Comparative effectiveness of telemedicine strategies on type 2 diabetes management: A systematic review and network meta-analysis

The effects of telemedicine strategies on the management of diabetes is not clear. This study aimed to investigate the impact of different telemedicine strategies on glycaemic control management of type 2 diabetes patients. A search was performed in 6 databases from inception until September 2016 for randomized controlled studies that examined the use of telemedicine in adults with type 2 diabetes. Studies were independently extracted and classified according to the following telemedicine strategies: teleeducation, telemonitoring, telecase-management, telementoring and teleconsultation. Traditional and network meta-analysis were performed to estimate the relative treatment effects. A total of 107 studies involving 20,501 participants were included. Over a median of 6 months follow-up, telemedicine reduced haemoglobin A1c (HbA1c) by a mean of 0.43% (95% CI: -0.64% to -0.21%). Network meta-analysis showed that all telemedicine strategies were effective in reducing HbA1c significantly compared to usual care except for telecase-management and telementoring, with mean difference ranging from 0.37% and 0.71%. Ranking indicated that teleconsultation was the most effective telemedicine strategy, followed by telecase-management plus telemonitoring, and finally teleeducation plus telecase-management. The review indicates that most telemedicine strategies can be useful, either as an adjunct or to replace usual care, leading to clinically meaningful reduction in HbA1c

Caloric Restriction and Rapamycin Differentially Alter Energy Metabolism in Yeast

Rapamycin (RM), a drug that inhibits the mechanistic target of rapamycin (mTOR) pathway and responds to nutrient availability, seemingly mimics the effects of caloric restriction (CR) on healthy life span. However, the extent of the mechanistic overlap between RM and CR remains incompletely understood. Here, we compared the impact of CR and RM on cellular metabolic status. Both regimens maintained intracellular ATP through the chronological aging process and showed enhanced mitochondrial capacity. Comparative transcriptome analysis showed that CR had a stronger impact on global gene expression than RM. We observed a like impact on the metabolome and identified distinct metabolites affected by CR and RM. CR severely reduced the level of energy storage molecules including glycogen and lipid droplets, whereas RM did not. RM boosted the production of enzymes responsible for the breakdown of glycogen and lipid droplets. Collectively, these results provide insights into the distinct energy metabolism mechanisms induced by CR and RM, suggesting that these two anti-aging regimens might extend life span through distinctive pathways

Osseointegrated implants in craniofacial application: Current status

Singapore Dental Journal2911-1

Transposon mutagenesis identifies genes driving hepatocellular carcinoma in a chronic hepatitis B mouse model

10.1038/ng.2847Nature Genetics46124-32NGEN

A guide to the BRAIN initiative cell census network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.Horizon 2020 (H2020)R01 NS096720Radiolog