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22 records · Page 2Linked to original sources
PubMed2026

Regional genomic analysis of lineage distribution and transferable multidrug resistance among chicken-associated Salmonella Kentucky isolates in China.

Salmonella enterica serovar Kentucky is an important multidrug-resistant foodborne pathogen in the poultry meat supply chain. Although recent broader genomic studies have elucidated the population structure and epidemiological significance of major lineages in China (e.g., ST198 and ST314), the regional dynamics within local poultry supply chains remain insufficiently characterized. In this study, 31 chicken meat-derived isolates from Shanghai and 39 publicly available genomes from China were analyzed using antimicrobial susceptibility testing, whole-genome sequencing, phylogenetic analysis, conjugation experiments, and complete sequencing of representative plasmids. This enabled a systematic characterization of the molecular epidemiological features of the population and the mechanisms underlying resistance dissemination. Population genomic analysis revealed a lineage composition markedly different from the global epidemiological pattern: ST314 was the predominant sequence type among the Shanghai chicken-derived isolates (74.2%), whereas the internationally recognized high-risk clone ST198 accounted for only 25.8% of the local isolates. However, risk stratification analysis indicated that although ST198 was detected less frequently, it carried a significantly greater burden of acquired resistance genes and therefore represented a higher-risk resistant lineage. Functional and structural validation further elucidated the molecular basis of resistance dissemination within this high-risk lineage. Conjugation experiments confirmed the co-transfer of a multidrug resistance module carrying blaTEM-1 and blaCTX-M-267 to the recipient strain Escherichia coli J53. Complete plasmid analysis revealed that these two β-lactam resistance genes were co-localized on a 242-kb transferable plasmid flanked by Tn1331, Tn3, and multiple transposase-associated elements, thereby providing a structural basis for their horizontal transfer. This study provides important molecular epidemiological evidence for lineage-specific surveillance and risk-stratified control of resistant Salmonella in the poultry meat supply chain and further underscores the need for continuous monitoring of mobile genetic elements within a One Health framework.

Animals↗
PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7 × 108 CFU/mL and a low detection limit of 1.66 CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗
PubMed2026

Fungal drivers of mycotoxin contamination in wheat: Early warning and plasma-based control.

Mycotoxin contamination in wheat is a major food safety concern; however, quantitative evidence linking fungal community signals, mycotoxin exceedance risk, and wheat quality traits in naturally contaminated wheat remains limited. In this study, wheat samples were collected from mycotoxin-prone monitoring sites under unusually rainy conditions in 2022 to explore early-warning indicators and post-harvest mitigation strategies. According to the National Food Safety Standard of China GB 2761-2017, aflatoxin B1 (AFB1), deoxynivalenol (DON), and zearalenone (ZEN) exceeded the maximum limits in 52.24, 47.76, and 23.88% of samples, respectively; 38.81% exceeded the reference EU threshold for T-2 toxin, and 46.27% showed co-contamination with at least two mycotoxins above their respective thresholds. Although Alternaria, Cladosporium, and Epicoccum dominated the fungal community, Fusarium abundance was significantly associated with DON contamination and Fusarium-damaged kernels (FDKs). Mediation analysis identified DON as a significant mediator linking Fusarium abundance to FDKs, accounting for 68.41% of the total effect. In addition, Fusarium abundance above 3.70% showed strong predictive performance for DON exceedance, with an area under the curve of 0.906, indicating its potential as an early-warning indicator. Culture-based assays confirmed the toxigenic potential of Aspergillus and Fusarium isolates under simulated temperature and moisture conditions. After optimization using a toxin-spiked wheat flour model, dielectric barrier discharge cold plasma degraded AFB1, DON, and ZEN by 29.30-35.68%, disrupted the morphology of toxigenic fungi, and did not significantly affect wheat quality. This study provides practical insights into mycotoxin risk warning and post-harvest mitigation in wheat.

Triticum↗
PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