Over 950 billion (about 3.8 million tons) masks have been consumed in the last four years around the world to protect human beings from COVID-19 and air pollution. However, very few of these used masks are being recycled, with the majority of them being landfilled or incinerated. To address this issue, we propose a repurposing upcycling strategy by converting these polypropylene (PP)-based waste masks to high-performance thermally conductive nanocomposites (PP@G, where G refers to graphene) with exceptional electromagnetic interference shielding property. The PP@G is fabricated by loading tannic acid onto PP fibers via electrostatic self-assembling, followed by mixing with graphene nanoplatelets (GNPs). Because this strategy enables the GNPs to form efficient thermal and electrical conduction pathways along the PP fiber surface, the PP@G shows a high thermal conductivity of 87 W m^-1 K^-1 and exhibits an electromagnetic interference shielding effectiveness of 88 dB (1100 dB cm^-1), making it potentially applicable for heat dissipation and electromagnetic shielding in advanced electronic devices. Life cycle assessment and techno-economic assessment results show that our repurposing strategy has significant advantages over existing methods in reducing environmental impacts and economic benefits. This strategy offers a facile and promising approach to upcycling/repurposing of fibrous waste plastics.

Experts representing multiple societies and institutions across 14 countries have published guidance for computed tomography (CT) imaging in patients with residual lung abnormalities after COVID-19 illness.

Long COVID patients with abnormal cardiopulmonary PET/MR findings may be more likely to develop heart and lung diseases, according to new research published in the July issue of The Journal of Nuclear Medicine. The study—which included the largest cohort of long COVID subjects with cardiopulmonary symptoms with the longest follow-up duration—suggests that abnormal cardiopulmonary PET/MR should be considered a risk factor for future cardiac and pulmonary disease and that closer monitoring is warranted in these patients.

Coupling wastewater surveillance and a newly developed AI algorithm can help public health organizations more quickly predict potential outbreaks.

New research by the University of Sydney offers important insights into how and when new coronavirus variants arise in bats. 

 A first-of-its-kind global-to-local study has found that COVID-19’s impact varied remarkably across 920 locations worldwide, even within the same country. By analyzing data on cases, deaths, and healthy years lost between 2020 and 2021, the researchers revealed that broad national or regional averages may mask substantial local disparities. The study calls for more detailed, location-specific data to guide better public health decisions in future pandemics.

Since the start of the SARS-CoV-2 pandemic, several variants of the virus have developed into Variants of Concern (VOCs), as classified by the World Health Organization (WHO). VOCs are virus variants that are predicted or known to cause large waves of infections due to their altered phenotypic characteristics and with risk of altering disease severity, reducing vaccine effectiveness or otherwise leading to increased burden of health care systems. The CoVerage web platform for genomic surveillance of the SARS-CoV-2 virus enables a rapid, computational identification and characterization of potential Variants of Interest (pVOIs), with a lead time of almost three months before their WHO designation as a VOC or as related variant categories and predicts their ability to escape existing immunity acquired by prior vaccinations or infections. Researchers led by Alice McHardy have successfully demonstrated this in a comprehensive analysis published in Nature Communications. Early detection of VOCs is particularly important for vaccine development in order to ensure vaccine protection against new virus variants.