Synovial tissue metabolomics analysis of the therapeutic effects of stir-fried Xanthii Fructus on rheumatoid arthritis in rats.

Rheumatoid arthritis (RA) is a rheumatic disease that easily causes synovial hyperplasia and joint damage. Comprehensive metabolomic profiling of synovial tissue can reveal local pathological changes during RA and identify metabolites as candidate biomarkers. Detecting metabolites in synovial tissue can more directly reflect the pathological state and disease activity associated with it.

Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

Purpose: To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields.

Methods: In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

Purpose: Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.

Methods: SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs.

Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

Purpose: To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP).

Methods: Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.