The objective of this study was to investigate and summarize the levels of incidence of Salmonella spp., Listeria monocytogenes, Staphylococcus aureus and Campylobacter spp. in poultry meat commercialized in Europe. After systematic review, incidence data and study characteristics were extracted from 78 studies conducted in 21 European countries. Pooled prevalence values from 203 extracted observations were estimated from random-effects meta-analysis models adjusted by pathogen, poultry type, sampling stage, cold preservation type, meat cutting type and packaging status. The results suggest that S. aureus is the main pathogen detected in poultry meat (38.5%; 95% CI: 25.4–53.4), followed by Campylobacter spp. (33.3%; 95% CI: 22.3–46.4%), while L. monocytogenes and Salmonella spp. present lower prevalence (19.3%; 95% CI: 14.4–25.3% and 7.10%; 95% CI: 4.60–10.8%, respectively). Despite the differences in prevalence, all pathogens were found in chicken and other poultry meats, at both end-processing step and retail level, in packed and unpacked products and in several meat cutting types. Prevalence data on cold preservation products also revealed that chilling and freezing can reduce the proliferation of pathogens but might not be able to inactivate them. The results of this meta-analysis highlight that further risk management strategies are needed to reduce pathogen incidence in poultry meat throughout the entire food chain across Europe, in particular for S. aureus and Campylobacter spp.

Species adulteration is a concern in herbal products, especially when plant substitutes of lower economic value replace valuable botanicals. Styphnolobium japonicum is well known as a potential adulterant of Ginkgo biloba, which is one of the most demanded medicinal plants due to its wide use in pharmaceuticals, food supplements, and traditional medicine. Despite bearing some resemblance to ginkgo's flavonol composition, S. japonicum lacks many of G. biloba's desired therapeutic properties. To prevent adulteration practices, it is crucial to implement rigorous quality control measures, including fast and simple diagnostic tools that can be used on-field. This study aims to develop for the first time a species-specific loop-mediated isothermal amplification (LAMP) method for the fast identification of S. japonicum in ginkgo-containing products. A set of four specific primers (SjF3, SjB3, SjFIP, and SjBIP) and loop primers (SjLF and SjLB) were designed for a LAMP based assay using the 5.8S partial sequence and the internal transcribed spacer 2 of nuclear ribosomal DNA of S. japonicum. The successful amplification of the LAMP assay was inspected through visual detection, with the highest intensity recorded at the optimal conditions set at 68 °C for 40 min. The primers showed high specificity and were able to accurately discriminate S. japonicum from G. biloba and 49 other species of medicinal plants. Furthermore, the proposed LAMP assay proved to be fast, selective, and highly sensitive, as demonstrated by the absolute and relative limits of detection, which were reached at 0.5 pg for S. japonicum DNA and 0.01 % S. japonicum in G. biloba, respectively. This novel approach allows easy identification and discrimination of S. japonicum as a potential adulterant of G. biloba, thus being a useful tool for quality control. Compared to chromatographic or PCR-based methods, the assay proved to be fast, sensitive and did not require expensive equipment, thus offering the possibly usage in field analysis.

The discovery of diagnostic or prognostic biomarkers is fundamental to optimize therapeutics for patients. By enhancing the interpretability of the prediction model, this work is aimed to optimize Leukemia diagnosis while retaining a high-performance evaluation in the identification of informative genes. For this purpose, we used an optimal parameterization of Kernel Logistic Regression method on Leukemia microarray gene expression data classification, applying metalearners to select attributes, reducing the data dimensionality before passing it to the classifier. Pearson correlation and chi-squared statistic were the attribute evaluators applied on metalearners, having information gain as single-attribute evaluator. The implemented models relied on 10-fold cross-validation. The metalearners approach identified 12 common genes, with highest average merit of 0.999. The practical work was developed using the public datamining software WEKA.

Plant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium–tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks.