Mouse Anti newt fibronectin Hybridoma [NU5]

To identify new candidate genes in osteoporosis, mainly involved in epigenetic mechanisms, we compared whole gene-expression in osteoblasts (OBs) obtained from women undergoing hip replacement surgery due to fragility fracture and severe osteoarthritis. Then, we analyzed the association of several SNPs with BMD in 1028 women. Microarray analysis yielded 2542 differentially expressed transcripts belonging to 1798 annotated genes, of which 45.6% (819) were overexpressed, and 54.4% (979) underexpressed (fold-change between-7.45 and 4.0). Among the most represented pathways indicated by transcriptome analysis were chondrocyte development, positive regulation of bone mineralization, BMP signaling pathway, skeletal system development and Wnt signaling pathway. In the translational stage we genotyped 4 SNPs in DOT1L, HEY2, CARM1 and DNMT3A genes. Raw data analyzed against inheritance patterns showed a statistically significant association between a SNP of DNMT3A and femoral neck-(FN) sBMD and primarily a SNP of CARM1 was correlated with both FN and lumbar spine-(LS) sBMD. Most of these associations remained statistically significant after adjusting for confounders. In analysis with anthropometric and clinical variables, the SNP of CARM1 unexpectedly revealed a close association with BMI (p=0.000082), insulin (p=0.000085), and HOMA-(IR) (p=0.000078). In conclusion, SNPs of the DNMT3A and CARM1 genes are associated with BMD, in the latter case probably owing to a strong correlation with obesity and fasting insulin levels.

Glaucoma is the second-most dominant cause for irreversible blindness, resulting in damage to the optic nerve. Ophthalmologist diagnoses this disease using a retinal examination of the dilated pupil. Since the diagnosis is a manual and laborious procedure, an automated approach for faster diagnosis is desirable. Convolutional neural networks (CNN) could allow automation of the diagnosis procedure due to their self-learning capabilities. This paper presents a deep learning-based glaucoma classification network (GC-NET) for classifying a retinal image as glaucomatous or non-glaucomatous. The proposed GC-NET has been tested on RIM-One and Drishti datasets. Our experimental results showed that GC-NET achieves accuracy of 97.51%, sensitivity of 98.78% and specificity of 96.20% with 0.81 true positive (Tp), 0.03 false positive (Fp), 0.76 true negative (Tn) and 0.01 false negative (Fn) which outperforms state of the art. Thus, the proposed approach could be very useful for initial screening of glaucoma patients.