Renal ischemia in rats: Mitochondria function and laser autofluorescence

Ischemia-reperfusion injury is the major cause of organ dysfunction or even nonfunction following transplantation. It can attenuate the long-term survival of transplanted organs. To evaluate the severity of renal ischemia injury determined by histology, we applied laser(442 nm and 532 nm) induced fluorescence (LIF), mitochondria respiration, and membrane swelling to evaluate 28 Wistar rats that underwent left kidney warm ischemia for 20, 40, 60, or 80 minutes. LIF performed before ischemia (control) was repeated at 20, 40, 60, and 80 minutes thereafter. We harvested left kidney tissue samples immediately after LIF determination for histology and mitochondrial analyses: state 3 and 4 respiration, respiration control rate (RCR), and membrane swelling. The association of optic spectroscopy with histological damage showed: LIF, 442 nm (r(2) = 0.39, P < .001) and 532 nm, (r(2) = 0.18, P = .003); reflecting laser/fluorescence-induced, 442 nm (r(2) = 0.20, P = .002) and 532 nm (r(2) = 0.004, P = .67). The associations between mitochondria function and tissue damage were: state 3 respiration (r(2) = 0.43, P = .0004), state 4 respiration (r(2) = 0.03, P = 0.38), RCR (r(2) = 0.28, P = .007), and membrane swelling (r(2) = 0.02, P = .43). The intensity of fluorescence emitted by tissue excited by laser, especially at a wave length of 442 nm, was determined in real time. Mitochondrial state 3 respiration and respiratory control ratio also exhibited good correlations with the grade of ischemic tissue damage.

Identification of protein-coding and intronic noncoding RNAs down-regulated in clear cell renal carcinoma

The clear cell subtype of renal cell carcinoma (RCC) is the most lethal and prevalent cancer of the urinary system. To investigate the molecular changes associated with malignant transformation in clear cell RCC, the gene expression profiles of matched samples of tumor and adjacent non-neoplastic tissue were obtained from six patients. A custom-built cDNA microarray platform was used, comprising 2292 probes that map to exons of genes and 822 probes for noncoding RNAs mapping to intronic regions. Intronic transcription was detected in all normal and neoplastic renal tissues. A subset of 55 transcripts was significantly down-regulated in clear cell RCC relative to the matched nontumor tissue as determined by a combination of two statistical tests and leave-one-out patient cross-validation. Among the down-regulated transcripts, 49 mapped to untranslated or coding exons and 6 were intronic relative to known exons of protein-coding genes. Lower levels of expression of SIN3B, TRIP3, SYNJ2BP and NDE1 (P<0.02), and of intronic transcripts derived from SND1 and ACTN4 loci (P<0.05), were confirmed in clear cell RCC by Real-time RT-PCR. A subset of 25 transcripts was deregulated in additional six nonclear cell RCC samples, pointing to common transcriptional alterations in RCC irrespective of the histological subtype or differentiation state of the tumor. Our results indicate a novel set of tumor suppressor gene candidates, including noncoding intronic RNAs, which may play a significant role in malignant transformations of normal renal cells. (C) 2008 Wiley-Liss, Inc.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.