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666400.9980Addressing the global challenge of bacterial drug resistance: insights, strategies, and future directions. The COVID-19 pandemic underscored bacterial resistance as a critical global health issue, exacerbated by the increased use of antibiotics during the crisis. Notwithstanding the pandemic's prevalence, initiatives to address bacterial medication resistance have been inadequate. Although an overall drop in worldwide antibiotic consumption, total usage remains substantial, requiring rigorous regulatory measures and preventive activities to mitigate the emergence of resistance. Although National Action Plans (NAPs) have been implemented worldwide, significant disparities persist, particularly in low- and middle-income countries (LMICs). Settings such as farms, hospitals, wastewater treatment facilities, and agricultural environments include a significant presence of Antibiotic Resistant Bacteria (ARB) and antibiotic-resistance genes (ARG), promoting the propagation of resistance. Dietary modifications and probiotic supplementation have shown potential in reshaping gut microbiota and reducing antibiotic resistance gene prevalence. Combining antibiotics with adjuvants or bacteriophages may enhance treatment efficacy and mitigate resistance development. Novel therapeutic approaches, such as tailored antibiotics, monoclonal antibodies, vaccines, and nanoparticles, offer alternate ways of addressing resistance. In spite of advancements in next-generation sequencing and analytics, gaps persist in comprehending the role of gut microbiota in regulating antibiotic resistance. Effectively tackling antibiotic resistance requires robust policy interventions and regulatory measures targeting root causes while minimizing public health risks. This review provides information for developing strategies and protocols to prevent bacterial colonization, enhance gut microbiome resilience, and mitigate the spread of antibiotic resistance.202540066274
659810.9980Shallow shotgun sequencing of healthcare waste reveals plastic-eating bacteria with broad-spectrum antibiotic resistance genes. The burgeoning crises of antimicrobial resistance and plastic pollution are converging in healthcare settings, presenting a complex challenge to global health. This study investigates the microbial populations in healthcare waste to understand the extent of antimicrobial resistance and the potential for plastic degradation by bacteria. Our metagenomic analysis, using both amplicon and shallow shotgun sequencing, provided a comprehensive view of the taxonomic diversity and functional capacity of the microbial consortia. The viable bacteria in healthcare waste samples were analyzed employing full-length 16S rRNA sequencing, revealing a diverse bacterial community dominated by Firmicutes and Proteobacteria phyla. Notably, Proteus mirabilis VFC3/3 and Pseudomonas sp. VFA2/3 were detected, while Stenotrophomonas maltophilia VFV3/2 surfaced as the predominant species, holding implications for the spread of hospital-acquired infections and antimicrobial resistance. Antibiotic susceptibility testing identified multidrug-resistant strains conferring antimicrobial genes, including the broad-spectrum antibiotic carbapenem, underscoring the critical need for improved waste management and infection control measures. Remarkably, we found genes linked to the breakdown of plastic that encoded for enzymes of the esterase, depolymerase, and oxidoreductase classes. This suggests that specific bacteria found in medical waste may be able to reduce the amount of plastic pollution that comes from biological and medical waste. The information is helpful in formulating strategies to counter the combined problems of environmental pollution and antibiotic resistance. This study emphasises the importance of monitoring microbial communities in hospital waste in order to influence waste management procedures and public health policy. The findings highlight the need for a multidisciplinary approach to mitigate the risks associated with antimicrobial resistance and plastic waste, especially in hospital settings where they intersect most acutely.202539551377
660120.9979Use of Wastewater to Monitor Antimicrobial Resistance Trends in Communities and Implications for Wastewater-Based Epidemiology: A Review of the Recent Literature. Antimicrobial resistance (AMR) presents a global health challenge, necessitating comprehensive surveillance and intervention strategies. Wastewater-based epidemiology (WBE) is a promising tool that can be utilized for AMR monitoring by offering population-level insights into microbial dynamics and resistance gene dissemination in communities. This review (n = 29 papers) examines the current landscape of utilizing WBE for AMR surveillance with a focus on methodologies, findings, and gaps in understanding. Reported methods from the reviewed literature included culture-based, PCR-based, whole genome sequencing, mass spectrometry, bioinformatics/metagenomics, and antimicrobial susceptibility testing to identify and measure antibiotic-resistant bacteria and