Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
International journal of food microbiology
Machine learning-based modeling for monitoring and predicting the detection rate and severity of pathogenic microorganisms in seafood
Pathogenic microbial contamination in seafood presents persistent risks to food safety and public health. Conventional monitoring methods frequently lack adequate sensitivity and specificity, particularly under dynamic environmental conditions. To address these limitations, this study developed a machine learning (ML) framework utilizing microbial testing data from seafood samples collected in Yantai, China (2014-2024), with 2025 data reserved for external validation. The framework simultaneously predicts pathogen prevalence and severity levels. And, exploratory data analysis assessed variable distributions and statistical associations with detection rates. Time series analysis identified significant long-term trends and seasonal patterns, while spatial analysis revealed heterogeneity in contamination risk across administrative regions, particularly in coastal economic zones and major ports. Applying a dynamic threshold optimization strategy, detection rates were predicted using six ML algorithms: ROSE-LASSO, LightGBM, XGBoost, k-NN, CART, and SVM. LightGBM demonstrated optimal performance with 97.6% specificity and 99.9% positive predictive value (PPV) at an optimized threshold of 0.157. External validation confirmed its robustness, yielding 91.2% sensitivity, a 92.4% F1 score, and 93.6% PPV. For three-class severity classification, XGBoost achieved 93.7% overall accuracy and high sensitivity (80.0%) for high-risk samples, with an AUC of 0.989. By integrating spatiotemporal environmental features with adaptive thresholding, this framework provides a scalable approach for pathogen risk identification within coastal seafood supply chains, supporting early warning systems and evidence-based resource allocation.
17 May 2026
Read appraisal →Annals of medicine
A comprehensive review of regulatory mechanisms of novel enterotoxin genes in Staphylococcus aureus from meat sources
BACKGROUND AND AIMS: Meat-borne Staphylococcus aureus (S. aureus) remains a leading global foodborne pathogen harbouring novel enterotoxin genes (NEGs) encoding superantigenic toxins with conditionally enhanced pathogenicity, representing a critical food safety hazard. This review characterizes NEG features, pathogenic mechanisms, multi-layered regulatory networks, and identifies key research challenges. METHODS: We systematically searched PubMed, Embase and the Web of Science Core Collection to synthesize molecular and multi-omics evidence (ChIP-seq, RNA-seq, CRISPR-Cas9) characterizing NEG classification, pathogenic mechanisms, multi-level regulatory networks (transcriptional, post-translational, environmental), and geographical distribution patterns in meat-derived S. aureus. RESULTS: NEGs comprise superantigenic and tissue-targeting subgroups mediating pathogenicity via cytotoxicity, intestinal microenvironment disruption, and immune evasion. Regulation involves a complex network of Agr/σB/SarA/Rot-mediated transcriptional control, phosphorylation/lactylation modifications and environmental sensing, exhibiting marked geographical divergence. Current limitations include technical resolution constraints, insufficient physiological model fidelity and incomplete regulatory crosstalk elucidation. CONCLUSIONS: Future research should prioritize transcription factor interaction mechanisms, growth-toxin correlation prediction models and multi-omics-based network decipherment. This review provides a foundational framework for NEG research to inform food safety risk assessment and targeted contamination control strategies.
10 May 2026
Read appraisal →Analytical chemistry
Multicomponent Simultaneous Identification Network (MSINet): An Advanced Deep Learning Model for Boosting Multiplex SERS Detection in Untreated Real Samples
Surface-enhanced Raman spectroscopy (SERS) represents unique advantages for on-site analysis due to its high sensitivity, high specificity, and applicability to gaseous, liquid, and solid samples. However, the complex and unknown components in matrices, as well as competitive adsorptions between targets, will cause signal fluctuation and spectral overlap, which make it challenging to detect multiple targets in untreated real samples. Herein, we designed a deep learning model named the multicomponent simultaneous identification network (MSINet) for analyzing the SERS spectra of untreated samples and output qualitative and content evaluation results of multiple inside targets. The hierarchical feature extraction and multiple feature integration made MSINet robust to signal fluctuations and spectral overlaps while requiring only a small amount of training to quantify target contents in any samples due to its threshold judgment rule. Using the MSINet-assisted SERS detection scheme, the presence and content levels of multiple UV absorbers in seawater, food additives in ready-to-drink cocktails, and biomarkers in human serum could all be determined with ≥0.90 accuracy when facing testing databases. But in contrast, the accuracy in evaluating the target contents in the above untreated samples by the standard curve method was as low as 0.05. The powerful ability to process spectral data, an easy-to-use graphical user interface, and "zero" extra cost make MSINet a universal tool to assist arbitrary SERS sensors in performing high-throughput detection of real samples, leading to great potential in on-site analysis.
8 Apr 2026
Read appraisal →PloS one
IOTA Tangle-based traceability framework for wheat crop supply chain
Distributed ledger technology (DLT) has emerged as a transformative solution across industries, offering decentralization, transparency, and security. Among DLT architectures, IOTA's Tangle stands out as a scalable, feeless framework well-suited for supply chain applications, particularly in critical sectors like agriculture. This work leverages IOTA's Tangle to design a secure and traceable wheat supply chain system, addressing challenges such as opacity, fraud, and inefficiency. The proposed solution is an IOTA-based framework designed for enhancing supply chain traceability of wheat, offering trustworthy, transparent, secure and scalable services. Based on smart contracts, a transaction processing system was developed to enhance transparency and traceability and automate tasks. Furthermore, we used various technologies for increased visibility and operational efficiency throughout the supply chain, including IPFS (InterPlanetary File System) for storing extensive data such as stakeholder certifications, AI (Artificial Intelligence) for predicting crop yields, and Self-Sovereign Identity (SSI) for enhanced security. In addition, a government-provided mobile app helps silo managers and police officers verify transportation credentials and prevent contraband. The implementation of this system is expected to enhance supply chain resilience and transparency, particularly in Tunisia, thereby supporting the country's goal of food autonomy.
6 Apr 2026
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