Quinolones (QNs), used extensively as therapeutic agents and growth promoters worldwide, have significantly residued in food and environment, causing serious threat to human health through food chain. There is a high and urgent demand for easy, rapid, sensitive, and economical analytical methods for QNs residues monitoring. Immunoassays are recommended as the first-rate choice in the rapid screening of QNs residues in food and environmental samples due to various advantages. Great progress has been achieved in recent researches of immunoassays for screening QNs. This comprehensive review systematically presents specific recognition elements production, immunoassay format construction and improvement, outlook and challenges of immunoassays for QNs. The review provides new information and insightful commentary, critically important to the development and innovation of further immunoassay for QNs detection.

Nicosulfuron, a typical sulfonylurea herbicide widely used in maize fields, easily accumulates in soil after long-term application, posing phytotoxic risks to subsequent sensitive crops such as wheat and cabbage and restricting sustainable crop rotation systems. In this study, a direct competitive enzyme-linked immunosorbent assay (dc-ELISA) was developed by conjugating prussian blue nanoparticles (PBNPs) with the highly specific rabbit monoclonal antibody 6D8 for rapid, stable, and low-cost detection of nicosulfuron in soil and agricultural products. The optimal detection conditions were determined by optimizing the working concentrations of recognition and buffer system. The specificity, matrix effects, and spike recovery of the method were systematically evaluated and verified by UPLC-MS/MS. The established method exhibited an IC50 of 2.031 ng/mL, a linear detection range of 0.107–38.232 ng/mL, and excellent specificity. Matrix interference was effectively eliminated by dilution, and the spiked recoveries ranged from 94.0%–105.3%. Pot experiments confirmed that nicosulfuron residues in soil were significantly positively correlated with wheat growth inhibition, and wheat in the high-residue group exhibited obvious phytotoxic symptoms such as dwarfism and yellowing. The established method can accurately reflect residue levels and ecological risks, providing technical support for residue monitoring, subsequent crop selection, and herbicide safe application.

Synthetic microbial community (SynCom) biosensors are emerging from the convergence of whole-cell biosensing, synthetic ecology, and computational design. Conventional whole-cell biosensors (WCBs) use a single microbial chassis to convert analyte recognition into optical, electrochemical, gaseous, or growth-linked outputs. This compact architecture supports low-cost and field-oriented detection, but it can be limited by cellular burden, narrow dynamic range, environmental interference, and difficulty in interpreting multicomponent signals. Natural microbial consortia provide an ecological template in which sensing, transformation, stress tolerance, and response are distributed across interacting populations. SynCom biosensors seek to translate this logic into engineered platforms with defined members, assigned functional roles, designed communication, and interpretable readouts. This review traces the transition from WCBs to natural consortia and engineered multicellular biosensors, emphasizing functional partitioning, signal routing, community control, and artificial intelligence (AI)-assisted design. AI is discussed as a practical tool for narrowing design space, predicting interactions, decoding complex biosignals, and supporting adaptive operation. Key challenges remain in community stability, orthogonal communication, data quality, biosafety, standardization, and real-sample validation. Future progress will depend on parsimonious community design, reliable containment, quantitative validation, and computational workflows that connect community composition with sensing performance.