A shift to a bioeconomy development model has been evolving, conducting the scientific community to investigate new ways of producing chemicals, materials and fuels from renewable resources, i.e., biomass. Specifically, technologies that provide high performance and maximal use of biomass feedstocks into commodities with reduced environmental impact have been highly pursued. A key example comprises the extraction and/or dissolution of polysaccharides, one of the most abundant fractions of biomass, which still need to be improved regarding these processes' efficiency and selectivity parameters. In this context, the use of alternative solvents and the application of less energy-intensive processes in the extraction of polysaccharides might play an important role to reach higher efficiency and sustainability in biomass valorization. This review debates the latest achievements in sustainable processes for the extraction of polysaccharides from a myriad of biomass resources, including lignocellulosic materials and food residues. https://www.selleckchem.com/products/poly-d-lysine-hydrobromide.html Particularly, the ability of ionic liquids (ILs) and deep eutectic solvents (DESs) to dissolve and extract the most abundant polysaccharides from natural sources, namely cellulose, chitin, starch, hemicelluloses and pectins, is scrutinized and the efficiencies between solvents are compared. The interaction mechanisms between solvent and polysaccharide are described, paving the way for the design of selective extraction processes. A detailed discussion of the work developed for each polysaccharide as well as the innovation degree and the development stage of dissolution and extraction technologies is presented. Their advantages and disadvantages are also identified, and possible synergies by integrating microwave- and ultrasound-assisted extraction (MAE and UAE) or a combination of both (UMAE) are briefly described. Overall, this review provides key information towards the design of more efficient, selective and sustainable extraction and dissolution processes of polysaccharides from biomass.
In order to explore the possible association between chronotype and risk of medication errors and chronotype in Italian midwives, we conducted a web-based survey. The questionnaire comprised three main components (1) demographic information, previous working experience, actual working schedule; (2) individual chronotype, either calculated by Morningness-Eveningness Questionnaire (MEQ); (3) self-perception of risk of medication error.

Midwives (
= 401) responded "yes, at least once" to the question dealing with self-perception of risk of medication error in 48.1% of cases. Cluster analysis showed that perception of risk of medication errors was associated with class of age 31-35 years, shift work schedule, working experience 6-10 years, and Intermediate-type MEQ score.

Perception of the risk of medication errors is present in near one out of two midwives in Italy. In particular, younger midwives with lower working experience, engaged in shift work, and belonging to an Intermediate chronotype, seem to be at higher risk of potential medication error. Since early morning hours seem to represent highest risk frame for female healthcare workers, shift work is not always aligned with individual circadian preference. Assessment of chronotype could represent a method to identify healthcare personnel at higher risk of circadian disruption.
Perception of the risk of medication errors is present in near one out of two midwives in Italy. In particular, younger midwives with lower working experience, engaged in shift work, and belonging to an Intermediate chronotype, seem to be at higher risk of potential medication error. Since early morning hours seem to represent highest risk frame for female healthcare workers, shift work is not always aligned with individual circadian preference. Assessment of chronotype could represent a method to identify healthcare personnel at higher risk of circadian disruption.Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGIB) who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML). Using prospective observational registry, 1439 out of 3363 consecutive patients were enrolled. Primary outcomes included adverse events such as mortality, hypotension, and rebleeding within 7 days. Four machine learning algorithms, namely, logistic regression with regularization (LR), random forest classifier (RF), gradient boosting classifier (GB), and voting classifier (VC), were compared with the Glasgow-Blatchford score (GBS) and Rockall scores. The RF model showed the highest accuracies and significant improvement over conventional methods for predicting mortality (area under the curve RF 0.917 vs. GBS 0.710), but the performance of the VC model was best in hypotension (VC 0.757 vs. GBS 0.668) and rebleeding within 7 days (VC 0.733 vs. GBS 0.694). Clinically significant variables including blood urea nitrogen, albumin, hemoglobin, platelet, prothrombin time, age, and lactate were identified by the global feature importance analysis. These results suggest that ML models will be useful early predictive tools for identifying high-risk patients with initially stable non-variceal UGIB admitted at an emergency department.Dairy products occupy a special place among foods in contributing to a major part of our nutritional requirements, while also being prone to fraud. Hence, the verification of the authenticity of dairy products is of prime importance. Multiple stable isotopic studies have been undertaken that demonstrate the efficacy of this approach for the authentication of foodstuffs. However, the authentication of dairy products for geographic origin has been a challenge due to the complex interactions of geological and climatic drivers. This study applies stable isotope measurements of d2H, d18O, d13C and d15N values from casein to investigate the inherent geo-climatic variation across dairy farms from the South and North Islands of New Zealand. The stable isotopic ratios were measured for casein samples which had been separated from freeze-dried whole milk samples. As uniform feeding and fertilizer practices were applied throughout the sampling period, the subtropical (North Island) and temperate (South Island) climates were reflected in the variation of d13C and d15N.