antimicrobial resistance genes (ARGs) in wastewater, as well as liquid chromatography-tandem mass spectrometry to measure antibiotic residues. Results indicate Escherichia coli, Enterococcus spp., and Pseudomonas spp. are the most prevalent antibiotic-resistant bacterial species with hospital effluent demonstrating higher abundances of clinically relevant resistance genes including bla, bcr, qnrS, mcr, sul1, erm, and tet genes compared to measurements from local treatment plants. The most reported antibiotics in influent wastewater across studies analyzed include azithromycin, ciprofloxacin, clindamycin, and clarithromycin. The influence of seasonal variation on the ARG profiles of communities differed amongst studies indicating additional factors hold significance when examining the conference of AMR within communities. Despite these findings, knowledge gaps remain, including longitudinal studies in multiple and diverse geographical regions and understanding co-resistance mechanisms in relation to the complexities of population contributors to AMR. This review underscores the urgent need for collaborative and interdisciplinary efforts to safeguard public health and preserve antimicrobial efficacy. Further investigation on the use of WBE to understand these unique population-level drivers of AMR is advised in a proposed framework to inform best practice approaches moving forward.202541011405
769730.9979Impact of sample multiplexing on detection of bacteria and antimicrobial resistance genes in pig microbiomes using long-read sequencing. The effects of sample multiplexing on the detection sensitivity of antimicrobial resistance genes (ARGs) and pathogenic bacteria in metagenomic sequencing remain underexplored in newer sequencing technologies such as Oxford Nanopore Technologies (ONT), despite its critical importance for surveillance applications. Here, we evaluate how different multiplexing levels (four and eight samples per flowcell) on two ONT platforms, GridION and PromethION, influence the detection of ARGs, bacterial taxa and pathogens. While overall resistome and bacterial community profiles remained comparable across multiplexing levels, ARG detection was more comprehensive in the four-plex setting with low-abundance genes. Similarly, pathogen detection was more sensitive in the four-plex, identifying a broader range of low abundant bacterial taxa compared to the eight-plex. However, triplicate sequencing of the same microbiomes revealed that these differences were primarily due to sequencing variability rather than multiplexing itself, as similar inconsistencies were observed across replicates. Given that eight-plex sequencing is more cost-effective while still capturing the overall resistome and bacterial community composition, it may be the preferred option for general surveillance. Lower multiplexing levels may be advantageous for applications requiring enhanced sensitivity, such as detailed pathogen research. These findings highlight the trade-off between multiplexing efficiency, sequencing depth, and cost in metagenomic studies.202540611965
770740.9979Exploring the dynamics of gut microbiota, antibiotic resistance, and chemotherapy impact in acute leukemia patients: A comprehensive metagenomic analysis. Leukemia poses significant challenges to its treatment, and understanding its complex pathogenesis is crucial. This study used metagenomic sequencing to investigate the interplay between chemotherapy, gut microbiota, and antibiotic resistance in patients with acute leukemia (AL). Pre- and post-chemotherapy stool samples from patients revealed alterations in microbial richness, taxa, and antibiotic resistance genes (ARGs). The analysis revealed a decreased alpha diversity, increased dispersion in post-chemotherapy samples, and changes in the abundance of specific bacteria. Key bacteria such as Enterococcus, Klebsiella, and Escherichia coli have been identified as prevalent ARG carriers. Correlation analysis between gut microbiota and blood indicators revealed potential links between microbial species and inflammatory biomarkers, including C-reactive protein (CRP) and adenosine deaminase (ADA). This study investigated the impact of antibiotic dosage on microbiota and ARGs, revealing networks connecting co-occurring ARGs with microbial species (179 nodes, 206 edges), and networks associated with ARGs and antibiotic dosages (50 nodes, 50 edges). Antibiotics such as cephamycin and sulfonamide led to multidrug-resistant Klebsiella colonization. Our analyses revealed distinct microbial profiles with Salmonella enterica elevated post-chemotherapy in NF patients and Akkermansia muciniphila elevated pre-chemotherapy. These microbial signatures could inform strategies to modulate the gut microbiome, potentially mitigating the risk of neutropenic fever in patients undergoing chemotherapy. Finally, a comprehensive analysis of KEGG modules shed light on disrupted metabolic pathways after chemotherapy, providing insights into potential targets for managing side effects. Overall, this study revealed intricate relationships between gut microbiota, chemotherapy, and antibiotic resistance, providing new insights into improving therapy and enhancing patient outcomes.202439620486
