Human exome sequences contain 15,000-20,000 variants but many variants have unknown clinical impact. In silico predictive classifiers are recognized by the American College of Medical Genetics as a resource for interpreting these “variants of uncertain significance.” Many in silico classifiers have been developed, of which PolyPhen-2 is highly successful and widely used. PolyPhen-2 uses a naïve Bayes model to synthesize sequence, structural and genomic information. I investigated whether predictive performance could be improved by replacing PolyPhen-2’s naïve Bayes model with alternative machine learning methods.

Classifiers using the PolyPhen-2 feature set were retrained using extreme gradient boosting (XGBoost), random forests, artificial neural networks, and support vector machines. compound library chemical Classifiers were externally validated on “pathogenic” and “benign” ClinVar variants absent from the training datasets. Software is implemented in Python and is freely available at https//github.com/djparente/polyboost and the Python Package Index (PyPI) under the BSD license.

An XGBoost-based classifier-designated PolyBoost (PolyPhen-2 Booster)-improves discriminative performance and calibration relative to PolyPhen-2 in external validation on ClinVar.

PolyBoost analyzes PolyPhen-2 output and can be incorporated into existing bioinformatics workflows as a post-analysis method to improve interpretation of clinical exome sequences obtained to identify monogenic disease.

PolyBoost analyzes PolyPhen-2 output and can be incorporated into existing bioinformatics workflows as a post-analysis method to improve interpretation of clinical exome sequences obtained to identify monogenic disease.

Observational studies have consistently reported that serum urate positively correlates with bone mineral density (BMD). The aim of this study was to determine whether moderate hyperuricaemia induced by inosine supplements influences bone turnover markers in post-menopausal women over a six-month period.

One hundred and twenty post-menopausal women were recruited into a six-month randomised, double-blind, placebo-controlled trial. Key exclusion criteria were osteoporosis, previous fragility fracture, bisphosphonate therapy, gout, kidney stones, and urine pH ≤5.0. Participants were randomised 11 to placebo or inosine. The co-primary endpoints were change in procollagen type-I N-terminal propeptide (PINP) and change in β-C-terminal telopeptide of type I collagen (β-CTX). Change in BMD measured by dual-energy x-ray absorptiometry was an exploratory endpoint.

Administration of inosine led to a significant increase in serum urate over the study period (P<0.0001 for all follow-up time-points). At week 26, the mean change in serum urate was +0.13 mmol/L (+2.2mg/dL) in the inosine group and 0.00mmol/L (0mg/dL) in the placebo group. There was no difference in PINP or β-CTX between groups over the six months. There were no significant changes in bone density between groups over the six months. Adverse events and serious adverse events were similar between the two groups.

This clinical trial shows that although inosine supplementation leads to sustained increases in serum urate over a six month period, it does not alter markers of bone turnover in post-menopausal women. These findings do not support the concept that urate has direct biological effects on bone turnover.

This clinical trial shows that although inosine supplementation leads to sustained increases in serum urate over a six month period, it does not alter markers of bone turnover in post-menopausal women. These findings do not support the concept that urate has direct biological effects on bone turnover.Large size cell-laden hydrogel models hold great promise for tissue repair and organ transplantation, but their fabrication using 3D bioprinting is limited by the slow printing speed that can affect the part quality and the biological activity of the encapsulated cells. Here a fast hydrogel stereolithography printing (FLOAT) method is presented that allows the creation of a centimeter-sized, multiscale solid hydrogel model within minutes. Through precisely controlling the photopolymerization condition, low suction force-driven, high-velocity flow of the hydrogel prepolymer is established that supports the continuous replenishment of the prepolymer solution below the curing part and the nonstop part growth. The rapid printing of centimeter-sized hydrogel models using FLOAT is shown to significantly reduce the part deformation and cellular injury caused by the prolonged exposure to the environmental stresses in conventional 3D printing methods. Embedded vessel networks fabricated through multiscale printing allows media perfusion needed to maintain the high cellular viability and metabolic functions in the deep core of the large-sized models. The endothelialization of this vessel network allows the establishment of barrier functions. Together, these studies demonstrate a rapid 3D hydrogel printing method and represent a first step toward the fabrication of large-sized engineered tissue models.

To assess epicardial adipose tissue volume (EATv) and its link to coronary atherosclerosis and plaque morphology in patients with rheumatoid arthritis (RA) and age- and gender-matched controls.

Computed tomography angiography evaluated EATv and coronary plaque in 139 RA patients and 139 non-RA controls. All models assessing the effect of EATv on plaque adjusted for age, gender, hypertension, diabetes, dyslipidemia, smoking, family history of coronary artery disease, and obesity (body mass index≥30 kg/m

).

Mean (±standard deviation [SD]) log-transformed EATv was similar in RA (4.69±0.36) and controls (4.70±0.42). EATv was higher in RA patients with atherosclerosis versus those without (P=0.046). In stratified analyses, EATv associated with number of segments with plaque in RA (rate ratio=1.20 [95%CI 1.01-1.41] per 1-SD increment in EATv) but not controls (P-for-interaction=0.089). Likewise, EATv (per 1-SD increment) related to the presence of multivessel or obstructive disease (odds ratio [OR]=1.63 [95%CI 1.