Covalent attachment of resveratrol to stainless steel toward the development of a resveratrol-releasing bare-metal stent.

Herein, we describe the covalent attachment of resveratrol, a naturally occurring antioxidant, to the surface of stainless-steel as a model for designing a novel bare-metal stent to treat coronary artery disease. Resveratrol has been shown to reduce oxidative stress in dysfunctional endothelial cells, and stimulate arterial healing. Resveratrol treatments, however, are limited by low water solubility, such that a localized delivery to the site of arterial narrowing via a coated stent presents a promising strategy for improving stent outcomes. Our attachment strategy utilizes zirconium vapor deposition to lay down a thin layer of zirconium oxide with labile hydrocarbon groups at the surface. Resveratrol can displace these hydrocarbons in aprotic solvent to afford a covalently attached layer of resveratrol. We evaluated the release of resveratrol under a range of pH levels, including physiological conditions (pH = 7.4 and 37 °C). Furthermore, we established that endothelial cells grown on a resveratrol-bound surface release elevated nitric oxide levels compared to controls, a key endothelial signaling molecule responsible for arterial health. These results are promising toward the development of a resveratrol-coated bare-metal stent to improve patient outcomes.

Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.

BACKGROUND: The gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks. METHODS: A total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels. RESULTS: Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus. CONCLUSIONS: Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.

Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease.

BACKGROUND: Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. RESULTS: We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response. CONCLUSIONS: We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.

Retention of an autologous endothelial layer on a bioprosthetic valve for the treatment of chronic deep venous insufficiency.

PURPOSE: Percutaneous transcatheter implantation of porcine small intestinal submucosa (SIS) bioprosthetic valves has been reported as a treatment for chronic deep venous insufficiency (CDVI). Endothelial progenitor outgrowth cells (EOCs), isolated from whole ovine blood, were evaluated as a source of in vitro autologous seeding for SIS endothelialization. Retention of the EOC monolayer was evaluated to test the feasibility of delivering an endothelialized SIS valve. MATERIALS AND METHODS: Twenty bioprosthetic venous valves were constructed from SIS sutured onto collapsible square stent frames and were seeded with ovine EOCs in vitro. Retention of the endothelial monolayer through valve loading and delivery (three valves), in vitro flow (three valves), and ex vivo flow (four valves) was evaluated with immunofluorescent staining and histologic analysis compared with paired unmanipulated control valves. In the ex vivo shunt loop, venous blood was pulled from an implanted dialysis catheter, through the valve, and returned to the sheep. RESULTS: Immunofluorescent staining of EOCs on the valves after in vitro seeding revealed a confluent monolayer (95.6% ± 2.3% confluent) on each side of the valve. When examined by immunofluorescent staining, the endothelial monolayer remained intact after loading and delivery (97.1% ± 1.7%) and when subjected to flow in the in vitro loop (96.0% ± 3.0%). Histologic analysis of the valves subjected to the ex vivo shunt loop revealed retention of the endothelial monolayer. CONCLUSIONS: Endothelial monolayers seeded on SIS were retained under loading and delivery, in vitro flow, and ex vivo flow. EOCs are a promising cell source for autologous endothelialization of bioprosthetic valves for the treatment of CDVI.