354850.9979From flagellar assembly to DNA replication: CJSe's role in mitigating microbial antibiotic resistance genes. The emergence of Antibiotic Resistance Genes (ARGs) in Campylobacter jejuni (CJ) poses a severe threat to food safety and human health. However, the specific impact of CJ and its variants on ARGs and other related factors remains to be further elucidated. Herein, integrated metagenomic sequencing and co-occurrence network analysis approach were employed to investigate the impact of CJ and CJ incorporated with biogenic selenium (CJSe) on ARGs, flagellar assembly pathways, microbial communities, and DNA replication pathways in chicken manure. Compared to the Control (CON) and CJ groups, the CJSe group exhibited 2.4-fold increase selenium levels (P < 0.01) in chicken manure. Notable differences were also observed between the CJ and CJSe groups, with sequence results showing a CJ > CJSe > CON trend in total ARG copy numbers. Furthermore, the CJSe group showed 31.6 % fewer flagellar assembly genes compared to the CJ group. Additionally, compared to the CJ group, CJSe inhibited pathways such as basal body/hook (e.g., FliH, FliO, FliQ reduced by 25-52 %) and stator (MotB downregulated by 42.3 %), suppressing flagellar assembly. We also found that both CJ and CJSe influenced bacterial DNA replication pathways, with the former increasing ARG-carrying bacteria and the latter, under selenium-induced selective pressure, reducing ARG-carrying bacteria. Moreover, compared to the CJ group, the CJSe group showed a significantly lower 9.72 % copy number of total archaeal DNA replication genes. Furthermore, through intricate co-occurrence network analysis, we discovered the complex interplay between changes in ARGs and bacterial and archaeal DNA replication dynamics within the microbial community. These findings indicate that CJSe mitigates the threat posed by CJ and reduces ARG prevalence, while its dual functionality enables applications in biofortified crop production and soil remediation in selenium-deficient regions, thereby advancing circular economy systems. While the current study demonstrates CJSe's dual functionality under controlled conditions, future work will implement a dedicated ecological risk assessment framework encompassing Se speciation/leaching tests and non-target organism assays to confirm environmental safety under field-relevant scenarios. This approach aligns with sustainable strategies for food security and public health safeguarding.202541108960
770060.9979Rapid identification of antibiotic resistance gene hosts by prescreening ARG-like reads. Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44-96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.202540059905
668670.9978The Impact of Wastewater on Antimicrobial Resistance: A Scoping Review of Transmission Pathways and Contributing Factors. BACKGROUND/OBJECTIVES: Antimicrobial resistance (AMR) is a global issue driven by the overuse of antibiotics in healthcare, agriculture, and veterinary settings. Wastewater and treatment plants (WWTPs) act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The One Health approach emphasizes the interconnectedness of human, animal, and environmental health in addressing AMR. This scoping review analyzes wastewater's role in the AMR spread, identifies influencing factors, and highlights research gaps to guide interventions. METHODS: This scoping review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted across the PubMed and Web of Science databases for articles published up to June 2024, supplemented by manual reference checks. The review focused on wastewater as a source of AMR, including hospital effluents, industrial and urban sewage, and agricultural runoff. Screening and selection were independently performed by two reviewers, with conflicts resolved by a third. RESULTS: Of 3367 studies identified, 70 met the inclusion criteria. The findings indicated that antibiotic residues, heavy metals, and microbial interactions in wastewater are key drivers of AMR development. Although WWTPs aim to reduce contaminants, they often create conditions conducive to horizontal gene transfer, amplifying resistance. Promising interventions, such as advanced treatment methods and regulatory measures, exist but require further research and implementation. CONCLUSIONS: Wastewater plays a pivotal role in AMR dissemination. Targeted interventions in wastewater management are essential to mitigate AMR risks. Future studies should prioritize understanding AMR dynamics in wastewater ecosystems and evaluating scalable mitigation strategies to support global health efforts.202540001375
