to severe complications, such as acute respiratory distress syndrome, multiple organ failure, and death during epidemics and pandemics. We present a protocol to systematically study patients with severe acute respiratory infection (SARI), including severe acute respiratory syndrome coronavirus 2, due to respiratory viral pathogens to evaluate the natural history, prognostic biomarkers, and characteristics, including hospital stress, associated with clinical outcomes and severity. DESIGN: Prospective cohort study. SETTING: Multicenter cohort of patients admitted to an acute care ward or ICU from at least 15 hospitals representing diverse geographic regions across the United States. PATIENTS: Patients with SARI caused by infection with respiratory viruses that can cause outbreaks, epidemics, and pandemics. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measurements include patient demographics, signs, symptoms, and medications; microbiology, imaging, and associated tests; mechanical ventilation, hospital procedures, and other interventions; and clinical outcomes and hospital stress, with specimens collected on days 0, 3, and 7-14 after enrollment and at discharge. The primary outcome measure is the number of consecutive days alive and free of mechanical ventilation (VFD) in the first 30 days after hospital admission. Important secondary outcomes include organ failure-free days before acute kidney injury, shock, hepatic failure, disseminated intravascular coagulation, 28-day mortality, adaptive immunity, as well as immunologic and microbiologic outcomes. CONCLUSIONS: SARI-Preparedness is a multicenter study under the collaboration of the Society of Critical Care Medicine Discovery, Resilience Intelligence Network, and National Emerging Special Pathogen Training and Education Center, which seeks to improve understanding of prognostic factors associated with worse outcomes and increased resource utilization. This can lead to interventions to mitigate the clinical impact of respiratory virus infections associated with SARI

Angiopoietin-Like4 Is a Novel Marker of COVID-19 Severity

IMPORTANCE: Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES: To assess the role of ANGPTL4 in COVID-19-related outcomes. DESIGN SETTING AND PARTICIPANTS: Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES: Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS: Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log CONCLUSIONS AND RELEVANCE: ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Soil Profile Studies under Different Orchard Management System in Chhindwara District of Madhya Pradesh, India

The investigation conducted at the laboratory of Rajiv Gandhi South Campus, Banaras Hindu University, Barkachha; Mirzapur during the year 2021-22. The objective of this study was to characterize the physicochemical properties and macro-nutrient availability of orchard soil depth under (0–15, 15-30, 30-60, 60-90 cm) of different blocks of Chhindwara District, Madhya Pradesh. By using GPS camera, 52 soil samples was collected from different orchard of different blocks of district. The physical and chemical properties of soil were critically analyzed. The pH of orchard soil was found slightly acidic to alkaline in nature. Total soluble salts were found less than 1dSm-1 . Organic carbon was observed high in upper surface (0-15 cm) and decreased with the increasing depth of the profile. Bulk density (1.28 – 1.97 Mg m3) and particle densities (2.49 – 2.65 Mg m3) were found in normal ranged for all the layers. The samples were found under low water holding capacity (30.87-65.24%) of all the orchards. Nitrogen was observed in low (110.30 kg ha-1) to medium (468.28 kg ha-1) range in the different layers (0-15, 15-30, 30-60, 60-90cm). Phosphorus and sulphur content were found medium (8.56-11.78 kg ha-1 and 10.6-13.9 kg ha-1) in surface layer and low (7.10 – 10.65 and 8.4-12.7 kg ha-1) for sub-surface layers. Available                                   potassium was determined in high range (224.0-378.8 kg ha-1) for all the layers (0-15, 15-30, 30-60, 60-90 cm)

Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms

Myopia progression risk assessment score (MPRAS): a promising new tool for risk stratification

Abstract Timely identification of individuals “at-risk” for myopia progression is the leading requisite for myopia practice as it aids in the decision of appropriate management. This study aimed to develop ‘myopia progression risk assessment score’ (MPRAS) based on multiple risk factors (10) to determine whether a myope is “at-risk” or “low-risk” for myopia progression. Two risk-score models (model-1: non-weightage, model-2: weightage) were developed. Ability of MPRAS to diagnose individual “at-risk” for myopia progression was compared against decision of five clinicians in 149 myopes, aged 6–29 years. Using model-1 (no-weightage), further 7 sub-models were created with varying number of risk factors in decreasing step-wise manner (1a: 10 factors to 1g: 4 factors). In random eye analysis for model-1, the highest Youden’s J-index (0.63–0.65) led to the MPRAS cut-off score of 41.50–43.50 for 5 clinicians with a sensitivity ranging from 78 to 85% and specificity ranging from 79 to 87%. For this cut-off score, the mean area under the curve (AUC) between clinicians and the MPRAS model ranged from 0.89 to 0.90. Model-2 (weighted for few risk-factors) provided similar sensitivity, specificity, and AUC. Sub-model analysis revealed greater AUC with high sensitivity (89%) and specificity (94%) in model-1g that has 4 risk factors compared to other sub-models (1a–1f). All the MPRAS models showed good agreement with the clinician’s decision in identifying individuals “at-risk” for myopia progression