Enhancement of dairy sheep cheese eating quality with increased n-3 long-chain polyunsaturated fatty acids.

This study investigated the effect of different plant oil-infused and rumen-protected wheat-based pellets containing eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3) on n-3 long-chain (≥C20) polyunsaturated fatty acid (LC-PUFA) content, fatty acid recovery, and sensory attributes of ripened cheese from dairy sheep. During a 10-wk supplementary feeding trial, 60 dairy ewes balanced by live weight, milk yield, parity, and sire breed were randomly divided into 6 groups that were (1) supplemented with on-farm existing commercial wheat-based pellets without oil inclusion (control) or supplemented with wheat-based pellets infused with 50 mL/kg dry matter of oils from (2) canola, (3) rice bran, (4) flaxseed, (5) safflower, and (6) rumen-protected EPA + DHA. Milk samples from each treatment were collected separately by sire breed during the experimental period for cheese processing at the end of the experiment. Twelve batches of cheese (2 batches per treatment) were processed and ripened for 120 d. Three cheese samples were collected and analyzed for each cheese making session (total of 36 cheese samples) at d 120 of ripening. Processed cheese of rumen-protected EPA + DHA had the most efficiency at elevating total n-3 LC-PUFA [total EPA + DHA + docosapentaenoic acid (DPA, 22:5n-3] content compared with the control (0.49 vs. 0.28%). Flaxseed elicited the greatest enhancement of α-linolenic acid (ALA, 18:3n-3), whereas safflower was the most effective diet in enhancing the level of linoleic acid (18:2n-6) in cheese (1.29 vs. 0.71% and 4.8 vs. 3.3%, respectively). Parallel recoveries of n-3 and n-6 LC-PUFA were observed across all treatments except for α-linolenic acid and EPA. Cheese eating sensory traits were also highly affected by oil supplementation, with the highest score of 7.5 in cheese from the rice bran and flaxseed treatments. These results provide new insights into the biological mechanisms and processes that determine dairy ewe milk productivity by underpinning the vital biological role of n-3 LC-PUFA in not only enhancing the healthy composition of cheese from ewes but also translating it into consumer acceptability.

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data.

The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and "Patient-level Prediction" (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.

A straightforward and flexible [4 + 2] route to beta-C-naphthyl-2-deoxy-glycosides through tandem hydroboration-ketal reduction: de novo access to C-naphthyl-6-fluoro and 6,6-difluoro 2-deoxyglycosides.

[reaction: see text] Under standard hydroboration-oxidation conditions, the dihydropyrans 4 underwent a highly stereocontrolled tandem reaction, involving the expected hydration of the double bond together with the reduction of the ketal moiety. This unprecedented transformation gives rise to a short, [4 + 2]-based synthetic route to (+/-)-beta-C-naphthyl-2-deoxyglycosides 5, which allows a significant structural and functional diversity at C-6. We thus described the first synthesis of (+/-)-C-aryl-6-fluoro and -6,6-difluoro olivosides, via the allylic mono- and difluorides produced by regioselective fluorination of, respectively, hydroxyalkyl and oxoalkyl dihydropyran derivatives.