Analysis of intestinal flora and cognitive function in maintenance hemodialysis patients using combined 16S ribosome DNA and shotgun metagenome sequencing.

BACKGROUND: Cognitive impairment is widely prevalent in maintenance hemodialysis (MHD) patients, and seriously affects their quality of life. The intestinal flora likely regulates cognitive function, but studies on cognitive impairment and intestinal flora in MHD patients are lacking. METHODS: MHD patients (36) and healthy volunteers (18) were evaluated using the Montreal Cognitive Function Scale, basic clinical data, and 16S ribosome DNA (rDNA) sequencing. Twenty MHD patients and ten healthy volunteers were randomly selected for shotgun metagenomic analysis to explore potential metabolic pathways of intestinal flora. Both16S rDNA sequencing and shotgun metagenomic sequencing were conducted on fecal samples. RESULTS: Roseburia were significantly reduced in the MHD group based on both 16S rDNA and shotgun metagenomic sequencing analyses. Faecalibacterium, Megamonas, Bifidobacterium, Parabacteroides, Collinsella, Tyzzerella, and Phascolarctobacterium were positively correlated with cognitive function or cognitive domains. Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included oxidative phosphorylation, photosynthesis, retrograde endocannabinoid signaling, flagellar assembly, and riboflavin metabolism. CONCLUSION: Among the microbiota, Roseburia may be important in MHD patients. We demonstrated a correlation between bacterial genera and cognitive function, and propose possible mechanisms.

Full supervised learning for osteoporosis diagnosis using micro-CT images.

Early osteoporosis diagnosis is of important significance for reducing fracture risk. Image analysis provides a new perspective for noninvasive diagnosis in recent years. In this article, we propose a novel method based on machine-learning method performed on micro-CT images to diagnose osteoporosis. The aim of this work is to find a way to more effectively and accurately diagnose osteoporosis on which many methods have been proposed and practiced. In this method, in contrast to the previously proposed methods in which features are analyzed individually, several features are combined to build a classifier for distinguishing osteoporosis group and normal group. Twelve features consisting of two groups are involved in our research, including bone volume/total volume (BV/TV), bone surface/bone volume (BS/BV), trabecular number (Tb.N), obtained from the software of micro-CT, and other four features from volumetric topological analysis (VTA). Support vector machine (SVM) method and k-nearest neighbor (kNN) method are introduced to create classifiers with these features due to their excellent performances on classification. In the experiment, 200 micro-CT images are used in which half are from osteoporosis patients and the rest are from normal people. The performance of the obtained classifiers is evaluated by precision, recall, and F-measure. The best performance with precision of 100%, recall of 100%, and F-measure of 100% is acquired when all the features are included. The satisfying result demonstrates that SVM and kNN are effective for diagnosing osteoporosis with micro-CT images.