Physicochemical characterisation of restructured Fenalår and safety implications of salt and nitrite reduction

There is a new trend to produce dry-cured ham from lamb in shorter times by boning the ham before salting to later obtain restructured hams that are easier to dry and slice. However, little information about the physicochemical characteristics of Norwegian Fenalårs during the process or the safety implications of their elaboration procedures is reported in the literature. The aim of this study was to characterize the colour, texture and physicochemical properties of restructured Fenalårs when using Standard Salting (SS), Salt Reduced (SR) and a Non-Nitrite Salt Reduced (NNSR) treatments. Microbiological safety implications of the elaboration process when using the different salting treatments were also assessed using predictive microbiology. To do so, sixty Fenalårs were elaborated using a Standard Salting (SS), a Salt reduced (SR) and a Non-Nitrite Salt Reduced (NNSR) treatments. Physicochemical characterization (instrumental colour and texture and Zinc Protoporphyrin content) was performed at the end of the process using thirty Fenalårs. The rest of the Fenalårs were used to characterize the product through the elaboration process (pH and aw) for the evaluation of microbiological hazards when using the different salting treatments using predictive microbiology. Results showed a significant increase in softness when reducing salt content and a decrease of redness when no nitrite was used, attributed to the formation of ZnPP content instead of nitrosylmyoglobin. In terms of risk assessment, the decrease of aw through the elaboration process reduced the growth capacity of all the microorganisms evaluated. However, microbiological safety implications in salt reduced Fenalårs are important, especially when no nitrite was added, because the considerable increase of growth potential of L. monocytogenes. The increase of growth potential of proteolytic C. botulinum is very little and no relevant effect of nitrite on growth potential of S. aureus was observed. Predictive microbiology and optimization of the process to enhance ZnPP formation can help to ensure safety and quality of salt reduced restructured Fenalårs without additives.info:eu-repo/semantics/acceptedVersio

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.Therapeutic cell differentiatio