The role of Calcineurin in skeletal muscle differentiation

1. Strong evidence suggests that Calcineurin levels are higher in fast muscle fibers compared to slow-twitch in resting skeletal muscles. Activation of the Calcineurin in 4 skeletal muscle myocytes selectively up-regulates slowfiber- specific gene promoters through a mechanism involving the transcription factor NFATcI. The Calcineurin pathway itself was down-regulated when rat skeletal muscles were chronically stimulated at 10 FIz for a period of 3 weeks illustrating adaptation. 2. Skeletal muscles that received chronic stimulation treatment showed a significant increase in mitochondrial content. Histochemical studies detected a change towards the slow phenotype, through the decrease of fast-twitch Type Bib fiber content in fast skeletal muscles. Metabolic activity was not significantly affected through this period of chronic stimulation. 3. Cyclosporin A was not able to prevent this initial transition towards the slow phenotype, even though 3 weeks of 10 Hz chronic stimulation was insufficient to cause marked changes in the skeletal muscle metabolism. This suggests an incomplete fast-to-slow transformation was elicited by these conditions. 4. Stimulation of L6 myocytes with the calcium ionophore 4-Bromo-A23 187 (I 0 M) a partial fast-to-slow transformation occurred. It is likely that this change was brought about by a number of processes including NFAT translocation to the nucleus

A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed

A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users? trust in these. This paper provides: (a) measures to evaluate the novelty of the users? recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust

Incorporating reliability measurements into the predictions of a recommender system

In this paper we introduce the idea of using a reliability measure associated to the predic- tions made by recommender systems based on collaborative filtering. This reliability mea- sure is based on the usual notion that the more reliable a prediction, the less liable to be wrong. Here we will define a general reliability measure suitable for any arbitrary recom- mender system. We will also show a method for obtaining specific reliability measures specially fitting the needs of different specific recommender systems

Combining steroid and global metabolome profiling by mass spectrometry with machine learning to investigate metabolic risk in benign adrenal tumours with mild autonomous cortisol secretion

Background: Benign adrenal tumours are discovered in 3-10% of adults and can be non-functioning (NFAT) or associated with adrenal hormone excess, most frequently mild autonomous cortisol secretion (MACS) defined by the failure to suppress cortisol after 1 mg dexamethasone overnight but lack of distinct signs of Cushing’s syndrome (CS). We found that MACS increases the prevalence and severity of type 2 diabetes and hypertension and primarily affects women (Ann Int Med. 2022 Doi:10.7326/M21-1737).Objectives: We prospectively recruited 1305 patients with benign adrenal tumours to assess their steroid and global metabolomes and determine links to type 2 diabetes and hypertension.Methods: We analysed 24-h urine samples from 1305 patients (649 NFAT, 591 MACS, 65 CS) using a 17-steroid liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay. We also performed untargeted serum metabolome analysis in a representative sub-cohort of 290 patients (104 NFAT, 140 MACS, 47 CS) employing HILIC and C18-lipidomics LC-MS assays. The data were analysed by two supervised machine learning approaches, generalized matrix learning vector quantization and ordinal regression, to identify the most relevant metabolic changes.Results: Urine steroid metabolome analysis revealed increased glucocorticoid metabolite excretion from NFAT over MACS to CS, whereas androgen metabolite excretion decreased. Similarly, increased glucocorticoid metabolites were observed in patients with type 2 diabetes and hypertension. Lipidome analysis revealed gradual progression towards lipotoxicity with increasing cortisol excess. Patients with type 2 diabetes showed additional changes in acylcarnitines, bioactive lipids, and triacylglycerides.Conclusions: We provide mechanistic insights into the metabolic consequences of cortisol excess. Increased cortisol was linked to a change in the lipidome towards lipotoxicity. Patients with type 2 diabetes and hypertension had increased glucocorticoid output and more adverse changes in the lipidome, indicative of a causative contribution of cortisol excess to their higher cardiometabolic burden. Observed changes may hold promise for risk stratification in MACS, a highly relevant and previously largely overlooked metabolic risk condition