Enrichment of antibiotic resistant genes and pathogens in face masks from coastal environments

Fabrication of microplastic-free biomass-based masks: Enhanced multi-functionality with all-natural fibers

With the coronavirus-2019 epidemic, disposable surgical masks have become a common personal protective necessity. However, off-the-shelf masks have low filtration efficiency and short service life and can only physically isolate pathogens, easily leading to secondary infection and cross-infection between users. Additionally, they produce debris and microplastics, which can be inhaled by the human body and cause serious diseases. To address this, this study introduced a brand-new, microplastic-free, long-life, biodegradable, self-disinfecting, and gas-sensitive mask made of basal dialdehyde-chitosan crosslinked animal-collagen/plant composite fibers (CP-Mask) with an asymmetric bilayer structure using scalable paper-processing technology. The CP-Mask demonstrated outstanding filtration performance (95.9 %) for particulate matter with various sizes and constantly maintained filtration efficiency even after 20 friction cycles. The CP-Mask also exhibited stable and lasting antibacterial properties, with significant inhibition rates of 99.21 % for Staphylococcus aureus and 98.86 % for Escherichia coli and could effectively filter bacterial aerosols. In addition, CP-Mask realized the real-time detection of respiratory ammonia concentration and timely identified the ammonia level. The average response value was 68.26 %, and the average response time was 159.3 s, presenting good circulatory stability and is suitable for early diagnosis of ammonia-related diseases. Breakthrough, the origin of natural ingredients, fundamentally makes CP-Mask less likely to emit microplastics than commercially available masks and endows it with complete biodegradability in soil within three months, eliminating the risk of microplastic inhalation from the source. The proposed CP-Mask provides a new idea to facilitate personal health monitoring and portability of medical protection equipment regarding biocompatibility, biodegradability, self-disinfection, and ammonia sensing ability.

Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant Acinetobacter baumannii from whole genome sequencing and gene expression

Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susceptibility phenotypes based on WGS data. This study aimed to establish an artificial intelligence-based computational approach in predicting antimicrobial susceptibilities of multidrug-resistant Acinetobacter baumannii from WGS and gene expression data. Antimicrobial susceptibility testing (AST) was performed using the broth microdilution method for 10 antimicrobial agents. In silico multilocus sequence typing (MLST), antimicrobial resistance genes, and phylogeny based on cgSNP and cgMLST strategies were analyzed. High-throughput qPCR was performed to measure the expression level of antimicrobial resistance (AMR) genes. Most isolates exhibited a high level of resistance to most of the tested antimicrobial agents, with the majority belonging to the IC2/CC92 lineage. Phylogenetic analysis revealed undetected transmission events or local outbreaks. The percentage agreements between AMR phenotype and genotype ranged from 70.08% to 89.96%, with the coefficient of agreement (κ) extending from 0.025 and 0.881. The prediction of AST employed by deep neural network models achieved an accuracy of up to 98.64% on the testing data set. Additionally, several linear regression models demonstrated high prediction accuracy, reaching up to 86.15% within an error range of one gradient, indicating a linear relationship between certain gene expressions and the corresponding antimicrobial MICs. In conclusion, neural network-based predictions could be used as a tool for the surveillance of antimicrobial resistance in multidrug-resistant A. baumannii.

Marine plastisphere selectively enriches microbial assemblages and antibiotic resistance genes during long-term cultivation periods

Several studies have focused on identifying and quantifying suspended plastics in surface and subsurface seawater. Microplastics (MPs) have attracted attention as carriers of antibiotic resistance genes (ARGs) in the marine environment. Plastispheres, specific biofilms on MP, can provide an ideal niche to spread more widely through horizontal gene transfer (HGT), thereby increasing risks to ecosystems and human health. However, the microbial communities formed on different plastic types and ARG abundances during exposure time in natural marine environments remain unclear. Four types of commonly used MPs (polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC)) were periodically cultured (46, 63, and 102 d) in a field-based marine environment to study the co-selection of ARGs and microbial communities in marine plastispheres. After the first 63 d of incubation (p < 0.05), the initial 16S rRNA gene abundance of microorganisms in the plastisphere increased significantly, and the biomass subsequently decreased. These results suggest that MPs can serve as vehicles for various microorganisms to travel to different environments and eventually provide a niche for a variety of microorganisms. Additionally, the qPCR results showed that MPs selectively enriched ARGs. In particular, tetA, tetQ, sul1, and qnrS were selectively enriched in the PVC-MPs. The abundances of intl1, a mobile genetic element, was measured in all MP types for 46 d (5.22 × 10-5 ± 8.21 × 10-6 copies/16s rRNA gene copies), 63 d (5.90 × 10-5 ± 2.49 × 10-6 copies/16s rRNA gene copies), and 102 d (4.00 × 10-5 ± 5.11 × 10-6 copies/16s rRNA gene copies). Network analysis indicated that ARG profiles co-occurred with key biofilm-forming bacteria. This study suggests that the selection of ARGs and their co-occurring bacteria in MPs could potentially accelerate their transmission through HGT in natural marine plastics.

The hind information: Exploring the impact of physical damage on mask microbial composition in the aquatic environment

Due to poor management and the lack of environmental awareness, lots of masks (an emerging form of plastic pollution) are discarded into the environment during the COVID-19, thereby jeopardizing the health of humans and the environment. Our study introduces a novel perspective by examining the impact of physical damage on the microbial composition of masks in the water environment. We focus on the variations in biofilm formation on each layer of both damaged and undamaged masks, which allows us to understand more about the biofilm on each layer and the significant changes that occur when masks are physically damaged. Research has shown that the community structure of microorganisms on discarded masks can be altered in just ten days, showing an evolution from undifferentiated pioneer colonizing species ("non-picky") to adaptive dominant species ("picky"). Especially, considering that discarded masks were inevitably damaged, we found that the biomass on the damaged samples is 1.62-2.38 times higher than that of the undamaged samples, respectively. Moreover, the microbial community structure on it was also significantly different. Genes involved in biogeochemical cycles of nutrients are more enriched in damaged masks. When damaged, the colonization process and community structure in the middle layer significantly differ from those in the inner and outer layers and even enrich more pathogenic bacteria. Based on the above, it is evident that the environmental risk of masks cannot be assessed as a whole, and the middle layer carries a higher risk.