Zone specific fractal dimension of retinal images as predictor of stroke incidence

Fractal dimensions (FDs) are frequently used for summarizing the complexity of retinal vascular. However, previous techniques on this topic were not zone specific. A new methodology to measure FD of a specific zone in retinal images has been developed and tested as a marker for stroke prediction. Higuchi's fractal dimension was measured in circumferential direction (FDC) with respect to optic disk (OD), in three concentric regions between OD boundary and 1.5 OD diameter from its margin. The significance of its association with future episode of stroke event was tested using the Blue Mountain Eye Study (BMES) database and compared against spectrum fractal dimension (SFD) and box-counting (BC) dimension. Kruskal-Wallis analysis revealed FDC as a better predictor of stroke (H=5.80, P=0.016, α=0.05) compared with SFD (H=0.51, P=0.475, α=0.05) and BC (H=0.41, P=0.520, α=0.05) with overall lower median value for the cases compared to the control group. This work has shown that there is a significant association between zone specific FDC of eye fundus images with future episode of stroke while this difference is not significant when other FD methods are employed

Strain-Dependent Differences in Bone Development, Myeloid Hyperplasia, Morbidity and Mortality in Ptpn2-Deficient Mice

Single nucleotide polymorphisms in the gene encoding the protein tyrosine phosphatase TCPTP (encoded by PTPN2) have been linked with the development of autoimmunity. Here we have used Cre/LoxP recombination to generate Ptpn2ex2−/ex2− mice with a global deficiency in TCPTP on a C57BL/6 background and compared the phenotype of these mice to Ptpn2−/− mice (BALB/c-129SJ) generated previously by homologous recombination and backcrossed onto the BALB/c background. Ptpn2ex2−/ex2− mice exhibited growth retardation and a median survival of 32 days, as compared to 21 days for Ptpn2−/− (BALB/c) mice, but the overt signs of morbidity (hunched posture, piloerection, decreased mobility and diarrhoea) evident in Ptpn2−/− (BALB/c) mice were not detected in Ptpn2ex2−/ex2− mice. At 14 days of age, bone development was delayed in Ptpn2−/− (BALB/c) mice. This was associated with increased trabecular bone mass and decreased bone remodeling, a phenotype that was not evident in Ptpn2ex2−/ex2− mice. Ptpn2ex2−/ex2− mice had defects in erythropoiesis and B cell development as evident in Ptpn2−/− (BALB/c) mice, but not splenomegaly and did not exhibit an accumulation of myeloid cells in the spleen as seen in Ptpn2−/− (BALB/c) mice. Moreover, thymic atrophy, another feature of Ptpn2−/− (BALB/c) mice, was delayed in Ptpn2ex2−/ex2− mice and preceded by an increase in thymocyte positive selection and a concomitant increase in lymph node T cells. Backcrossing Ptpn2−/− (BALB/c) mice onto the C57BL/6 background largely recapitulated the phenotype of Ptpn2ex2−/ex2− mice. Taken together these results reaffirm TCPTP's important role in lymphocyte development and indicate that the effects on morbidity, mortality, bone development and the myeloid compartment are strain-dependent

CD164 identifies CD4+ T cells highly expressing genes associated with malignancy in Sézary syndrome: the Sézary signature genes, FCRL3, Tox, and miR-214

Sézary syndrome (SS), a leukemic variant of cutaneous T-cell lymphoma (CTCL), is associated with a significantly shorter life expectancy compared to skin-restricted mycosis fungoides. Early diagnosis of SS is, therefore, key to achieving enhanced therapeutic responses. However, the lack of a biomarker(s) highly specific for malignant CD4+ T cells in SS patients has been a serious obstacle in making an early diagnosis. We recently demonstrated the high expression of CD164 on CD4+ T cells from Sézary syndrome patients with a wide range of circulating tumor burdens. To further characterize CD164 as a potential biomarker for malignant CD4+ T cells, CD164+ and CD164-CD4+ T cells isolated from patients with high-circulating tumor burden, B2 stage, and medium/low tumor burden, B1-B0 stage, were assessed for the expression of genes reported to differentiate SS from normal controls, and associated with malignancy and poor prognosis. The expression of Sézary signature genes: T plastin, GATA-3, along with FCRL3, Tox, and miR-214, was significantly higher, whereas STAT-4 was lower, in CD164+ compared with CD164-CD4+ T cells. While Tox was highly expressed in both B2 and B1-B0 patients, the expression of Sézary signature genes, FCRL3, and miR-214 was associated predominantly with advanced B2 disease. High expression of CD164 mRNA and protein was also detected in skin from CTCL patients. CD164 was co-expressed with KIR3DL2 on circulating CD4+ T cells from high tumor burden SS patients, further providing strong support for CD164 as a disease relevant surface biomarker

