Imaging mass spectrometry analysis reveals an altered lipid distribution pattern in the tubular areas of hyper-IgA murine kidneys.

Immunoglobulin A (IgA) nephropathy is the most common glomerular disease worldwide. To investigate the pathogenesis of this renal disease, we used animal models that spontaneously develop mesangioproliferative lesions with IgA deposition, which closely resemble the disease in humans. We analyzed the molecular distribution of lipids in hyper-IgA (HIGA) murine kidneys using matrix-assisted laser desorption/ionization-quadrupole ion trap-time of flight (MALDI-QIT-TOF)-based imaging mass spectrometry (IMS), which supplies both spatial distribution of the detected molecules and allows identification of their structures by their molecular mass signature. For both HIGA and control (Balb/c) mice, we found two phosphatidylcholines, PC(16:0/22:6) and PC(18:2/22:6), primarily located in the cortex area and two triacylglycerols, TAG(16:0/18:2/18:1) and TAG(18:1/18:2/18:1), primarily located in the hilum area. However, several other molecules were specifically seen in the HIGA kidneys, particularly in the tubular areas. Two HIGA-specific molecules were O-phosphatidylcholines, PC(O-16:0/22:6) and PC(O-18:1/22:6). Interestingly, common phosphatidylcholines and these HIGA-specific ones possess 22:6 lipid side chains, suggesting that these molecules have a novel, unidentified renal function. Although the primary structure of the HIGA-specific molecules corresponding to m/z 854.6, 856.6, 880.6, and 882.6 remained undetermined, they shared similar fragmentation patterns, indicating their relatedness. We also showed that all the HIGA-specific molecules were derived from urine, and that artificial urinary stagnation-due to unilateral urethral obstruction-caused HIGA-specific distribution of lipids in the tubular area

Measurement of enzymatic treatment effect on textile using digital image analysis

The effect of enzymatic treatment on textiles has been investigated using standard texture algorithms. An extensive study in both the Fourier domain and the spatial domain has revealed the nature of the changes and resulted in one single feature that measures these changes in a fast and robust way

Automated detection of diabetic retinopathy in a fundus photographic screening population

PURPOSE. To evaluate the performance of an automated fundus photographic image-analysis algorithm in high-sensitivity and/or high-specificity segregation of patients with diabetes with untreated diabetic retinopathy from those without retinopathy. METHODS. This was a retrospective cross-sectional study of 260 consecutive nonphotocoagulated eyes in 137 diabetic patients attending routine photographic retinopathy screening. Mydriatic 60°fundus photography on 35-mm color transparency film was used, with a single fovea-centered field. Routine grading was based on visual examination of slide-mounted transparencies. Reference grading was performed with specific emphasis on achieving high sensitivity. Computer-assisted automated red lesion detection was performed on digitized transparencies. RESULTS. When applied in a screening population comprising patients with diabetes with untreated diabetic retinopathy in any eye and patients with diabetes without retinopathy, the automated lesion detection correctly identified 90.1% of patients with retinopathy and 81.3% of patients without retinopathy. A per-eye analysis for methodological purposes demonstrated that the automated lesion detection could be adapted to simulate various visual evaluation strategies. When adapted at high sensitivity, the automated system demonstrated sensitivity at 93.1% and specificity at 71.6%. When adapted at high specificity the automated system demonstrated sensitivity at 76.4% and specificity at 96.6%, closely matching routine visual grading at sensitivity 76.4% and specificity 98.3%. CONCLUSIONS. Automated detection of untreated diabetic retinopathy in fundus photographs from a screening population of patients with diabetes can be made with adjustable priority settings, emphasizing high-sensitivity identification of diabetic retinopathy or high-specificity identification of absence of retinopathy, covering opposing extremes of visual evaluation strategies demonstrated by human observers. (Invest Ophthalmol Vis Sci. 2003;44:767-771