How Can AI Help Improve Food Safety?

Annual Review of Food Science and Technology, Journal Year: 2022, Volume and Issue: 14(1), P. 517 - 538

Published: Dec. 21, 2022

With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest using different AI applications, such as machine learning models, natural language processing, computer vision to improve safety. Possible applications broad include, but not limited to, ( a) safety risk prediction monitoring well optimization throughout supply chain, b) improved public health (e.g., by providing early warning outbreaks source attribution), c) detection, identification, characterization foodborne pathogens. However, technologies lag behind commercial because obstacles data sharing collaborative research efforts. Future actions should be directed toward applying privacy protection methods, improving standardization, developing a ecosystem drive innovations

Language: Английский

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Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

International Journal of Molecular Sciences, Journal Year: 2022, Volume and Issue: 23(17), P. 9834 - 9834

Published: Aug. 30, 2022

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into antimicrobial resistance virulence potential of a single microbiological isolate during diagnostic process. In contrast, metagenomic allows analysis DNA segments from multiple microorganisms within community, either using an amplicon- or shotgun-based approach. However, WGS shotgun data are rarely combined, although such approach may generate additive synergistic information, critical for, e.g., patient management, infection control, pathogen surveillance. To produce combined workflow with actionable outputs, we need to understand pre-to-post analytical process both technologies. This will require specific databases storing interlinked metadata, also involves customized bioinformatic pipelines. review article overview steps clinical application combining metagenomics together diagnosis.

Language: Английский

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Candida albicans selection for human commensalism results in substantial within-host diversity without decreasing fitness for invasive disease

PLoS Biology, Journal Year: 2023, Volume and Issue: 21(5), P. e3001822 - e3001822

Published: May 19, 2023

Candida albicans is a frequent colonizer of human mucosal surfaces as well an opportunistic pathogen. C. remarkably versatile in its ability to colonize diverse host sites with differences oxygen and nutrient availability, pH, immune responses, resident microbes, among other cues. It unclear how the genetic background commensal colonizing population can influence shift pathogenicity. Therefore, we examined 910 isolates from 35 healthy donors identify niche-specific adaptations. We demonstrate that people are reservoirs for genotypically phenotypically strains. Using limited diversity exploitation, identified single nucleotide change uncharacterized ZMS1 transcription factor was sufficient drive hyper invasion into agar. found SC5314 significantly different majority both bloodstream induce cell death. However, our strains retained capacity cause disease Galleria model systemic infection, including outcompeting reference strain during competition assays. This study provides global view variation within-host suggests selection commensalism humans does not result fitness cost invasive disease.

Language: Английский

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Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Antibiotics, Journal Year: 2023, Volume and Issue: 12(11), P. 1580 - 1580

Published: Oct. 30, 2023

Recent advancements in sequencing technology and data analytics have led to a transformative era pathogen detection typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming standard for analysis control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into epidemiology emergence antimicrobial-resistant strains. Antimicrobial resistance (AMR) is pressing global public health issue. While clinical laboratories traditionally relied on culture-based antimicrobial susceptibility testing, integration AMR holds immense promise. Genomic-based furnish swift, consistent, highly accurate predictions phenotypes specific strains or populations, all while contributing invaluable surveillance. Moreover, genome assumes pivotal role investigation hospital outbreaks. It aids identification infection sources, unveils genetic connections among isolates, informs strategies The One Health initiative, with its focus intricate interconnectedness humans, animals, environment, seeks develop comprehensive approaches disease surveillance, control, prevention. When integrated epidemiological systems, forecast expansion bacterial populations species transmissions. Consequently, this provides evolution relationships pathogens, hosts, environment.