429880.9978Genomic and Metagenomic Approaches for Predictive Surveillance of Emerging Pathogens and Antibiotic Resistance. Antibiotic-resistant organisms (AROs) are a major concern to public health worldwide. While antibiotics have been naturally produced by environmental bacteria for millions of years, modern widespread use of antibiotics has enriched resistance mechanisms in human-impacted bacterial environments. Antibiotic resistance genes (ARGs) continue to emerge and spread rapidly. To combat the global threat of antibiotic resistance, researchers must develop methods to rapidly characterize AROs and ARGs, monitor their spread across space and time, and identify novel ARGs and resistance pathways. We review how high-throughput sequencing-based methods can be combined with classic culture-based assays to characterize, monitor, and track AROs and ARGs. Then, we evaluate genomic and metagenomic methods for identifying ARGs and biosynthetic pathways for novel antibiotics from genomic data sets. Together, these genomic analyses can improve surveillance and prediction of emerging resistance threats and accelerate the development of new antibiotic therapies to combat resistance.201931172511
322490.9978Assessing phenotypic and genotypic antibiotic resistance in bacillus-related bacteria isolated from biogas digestates. Antibiotic resistance poses a significant public health challenge, with biogas digestate, a byproduct of anaerobic digestion (AD), presenting potential risks when applied as a biofertilizer. Understanding the actual resistance levels in digestate is crucial for its safe application. While many studies have investigated antibiotic resistance in AD processes using culture-independent molecular methods, these approaches are limited by their reliance on reference databases and inability to account for gene expression, leading to potential inaccuracies in resistance assessment. This study addresses these limitations by combining culture-independent whole-genome sequencing (WGS) with culture-dependent phenotypic testing to provide a more accurate understanding of antibiotic resistance in digestate. We investigated the phenotypic and genotypic resistance profiles of 18 antibiotic-resistant bacteria (ARB) isolated from digestates produced from food waste and animal manure. Resistance was assessed using WGS and Estrip testing across 12 antibiotics from multiple classes. This is the first study to directly compare phenotypic and genotypic resistance in bacteria isolated from digestate, revealing significant discrepancies between the two methods. Approximately 30 % of resistance levels were misinterpreted when relying solely on culture-independent methods, with both over- and underestimation observed. These findings highlight the necessity of integrating both methods for reliable resistance assessments. Additionally, our WGS analysis indicated low potential for transferability of detected ARGs among the isolated ARB, suggesting a limited risk of environmental dissemination. This study provides new insights into antibiotic resistance in digestate and underscores the importance of integrating methodological approaches to achieve accurate evaluations of resistance risks.202539947064
6716100.9978Wastewater surveillance of antibiotic-resistant bacteria for public health action: potential and challenges. Antibiotic resistance is an urgent public health threat. Actions to reduce this threat include requiring prescriptions for antibiotic use, antibiotic stewardship programs, educational programs targeting patients and healthcare providers, and limiting antibiotic use in agriculture, aquaculture, and animal husbandry. Wastewater surveillance might complement clinical surveillance by tracking time/space variation essential for detecting outbreaks and evaluating efficacy of evidence-based interventions, identifying high-risk populations for targeted monitoring, providing early warning of the emergence and spread of antibiotic-resistant bacteria (ARBs), and identifying novel antibiotic-resistant threats. Wastewater surveillance was an effective early warning system for SARS-CoV-2 spread and detection of the emergence of new viral strains. In this data-driven commentary, we explore whether monitoring wastewater for antibiotic-resistant genes (ARGs) and/or bacteria resistant to antibiotics might provide useful information for public health action. Using carbapenem resistance as an example, we highlight technical challenges associated with using wastewater to quantify temporal/spatial trends in ARBs and ARGs and compare with clinical information. While ARGs and ARBs are detectable in wastewater enabling early detection of novel ARGs, quantitation of ARBs and ARGs with current methods is too variable to reliably track space/time variation.202539475072
