ArticleItalian journal of food safety2026
Current research trends towards the control of protozoans in foods.
Article in Italian journal of food safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Protozoan parasites such as Cryptosporidium spp., Giardia duodenalis, Toxoplasma gondii and Cyclospora cayetanensis remain difficult-to-control hazards in food due to environmental persistence, low infectious doses, and the interpretability gap between nucleic acid detection and infectivity. This review synthesizes 4-year research trends shaping protozoan control in food systems, focusing on three critical pillars: matrix-adapted front-end processing (concentration, lysis, inhibitor management); inhibitor-resilient quantification; and sequencing-based attribution for outbreak investigation and source tracking. Recent benchmarking across wastewater, the water-soil-produce nexus, and food-relevant matrices repeatedly indicates - depending on matrix and study design - that upstream workflow steps often dominate analytical sensitivity and reproducibility. Accordingly, tiered analytical strategies are emerging in which the quantitative polymerase chain reaction (PCR) technique supports scalable screening, droplet digital PCR is used for decision-grade confirmation/quantification under inhibition and low-template conditions, and targeted sequencing or metagenomics is deployed selectively for traceback and contextual investigation. We integrate these developments into an actionable control framework that links prevention at the water-soil-plant interface with tiered analytics and viability-aware interpretation of post-intervention results. Research priorities ahead include harmonized performance reporting (recovery, inhibition controls, limit of detection/quantification), transparent endpoint hierarchy for intervention claims (detectability vs. viability/infectivity), and interoperable sequence databases to enable cross-laboratory attribution and program-level learning. The field is moving from "can we detect?" towards "can we decide? - requiring reproducible front-end processing, inhibitor-resilient quantification, interoperable attribution resources, and endpoint discipline for intervention efficacy claims.
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