Foxp3 and Treg cells in HIV-1 infection and immuno-pathogenesis

FoxP3+CD4+CD25+ regulatory T (Treg) cells are implicated in a number of pathologic processes including elevated levels in cancers and infectious diseases, and reduced levels in autoimmune diseases. Treg cells are activated to modulate immune responses to avoid over-reactive immunity. However, conflicting findings are reported regarding relative levels of Treg cells during HIV-1 infection and disease progression. The role of Treg cells in HIV-1 diseases (aberrant immune activation) is poorly understood due to lack of a robust model. We summarize here the regulation and function of Foxp3 in Treg cells and in modulating HIV-1 replication. Based on recent findings from SIV/monkey and HIV/humanized mouse models, a model of the dual role of Treg cells in HIV-1 infection and immuno-pathogenesis is discussed

A novel approach for quantification of contour irregularities of diabetic foot ulcers and its association with ischemic heart disease

Diabetic Foot Ulcer (DFU) is a common and global health problem in patients suffering from Diabetes. Delayed healing of these ulcers lead to serious problems, such as infection and amputation, apart from the problems faced in carrying out daily activities. This work has hypothesized the change in irregularity of wound contour may be linked to patient's clinical conditions. So far, fractal dimension is known to describe the change in irregularity of the fractal objects with change in scale but it fails to show any relation with the medical conditions in case of DFUs. This work introduces a new approach to quantify the irregularity of the wound contour and studies its association with some medical conditions. It was found that the change in irregularity of the wound contour from week 1 to week 4 had a close relation with ischemic heart disease as an important factor affecting the healing of DFUs

A novel color space of fundus images for automatic exudates detection

This paper has compared the performance of different color spaces of fundus images for automatic detection of exudates. A convolutional neural network was employed to assess the performances of different color spaces generated by orthogonal transformation of the original colors in red/green/blue (RGB) space. Experiments were conducted on two publicly available databases: (1) DIARETDB1 and (2) e-Ophtha. Based on the experimental results, this study has proposed a new color space of fundus images with three channels: (i) second eigenchannel of the RGB space, (ii) hue and (iii) saturation channels of Hue/Saturation and Intensity (HSI) space. This achieved an accuracy, sensitivity and specificity of 98.2%, 0.99 and 0.98, respectively. Twenty times 20-fold cross validation technique confirmed that proposed color space obtained higher replicability compared with conventional color spaces

Curve irregularity index for quantification of roughness in non-fractal curves

Fractal geometry is widely used to study the roughness of irregular curves and this has a number of biomedical and geological applications. However, many natural and biological objects are semi-fractal,multi-fractal or non-fractal objects, where the fractal dimension (FD)measure does not work. This paper proposes an irregularity index (Ic)to quantify curve irregularity by measuring the change in the roughness of the segments of the curve with change in window sizes. The proposed index has been validated using synthetically generated curves having different degrees of roughness and the results showed linear relationship of the index with the roughness level (R2=0.99). Statistical significance using ANOVA was tested to determine the difference in the value of the index for curves having different irregularity for both Ic and FD and it was found that only Ic values showed significant difference among the different groups of curves with varying irregularity (p-value<0.001)

Adaptive colour transformation of retinal images for stroke prediction

Identifying lesions in the retinal vasculature using Retinal imaging is most often done on the green channel. However, the effect of colour and single channel analysis on feature extraction has not yet been studied. In this paper an adaptive colour transformation has been investigated and validated on retinal images associated with 10-year stroke prediction, using principle component analysis (PCA). Histogram analysis indicated that while each colour channel image had a uni-modal distribution, the second component of the PCA had a bimodal distribution, and showed significantly improved separation between the retinal vasculature and the background. The experiments showed that using adaptive colour transformation, the sensitivity and specificity were both higher (AUC 0.73) compared with when single green channel was used (AUC 0.63) for the same database and image features

Parkinson's Disease Diagnosis Based on Multivariate Deep Features of Speech Signal

Parkinson's disease (PD) is known as neurodegenerative disorder causing speech impairment in patients. Therefore, voice recording has been used as useful tool for diagnosis of PD. For the first time in this study, we have tested the effectiveness of deep convolutional neural network (DCNN) in distinguishing between Parkinson's and healthy voices using spectral features from sustained phoneme /a/ (as pronounced in car). Various designs of the DCNN architecture were investigated on raw pathological and healthy voices of varying lengths. This study also investigated the effect of parameters such as frame size, number of convolutional layers and feature maps as well as topology of fully connected layers on the accuracy of the classification outcome. The best network achieved accuracy of 75.7% corresponding on 815 ms of data for distinguishing between Parkinson's and healthy samples. This work has demonstrated that online speech recording has the potential for being used to screening people for Parkinson's disease

Exudate detection in fundus images using deeply-learnable features

Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor performance. This study has investigated different deep learning techniques to maximize the sensitivity and specificity. We have compared multiple deep learning methods, and both supervised and unsupervised classifiers for improving the performance of automatic exudate detection, i.e., CNNs, pre-trained Residual Networks (ResNet-50) and Discriminative Restricted Boltzmann Machines. The experiments were conducted on two publicly available databases: (i) DIARETDB1 and (ii) e-Ophtha. The results show that ResNet-50 with Support Vector Machines outperformed other networks with an accuracy and sensitivity of 98% and 0.99, respectively. This shows that ResNet-50 can be used for the analysis of the fundus images to detect exudates