Language: Английский

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IntracellularStaphylococcus aureusand host cell death pathways

Cellular Microbiology, Journal Year: 2021, Volume and Issue: 23(5)

Published: Feb. 7, 2021

Staphylococcus aureus is a major opportunistic human pathogen that globally prevalent. Although S. and humans may have co-evolved to the point of commensalism, bacterium equipped with virulence factors causing devastating infections. The adoption an intracellular lifestyle by important facet its pathogenesis. Occupying privileged compartment permits evasion from bactericidal actions host immunity antibiotics. However, this localization exposes cell-intrinsic processes comprising autophagy, metabolic challenges clearance mechanisms orchestrated programmed cell death pathways (PCDs), including apoptosis, pyroptosis necroptosis. Mounting evidence suggests deploys pathoadaptive modulate expression prevent elimination through PCD pathways. In review, we critically analyse current literature on interplay between key, intertwined nodes PCD. We discuss how adaptation plays essential role in PCD, consider future directions study aureus-PCD interactions.

Language: Английский

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Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(3), P. e1010018 - e1010018

Published: March 25, 2022

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) spread humans. Here, we performed a longitudinal study in large-scale commercial poultry farm China, collecting E . isolates from both slaughterhouse; targeting animals, carcasses, workers their households environment. By using whole-genome phylogenetic analysis network based on single nucleotide polymorphisms (SNPs), found highly interrelated non-pathogenic pathogenic strains with intermixing, high prevalence shared multidrug profiles amongst human Through an original data processing pipeline which combines omics, machine learning, gene sharing mobile genetic elements analysis, investigated 26 different antimicrobials identified 361 associated (AMR) phenotypes; 58 these were known AMR-associated 35 resistance. We uncovered extensive genes, correlated AMR phenotypes, among humans, slaughterhouse environments. also several human, livestock environmental closely related carrying ARGs across host species In scenario where no consensus exists how antibiotic use may affect population, our findings novel insights into broader epidemiology farming. Moreover, method has potential uncover transmission pathways when applied other pathogens active anthropogenic characterised complex interconnections between species.

Language: Английский

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From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

FEMS Microbiology Reviews, Journal Year: 2023, Volume and Issue: 47(4)

Published: June 7, 2023

Abstract When selecting microbial strains for the production of fermented foods, various phenotypes need to be taken into account achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, whole-genome sequences increasing quality can now obtained both cheaper faster, which increases relevance genome-based characterization phenotypes. Prediction from genome makes it possible quickly screen large strain collections silico identify candidates with desirable traits. Several relevant foods predicted using knowledge-based approaches, leveraging our existing understanding genetic molecular mechanisms underlying those In absence this knowledge, data-driven approaches applied estimate genotype–phenotype relationships based on experimental datasets. Here, we review computational methods that implement knowledge- phenotype prediction, well combine elements approaches. Furthermore, provide examples how these have been industrial biotechnology, special focus food industry.

Language: Английский

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Machine learning-based global trends and the development prospects of wastewater treatment: A bibliometric analysis

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(3), P. 112732 - 112732

Published: April 7, 2024

Language: Английский

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Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 170, P. 108062 - 108062

Published: Jan. 30, 2024

Language: Английский

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Immunosenescence: How Aging Increases Susceptibility to Bacterial Infections and Virulence Factors

Microorganisms, Journal Year: 2024, Volume and Issue: 12(10), P. 2052 - 2052

Published: Oct. 11, 2024

The process of aging leads to a progressive decline in the immune system function, known as immunosenescence, which compromises both innate and adaptive responses. This includes impairments phagocytosis decreased production, activation, function T- B-lymphocytes, among other effects. Bacteria exploit immunosenescence by using various virulence factors evade host’s defenses, leading severe often life-threatening infections. manuscript explores complex relationship between bacterial virulence, focusing on underlying mechanisms that increase vulnerability infections elderly. Additionally, it discusses how machine learning methods can provide accurate modeling interactions weakened mechanisms, guiding development personalized interventions. vaccines, novel antibiotics, antivirulence therapies for multidrug-resistant bacteria, well investigation potential immune-boosting therapies, are promising strategies this field. Future research should focus approaches be integrated with immunological, microbiological, clinical data develop interventions improve outcomes growing elderly population.

Language: Английский

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