Hyperglycemia in Children Hospitalized with Acute Asthma.

Hyperglycemia is frequently observed in adults with acute asthma. We aimed to assess the frequency of hyperglycemia and its relation to outcomes in children admitted with acute asthma. In this retrospective study, we reviewed medical records of non-diabetic 166 children (66 girls) with the mean age of 5.4 ± 2.6 years (range of 2-12 years), who were hospitalized with acute asthma between January 2012 through December 2014. Data pertaining to demographics, vital signs, oxygen saturation, serum blood glucose level, electrolytes, blood gases, and admission were collected. Children with other chronic conditions were excluded. The findings were that hyperglycemia (blood glucose ≥ 11.1 mmol/l) was observed in 38.6% of children. The median baseline blood glucose (IQR) was 9.8 mmol/l (7.2-13.3 mmol/l). Blood glucose level was associated with the length of hospitalization, with a median extension of 1.8 days, but was inversely associated with the serum potassium and bicarbonate levels. There were no associations between baseline blood glucose and age, gender, baseline respiratory rate, oxygen saturation, or intensive care admission. Hyperglycemia resolved spontaneously in all affected children. We conclude that hyperglycemia is common in children hospitalized with acute asthma. Hyperglycemia could be considered as a marker of a longer hospital stay.

Comparing PTR-MS profile of milk inoculated with pure or mixed cultures of spoilage bacteria.

The volatile organic compounds (VOCs) associated with UHT milk (n=8) inoculated with either pure inoculums of Pseudomonas fluorescens (two strains tested) or Chryseobacterium sp., or with mixed cultures of 2 or all 3 of the bacterial strains, and held at 4.5 °C for up to 26 days was measured using proton transfer reaction - mass spectrometry (PTR-MS). The VOCs evolved included a range of carbonyl compounds, alcohols, esters, and acids and had significant qualitative and quantitative differences between the inoculums. Milks inoculated with paired (mixed) bacterial cultures attained patterns similar to the VOC composition of one of the pure inoculums, which could be attributed to the domination of these bacteria within the mixed inoculum. This study will help to characterize the spoilage of milk and provide important insights into understanding the factors that limit the shelf life of milk.

Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.

The inputs to the outputs of nonlinear systems can be modeled using machine and deep learning approaches, among which artificial neural networks (ANNs) are a promising option. However, noisy signals affect ANN modeling negatively; hence, it is important to investigate these signals prior to the modeling. Herein, two customized and simple approaches, visual inspection and absolute correlation, are proposed to examine the relationship between the inputs and outputs of a nonlinear system. The system under consideration uses biosignals from surface electromyography as inputs and human finger joint angles as outputs, acquired from eight intact participants performing movements and grasping tasks in dynamic conditions. Furthermore, the results of these approaches are tested using the standard mutual information measure. Hence, the system dimensionality is reduced, and the ANN learning (convergence) is accelerated, where the most informative inputs are selected for the next phase. Subsequently, four ANN types, i.e., feedforward, cascade-forward, radial basis function, and generalized regression ANNs, are used to perform the modeling. Finally, the performance of the ANNs is compared with findings from the signal analysis. Results indicate a high level of consistency among all the aforementioned signal pre-analysis techniques from one side, and they also indicate that these techniques match the ANN performances from the other side. As an example, for a certain movement set, the ANN models resulted in the rotation estimation accuracy of the joints in the following descending order: carpometacarpal, metacarpophalangeal, proximal interphalangeal, and distal interphalangeal. This information has been indicated in the signal pre-analysis step. Therefore, this step is crucial in input-output variable selections prior to machine-/deep-learning-based modeling approaches.

The "Grass-Fed" Milk Story: Understanding the Impact of Pasture Feeding on the Composition and Quality of Bovine Milk.

Milk is a highly nutritious food that contains an array of macro and micro components, scientifically proven to be beneficial to human health. While the composition of milk is influenced by a variety of factors, such as genetics, health, lactation stage etc., the animal's diet remains a key mechanism by which its nutrition and processing characteristics can be altered. Pasture feeding has been demonstrated to have a positive impact on the nutrient profile of milk, increasing the content of some beneficial nutrients such as Omega-3 polyunsaturated fatty acids, vaccenic acid, and conjugated linoleic acid (CLA), while reducing the levels of Omega-6 fatty acids and palmitic acid. These resultant alterations to the nutritional profile of "Grass-Fed" milk resonate with consumers that desire healthy, "natural", and sustainable dairy products. This review provides a comprehensive comparison of the impact that pasture and non-pasture feeding systems have on bovine milk composition from a nutritional and functional (processability) perspective, highlighting factors that will be of interest to dairy farmers, processors, and consumers.