Background:A shallow active layer of soil above the permafrost thaws during the summer months which promotes microbial growth and releases previously confined pathogens which result in bacterial epidemics in circumpolar regions. Furthermore, these permafrost sources harbor several antibiotic resistance genes (ARGs) which may disseminate and pose a challenge for pharmacologists worldwide.Aims:The authors examined the potential association between climate change-induced permafrost thawing, and the resulting release of antibiotic-resistant pathogens, as well as the potential impact this can have on global healthcare systems in the long run.Methodology:A cursory abstract screening was done to rule out any articles that did not have to do with viral pathogens caused by melting permafrost. Articles that were not available in English or that our institutions library did not have full-text access were weeded out by a secondary screen.Results:A comprehensive analysis of 13 relevant studies successfully revealed a wide variety of bacterial genera, including Staphylococcus spp., Pseudomonas spp., Acinetobacter spp., and Achromobacter spp., along with a total of 1043 antibiotic resistance genes (ARGs), with most pertaining to aminoglycosides and beta-lactams, offering resistance via diverse mechanisms such as efflux pumps and enzymatic modifications, within the permafrost isolates. Additionally, mobile genetic elements (MGEs) housing antibiotic resistance genes (ARGs) and virulence factor genes (VFGs), including plasmids and transposons, were also discovered.Conclusion:Permafrost thawing is an underrated healthcare challenge warranting the need for further articles to highlight it alongside concerted efforts for effective mitigation.

Introduction: European forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is the European Spruce Bark Beetle (Ips typographus L.). Effective management of I. typographus outbreaks necessitates the timely detection of recently attacked spruce trees, which is challenging given the difficulty in spotting symptoms on infested tree crowns. Bark beetle population density is one of many factors that can affect infestation rate and symptoms development. This study compares the appearance of early symptoms in endemic and epidemic bark beetle populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery. Methods: In spring of 2022, host colonization by bark beetles was induced on groups of spruce trees growing in 10 sites in the Southern Alps, characterized by different population density (5 epidemic and 5 endemic). A multispectral sensor mounted on a drone captured images once every 2 weeks, from May to August 2022. The analyses of a set of vegetational indices allowed the actual infested trees' reflectance features and symptoms appearance to be observed at each site, comparing them with those of unattacked trees. Results: Results show that high bark beetles population density triggers a more rapid and intense response regarding the emergence of symptoms. Infested trees were detected at least 1 month before symptoms became evident to the human eye (red phase) in epidemic sites, while this was not possible in endemic sites. Key performing vegetation indices included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Vegetation Index, with a correction factor of 0.44), and NDRE (Normalized Difference Red Edge index). Discussion: This early-detection approach could allow automatic diagnosis of bark beetles' infestations and provide useful guidance for the management of areas suffering pest outbreaks.

Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's morning peak of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.