Metformin, an established first-line treatment for patients with type 2 diabetes, has been associated with gastrointestinal (GI) adverse effects that limit its use. Histamine and serotonin have potent effects on the GI tract. The effects of metformin on histamine and serotonin uptake were evaluated in cell lines overexpressing several amine transporters (OCT1, OCT3 and SERT). Metformin inhibited histamine and serotonin uptake by OCT1, OCT3 and SERT in a dose-dependent manner, with OCT1-mediated amine uptake being most potently inhibited (IC50 = 1.5 mM). A chemoinformatics-based method known as Similarity Ensemble Approach predicted diamine oxidase (DAO) as an additional intestinal target of metformin, with an E-value of 7.4 × 10−5. Inhibition of DAO was experimentally validated using a spectrophotometric assay with putrescine as the substrate. The Ki of metformin for DAO was measured to be 8.6 ± 3.1 mM. In this study, we found that metformin inhibited intestinal amine transporters and DAO at concentrations that may be achieved in the intestine after therapeutic doses. Further studies are warranted to determine the relevance of these interactions to the adverse effects of metformin on the gastrointestinal tract.

Machine learning allows detecting specific physical activities using data from wearable sensors. Such a quantification of patient mobility over time promises to accurately inform clinical decisions for physical rehabilitation. There are two strategies of setting up the machine learning problem: detect one patient's activities using data from the same patient (personal model) or detect their activities using data from other patients (global model), and we currently do not know if personal models are necessary. Here we consider the problem of detecting physical activities from a waist-worn accelerometer in patients who use a knee-ankle-foot orthosis (KAFO) to walk. We show that while a model based on healthy subjects has low accuracy, the global model performs as well as the personal model. This is encouraging because it suggests that condition-specific activity recognition algorithms are sufficient and that no data from individual patients is necessary.

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BackgroundWearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition.ObjectiveThe objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals.MethodsData were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject.ResultsMedian accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition.ConclusionsOur results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations.

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