6531110.9978A comprehensive framework of health risk assessment for antibiotic resistance in aquatic environments: Status, progress, and perspectives. Antibiotic resistance (AR), driven by antibiotics as emerging pollutants, has become a critical global health threat, jeopardizing both environmental and human health. The persistence and spread of AR in aquatic ecosystems are governed by the intricate interplay between antibiotics, antibiotic resistance genes (ARGs), and antibiotic-resistant bacteria (ARB), which collectively influences its occurrence, transportation, and fate in aquatic ecosystems. However, most assessments focus primarily on antibiotics and ARGs, often relying on single-factor criteria while overlooking critical influence factors such as ARG forms, non-antibiotic chemicals, antibiotic pressure, and microbial competition. Furthermore, many fail to incorporate potential future risks, limiting their predictive accuracy and overall effectiveness in addressing AR in aquatic environments. To bridge these research gaps, we introduce a comprehensive health risk assessment framework that integrates the interactions among antibiotics, ARGs, and ARB. The proposed approach comprises four steps: 1. Determining the type of water body; 2. Performing model simulations; 3. Assessing antibiotics and ARGs; and 4. Evaluating ARB. Finally, a comprehensive risk index for AR is established, along with a corresponding hierarchical risk ranking system. Moreover, to demonstrate the practical application of the framework, an assessment of antibiotic resistance risk was conducted using a typical lake in Northeast China as a case study, indicating the efficacy of the proposed framework in quantifying the multidimensional health risk of AR. This framework not only provides a crucial foundation for dynamic health risk assessment, but also paving the way for more effective mitigation strategies to safeguard both aquatic ecosystems and human health in the future.202540914069
5101120.9978Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232
6689130.9978Wastewater-Based Epidemiology as a Complementary Tool for Antimicrobial Resistance Surveillance: Overcoming Barriers to Integration. This commentary highlights the potential of wastewater-based epidemiology (WBE) as a complementary tool for antimicrobial resistance (AMR) surveillance. WBE can support the early detection of resistance trends at the population level, including in underserved communities. However, several challenges remain, including technical variability, complexities in data interpretation, and regulatory gaps. An additional limitation is the uncertainty surrounding the origin of resistant bacteria and their genes in wastewater, which may derive not only from human sources but also from industrial, agricultural, or infrastructural contributors. Therefore, effective integration of WBE into public health systems will require standardized methods, sustained investment, and cross-sector collaboration. This could be achieved through joint monitoring initiatives that combine hospital wastewater data with agricultural and municipal surveillance to inform antibiotic stewardship policies. Overcoming these barriers could position WBE as an innovative tool for AMR monitoring, enhancing early warning systems and supporting more responsive, equitable, and preventive public health strategies.202540522150
7712140.9978Metagenomic analysis of microbial communities and antibiotic resistance genes in spoiled household chemicals. Numerous attempts have been utilized to unveil the occurrences of antibiotic resistance genes (ARGs) in human-associated and non-human-associated samples. However, spoiled household chemicals, which are usually neglected by the public, may be also a reservoir of ARGs because of the excessive and inappropriate uses of industrial drugs. Based upon the Comprehensive Antibiotic Research Database, a metagenomic sequencing method was utilized to detect and quantify Antibiotic Resistance Ontology (AROs) in six spoiled household chemicals, including hair conditioner, dishwashing detergent, bath shampoo, hand sanitizer, and laundry detergent. Proteobacteria was found to be the dominant phylum in all the samples. Functional annotation of the unigenes obtained against the KEGG pathway, eggNOG and CAZy databases demonstrated a diversity of their functions. Moreover, 186 types of AROs that were members of 72 drug classes were identified. Multidrug resistance genes were the most dominant types, and there were 17 AROs whose resistance mechanisms were categorized into the resistance-nodulation-cell division antibiotic efflux pump among the top 20 AROs. Moreover, Proteobacteria was the dominant carrier of AROs with the primary resistance mechanism of antibiotic efflux. The maximum temperature of the months of collection significantly affected the distributions of AROs. Additionally, the isolated individual bacterium from spoiled household chemicals and artificial mixed communities of isolated bacteria demonstrated diverse resistant abilities to different biocides. This study demonstrated that there are abundant microorganisms and a broad spectrum profile of AROs in spoiled household chemicals that might induce a severe threat to public healthy securities and merit particular attention.202234740703
5099150.9978A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria. Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options.202439816256
