In vitro colonic metabolism of coffee and chlorogenic acid results in selective changes in human faecal microbiota growth

Coffee is a relatively rich source of chlorogenic acids (CGA), which, like other polyphenols are postulated to exert preventative effects against cardiovascular disease and type-2 diabetes. As a considerable proportion of ingested CGA reaches the large intestine, CGA may be capable of exerting beneficial effects in the large gut. Here we utilise a stirred, anaerobic, pH controlled, batch culture fermentation model of the distal region of the colon in order to investigate the impact of coffee and CGA on the growth of the human faecal microbiota. Incubation of the coffee with the human faecal microbiota led to the rapid metabolism of CGA (4h) and the production of dihydrocaffeic acid and dihydroferulic acid, whilst caffeine remained un-metabolised. The coffee with the highest levels of CGA (p<0.05, relative to the other coffees) induced a significant increase in Bifidobacterium spp. relative to the control at 10 hours post exposure (p<0.05). Similarly, an equivalent quantity of CGA (80.8mg; matched with that in high CGA coffee) induced a significant increase in Bifidobacterium spp. (p<0.05). CGA alone also induced a significant increase in the Clostridium coccoides-Eubacterium rectale group (p<0.05). This selective metabolism and subsequent amplification of specific bacterial populations could be beneficial to host health.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.