Pantothenic acid - a scoping review for Nordic Nutrition Recommendations 2023.

Pantothenic acid, also referred to as vitamin B5, is a water-soluble vitamin that has essential functions in the body as a component of coenzyme A (CoA) and acyl carrier protein (ACP). It is widely distributed in animal and plant-source foods. Nutritional deficiency of pantothenic acid is rare and toxicity negligible. Information on pantothenic acid intakes in the Nordic countries is limited and biomarker data from Nordic and Baltic populations is missing. Due to a lack of data, no dietary reference values (DRVs) were given for pantothenic acid in the Nordic Nutrition Recommendations (NNR) since 2012. The aim of this scoping review was to examine recent evidence relevant for updating the DRVs for NNR2023. Scientific literature since 2012 on associations of pantothenic acid with health-related issues in Nordic and Baltic countries was searched. No health concerns related to pantothenic acid were identified.

A psychological symptom based machine learning model for clinical evaluation of irritable bowel syndrome.

Background: Irritable bowel syndrome (IBS) is a chronic functional gastrointestinal disorder characterized by recurrent abdominal pain associated with alterations  in stool form and/or stool frequency. Co-morbidities such as anxiety, depression, fatigue, and insomnia are frequently reported by patients suffering from IBS. Identification of these symptoms should thus be an integral part of an IBS assessment.      However, an optimal tool to screen for core psychological symptoms in IBS is still  missing. Here, we aim to develop a psychological symptom based machine learning model to efficiently help clinicians to identify patients suffering from IBS. Methods: We developed a machine learning workflow to select the most significant psychological features associated with IBS in a dataset including 49 patients with IBS and 35 healthy controls. These features were used to train three different types of machine learning models: logistic regression, decision trees and support vector machine classifiers; which were validated on a holdout validation dataset and an unseen test set. The performance of these models was compared in terms of balanced accuracy scores. Results: A logistic regression model including a combination of symptom features associated with anxiety and fatigue resulted in a balanced accuracy score of 0.93 (0.81-1.0) on unseen test data and outperformed the other comparable models. The same model correctly identified all patients with IBS in a test set (recall score 1) and misclassified one non-IBS subject (precision score 0.91). A complementary post-hoc leave-one-out cross validation analysis including the same symptom features showed similar, but slightly inferior results (balanced accuracy 0.84, recall 0.88, precision 0.86). Conclusions: Inclusion of machine learning based psychological evaluation can complement and improve existing clinical procedure for diagnosis of IBS.