Clinical Phenotypes and Cellular Mediators in Diabetic Retinopathy

The aim of this work was to establish meaningful clinical end-points and surrogate markers for diabetic macular ischaemia, a condition for which there is no treatment. The relationship between diabetic eye disease and circulating cellular mediators of angiogenesis and inflammation was further explored with a view to developing therapy in the longer term. Visual loss in diabetic macular ischaemia was observed to occur only in moderate to severe disease, progresses at a rate of 5-10% increase in area per year and associated with thinning of the retinal nerve fibre layer. Direct visualisation of cells in the vitreous was achieved using optical coherence tomography. Novel methods for this were further developed in inflammatory eye disease, with a view for application in diabetic eye disease. A method for in vivo labelling of cells using ICG to enhance visualisation was described. In the field of regenerative medicine, this technique may allow direct visualisation of cell-mediated inflammation regardless of the type of cell or tissue transplanted. EPC and monocyte profiles were analysed in the context of diabetic eye disease. Elevated levels of EPCs as defined by CD34+ CD309+ were observed in diabetes, but no associations were observed with progression. There were no initial associations between monocyte subsets and diabetic eye disease severity at the outset but differences were observed in the context of progression. Observations from this work support the notion that inflammation plays an important role in diabetic eye disease and will inform development of new treatments in this field

Additional file 1: Table S1. of Informed walks: whispering hints to gene hunters inside networks’ jungle

Common and Exclusive genes between the seven subnetworks for each different cancer type. Table S2. Significant pathways for the case of common genes between the seven cancer types. Table S3. Significant pathways for the case of the exclusive breast cancer genes. Table S4. Significant pathways for the case of the exclusive colon cancer genes. Table S5. Significant pathways for the case of the exclusive colorectal cancer genes. Table S6. Significant pathways for the case of the exclusive rectum genes. Table S7. Significant pathways for the case of the exclusive ovarian cancer genes. Table S8. Significant pathways for the case of the exclusive glioma genes. Table S9. Significant pathways for the case of the exclusive glioblastoma genes. Table S10. Significant pathways for the case of breast cancer. Table S11. Significant pathways for the case of colon cancer. Table S12. Significant pathways for the case of colorectal cancer. Table S13. Significant pathways for the case of rectum. Table S14. Significant pathways for the case of ovarian cancer. Table S15. Significant pathways for the case of glioblastoma. Table S16. Significant pathways for the case of glioma. Table S17. Common and Exclusive mechanisms between the seven different cancer types. Table S18. Common and exclusive repurposed drugs of each cancer type. (DOCX 52 kb

Statistics of the predicted plant antimicrobial peptides.

<p>Statistics of the predicted plant antimicrobial peptides.</p

Comparative overview of other antimicrobial peptide studies.

<p>Comparative overview of other antimicrobial peptide studies.</p

Snapshots from the C-PAmP Database.

<p>Snapshots from the C-PAmP Database.</p

Snapshots from the web interface of C-PAmP Database.

<p>Snapshots from the web interface of C-PAmP Database.</p

C-PAmP predictions for 6 antimicrobial regions in protein O24006 of Impatiens balsamina (Balsam) in comparison with the corresponding annotations in UniProtKB/Swiss-Prot.

<p>C-PAmP predictions for 6 antimicrobial regions in protein O24006 of Impatiens balsamina (Balsam) in comparison with the corresponding annotations in UniProtKB/Swiss-Prot.</p

Distribution of predicted probabilities for antimicrobial (a) and non-antimicrobial (b) samples.

<p>Distribution of predicted probabilities for antimicrobial (a) and non-antimicrobial (b) samples.</p

Maximum, minimum and average values of Accuracy, Sensitivity, Specificity and Matthews Correlation Coefficient (MCC) for a 10-fold cross-validation.

<p>Maximum, minimum and average values of Accuracy, Sensitivity, Specificity and Matthews Correlation Coefficient (MCC) for a 10-fold cross-validation.</p