Skeletal muscle and intermuscular adipose tissue gene expression profiling identifies new biomarkers with prognostic significance for insulin resistance progression and intervention response

Although insulin resistance often leads to Type 2 Diabetes Mellitus (T2D), its early stages remain often unrecognized thus reducing the probability of successful prevention and intervention. Moreover, treatment efficacy is affected by the genetics of the individual patient. To identify potential candidate genes for the prediction of diabetes risk and intervention response we linked genetic expression profiles of human skeletal muscle and intermuscular adipose tissue (IMAT) to fasting glucose (FG) and glucose infusion rate (GIR). We found that genes with a strong association to these measures clustered into three distinct expression patterns. Their predictive values for insulin resistance varied strongly between muscle and IMAT. Moreover, we discovered that individual genetic expression based classifications may differ from those classifications based predominantly on clinical parameters indicating a potential incomplete patient stratification. Out of the 15 top hit candidate genes, we identified ST3GAL2, AASS, ARF1 and the transcription factor SIN3A as novel candidates for a refined diabetes risk and intervention response prediction. Our results confirm that disease progression and a successful intervention depend on individual genetics. We anticipate that our findings may lead to a better understanding and prediction of the individual diabetes risk and may help to develop individualized intervention strategies.

Assessment of Benefits and Risks of Probiotics in Processed Cereal-based Baby Foods Lactobacillus Paracasei ssp. Paracasei F19

The Norwegian Scientific Committee for Food Safety (VKM) has appointed an ad hoc-group of experts to answer a request from the Norwegian Food Safety Authority regarding benefit and risk assessment of Lactobacillus paracasei ssp. paracasei F19 (F19) in processed cerealbased baby foods intended for small children 1-3 years. This assessment is based on the literature provided by the notifier as well as that found by a MEDLINE search. A notification regarding two products of processed cereal-based baby foods (hereafter called cereals), intended for small children and supplemented with the bacterium F19 initiated this work. A daily supply of a monoculture of a particular bacterial strain in large quantities to an age group without a fully established intestinal flora, may have unknown adverse effects. There are however, to our knowledge, no studies investigating possible short or long term adverse health effects of F19 in processed cereal-based baby food given to children 13 months onwards. The documentation and information provided by the notifier regarding the genetic stability of F19 in the two products during processing and storage, is considered insufficient and does not allow any conclusions to be drawn. Moreover, the documentation obtained is not conclusive regarding the antibiotic resistance pattern of the bacterial strain used in the products in question, as the information on different antibiotics is partly inconsistent. The information about specific localization (chromosomal, plasmid) of the resistance genes is not sufficient. Studies demonstrate that F19, as well as other bacterial strains considered probiotic, is able to “crosstalk” with enterocytes in mice and that the result of the “crosstalk” depends upon the microbiota present. Whether F19 has a similar “crosstalk-profile” in humans is unknown. However, as the strain is originally of human origin, it seems reasonable to assume that such “crosstalk” may occur. Thus, before giving F19 daily for months and years, it seems reasonable to ask for additional molecular and physiological studies to unravel the functional impact of possible changes in genetic expression in children. Lactobacillus infections do occasionally occur, mainly as bacteremia, endocarditis and localized infections (e.g. abscesses, peritonitis, and meningitis) in patients with severe underlying diseases. Most of them are elderly, but children are not excluded. The species most often isolated are L. casei and L. rhamnosus, followed by L. paracasei. The increasing use of immunosuppressive therapy and broad spectrum antibiotics which are ineffective against Lactobacillus, might increase the importance of these bacteria as possible pathogens. In order to be able to draw any conclusions regarding beneficial effects of F19, there is a need for randomized placebo-controlled studies in larger populations and in the relevant age group. According to EFSA, Lactobacillus paracasei ssp. paracasei F19 is sufficiently characterized. The documentation provided is, however, not sufficient to claim positive health effects and thus F19 is not proven to be probiotic. There are no published dose-response studies of F19 in children, neither regarding survival of F19 in the gastrointestinal tract, nor possible negative health effects. Thus the potential for negative health effects as e.g. spreading of antimicrobial resistance or unfavourable impact on the genetic expression in children related to the frequency and/or dose of a monoculture of F19 cannot be assessed.

On the Application of Machine Learning in Savonius Wind Turbine Technology: An Estimation of Turbine Performance Using Artificial Neural Network and Genetic Expression Programming

This paper addresses the application of artificial neural network (ANN) and genetic expression programming (GEP), the popular artificial intelligence and machine learning methods, in order to estimate the Savonius wind rotor's performance based on different independent design variables. Savonius wind rotor is one of the competent members of the vertical axis wind turbines (VAWTs) due to its advantageous qualities such as direction independency, design simplicity, ability to perform at low wind speeds, potent standalone system. The available experimental data on Savonius wind rotor have been used to train the ANN and GEP using MATLAB R2020b and GeneXProTools 5.0 software, respectively. The input variables used in ANN and GEP architecture include newly proposed design shape factors, number of blades and stages, gap and overlap lengths, height and diameter of the rotor, free stream velocity, end plate diameter and tip speed ratio, besides cross-sectional area of wind tunnel test section. Based on this, the unknown governing function constituted by the aforementioned input variables is established using ANN and GEP to approximate/forecast the rotor performance as an output. The governing equation formulated by ANN is in the form of weights and biases, while GEP provides it in the form of traditional mathematical functions. The trained ANN and GEP are capable to estimate the rotor performance with R2 ≈ 0.97 and R2 ≈ 0.65, respectively, in correlation with the reported experimental rotor performance.