Meat serves as a prime medium for the growth of foodborne pathogens due to its rich protein content and high water activity, contributing significantly to the global burden of foodborne illnesses. This review synthesizes current advances in meat-borne bacterial pathogen detection with particular emphasis on emerging artificial intelligence (AI)-enabled applications. Major pathogens of concern, including Salmonella, Listeria monocytogenes, Escherichia coli, Campylobacter, Clostridium, and Staphylococcus aureus, are examined in relation to their relevance across the meat supply chain. Recent progress in biosensors (clustered regularly interspaced short palindromic repeats), CRISPR-based assays, isothermal amplification, and metagenomics is evaluated alongside the growing role of AI in automating signal interpretation, enhancing image-based diagnostics, and supporting early contamination prediction. AI-based systems have proved 96.4–104% recovery and 100% bacterial capture ability. Embedding AI methods in a wet lab demands technical and logical modeling, as well as learning and calibration decorum. Nonetheless, AI readiness and full-scale application for meat-borne pathogens surveillance are on the way. Furthermore, additional focus is aligned on meat-borne bacterial pathogen genomic databases, i.e., (NCBI Pathogen Detection, EnteroBase, VFDB, ComBase, and GenBank), which serve as critical training resources for AI models for outbreak tracking, virulence profiling, and antimicrobial resistance (AMR) prediction. By integrating molecular methods, genomic surveillance, and AI-driven analytics, this review presents a framework for strengthening meat safety systems. This will improve early detection capabilities and support data-driven public health interventions in the future.

Nucleotide-binding site (NBS) domain genes are one of a superfamily of resistance genes involved in plant responses to pathogens. The current study identified presumably identified 12,820 NBS-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes classified into 168 classes with several novel domain architectures patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG ₀ , OG ₁ , OG ₂ etc.) and unique (highly specific to species; OG ₈₀ , OG ₈₂ etc) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG ₂ , OG _6, and OG ₁₅ in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to CLCuD. The genetic variation between susceptible (Coker 312) and tolerant (Mac7) G. hirsutum accessions identified several unique variants in NBS genes of Mac7 (6,583 varaints) and Coker312 (5,173 variants). The protein-ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of cotton leaf curl disease virus. The silencing of GaNBS (OG ₂ ) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful to understand the plant adaptation mechanism.