Accelerated Postnatal Growth Increases Lipogenic Gene Expression and Adipocyte Size in Low–Birth Weight Mice

OBJECTIVE: To characterize the hormonal milieu and adipose gene expression in response to catch-up growth (CUG), a growth pattern associated with obesity and diabetes risk, in a mouse model of low birth weight (LBW). RESEARCH DESIGN AND METHODS: ICR mice were food restricted by 50% from gestational days 12.5–18.5, reducing offspring birth weight by 25%. During the suckling period, dams were either fed ad libitum, permitting CUG in offspring, or food restricted, preventing CUG. Offspring were killed at age 3 weeks, and gonadal fat was removed for RNA extraction, array analysis, RT-PCR, and evaluation of cell size and number. Serum insulin, thyroxine (T4), corticosterone, and adipokines were measured. RESULTS: At age 3 weeks, LBW mice with CUG (designated U-C) had body weight comparable with controls (designated C-C); weight was reduced by 49% in LBW mice without CUG (designated U-U). Adiposity was altered by postnatal nutrition, with gonadal fat increased by 50% in U-C and decreased by 58% in U-U mice (P less than 0.05 vs. C-C mice). Adipose expression of the lipogenic genes Fasn, AccI, Lpin1, and Srebf1 was significantly increased in U-C compared with both C-C and U-U mice (P less than 0.05). Mitochondrial DNA copy number was reduced by greater than 50% in U-C versus U-U mice (P = 0.014). Although cell numbers did not differ, mean adipocyte diameter was increased in U-C and reduced in U-U mice (P less than 0.01). CONCLUSIONS: CUG results in increased adipose tissue lipogenic gene expression and adipocyte diameter but not increased cellularity, suggesting that catch-up fat is primarily associated with lipogenesis rather than adipogenesis in this murine model

Random subwindows and extremely randomized trees for image classification in cell biology

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems

Current trend in synthesis, Post-Synthetic modifications and biological applications of Nanometal-Organic frameworks (NMOFs)

Since the early reports of MOFs and their interesting properties, research involving these materials has grown wide in scope and applications. Various synthetic approaches have ensued in view of obtaining materials with optimised properties, the extensive scope of application spanning from energy, gas sorption, catalysis biological applications has meant exponentially evolved over the years. The far‐reaching synthetic and PSM approaches and porosity control possibilities have continued to serve as a motivation for research on these materials. With respect to the biological applications, MOFs have shown promise as good candidates in applications involving drug delivery, BioMOFs, sensing, imaging amongst others. Despite being a while away from successful entry into the market, observed results in sensing, drug delivery, and imaging put these materials on the spot light as candidates poised to usher in a revolution in biology. In this regard, this review article focuses current approaches in synthesis, post functionalization and biological applications of these materials with particular attention on drug delivery, imaging, sensing and BioMOFs