6536160.9977Natural compound-induced downregulation of antimicrobial resistance and biofilm-linked genes in wastewater Aeromonas species. Addressing the global antimicrobial resistance (AMR) crisis requires a multifaceted innovative approach to mitigate impacts on public health, healthcare and economic systems. In the complex evolution of AMR, biofilms and the acquisition of antimicrobial resistance genes (ARGs) play a pivotal role. Aeromonas is a major AMR player that often forms biofilm, harbors ARGs and is frequently detected in wastewater. Existing wastewater treatment plants (WWTPs) do not have the capacity to totally eliminate antimicrobial-resistant bacteria favoring the evolution of ARGs in wastewater. Besides facilitating the emergence of AMR, biofilms contribute significantly to biofouling process within the activated sludge of WWTP bioreactors. This paper presents the inhibition of biofilm formation, the expression of biofilm-linked genes and ARGs by phytochemicals andrographolide, docosanol, lanosterol, quercetin, rutin and thymohydroquinone. Aeromonas species were isolated and purified from activated sludge samples. The ARGs were detected in the isolated Aeromonas species through PCR. Aeromonas biofilms were quantified following the application of biocompounds through the microtiter plate assay. qPCR analyses of related genes were done for confirmation. Findings showed that the natural compounds inhibited the formation of biofilms and reduced the expression of genes linked to biofilm production as well as ARGs in wastewater Aeromonas. This indicates the efficacy of these compounds in targeting and controlling both ARGs and biofilm formation, highlighting their potential as innovative solutions for combating antimicrobial resistance and biofouling.202439469451
6544170.9977A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments. The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.202439047454
6534180.9977Antibiotic resistance dissemination in soil ecosystems: deep understanding for effective management and global health protection. Antibiotic resistance poses a significant threat to global health, extending beyond clinical settings into environmental reservoirs such as soil, where resistant bacteria persist and evolve. Current efforts focus on understanding the origins and implications of antibiotic resistance in soil ecosystems. It defines antibiotic resistance within an environmental context and highlights soil as a critical reservoir for antibiotic-resistant genes (ARGs). Key sources of antibiotics in soil are identified, including agricultural practices, medical waste, and municipal and industrial effluents. The classification and mechanisms of ARGs are outlined, along with their transmission pathways, particularly within soil biofilms, which play a crucial role in gene transfer and microbial protection. The interplay between soil microbial communities and antibiotic resistance is discussed, emphasizing its potential risks to human health, including infectious diseases and food safety concerns. Strategies for mitigating antibiotic resistance in soil are presented, focusing on optimizing antibiotic usage, developing alternatives, and enhancing degradation mechanisms. This review underscores the need for interdisciplinary research to deepen understanding of soil microbial diversity and its connection to antibiotic resistance, emphasizing integrated efforts to safeguard soil and human health.202541166035
6718190.9977Agroecosystem exploration for Antimicrobial Resistance in Ahmedabad, India: A Study Protocol. INTRODUCTION: Antimicrobial resistance (AMR) has emerged as one of the leading threats to public health. AMR possesses a multidimensional challenge that has social, economic, and environmental dimensions that encompass the food production system, influencing human and animal health. The One Health approach highlights the inextricable linkage and interdependence between the health of people, animal, agriculture, and the environment. Antibiotic use in any of these areas can potentially impact the health of others. There is a dearth of evidence on AMR from the natural environment, such as the plant-based agriculture sector. Antibiotics, antibiotic-resistant bacteria (ARB), and related AMR genes (ARGs) are assumed to present in the natural environment and disseminate resistance to fresh produce/vegetables and thus to human health upon consumption. Therefore, this study aims to investigate the role of vegetables in the spread of AMR through an agroecosystem exploration in Ahmedabad, India. PROTOCOL: The present study will be executed in Ahmedabad, located in Gujarat state in the Western part of India, by adopting a mixed-method approach. First, a systematic review will be conducted to document the prevalence of ARB and ARGs on fresh produce in South Asia. Second, agriculture farmland surveys will be used to collect the general farming practices and the data on common vegetables consumed raw by the households in Ahmedabad. Third, vegetable and soil samples will be collected from the selected agriculture farms and analyzed for the presence or absence of ARB and ARGs using standard microbiological and molecular methods. DISCUSSION: The analysis will help to understand the spread of ARB/ARGs through the agroecosystem. This is anticipated to provide an insight into the current state of ARB/ARGs contamination of fresh produce/vegetables and will assist in identifying the relevant strategies for effectively controlling and preventing the spread of AMR.202338644926