During hepatic resection, the risk of severe intra-operative bleeding is a major risk.1) To prevent massive blood loss, continuous or intermittent vascular clamping of the hepatic artery and portal vein ligation is known as "Pringle manoeuvere" is an efficient method to reduce hemorrhage. 2,3) "Pringle manoeuvere" is a technique which leads to ischemic and reperfusion injury. Ischemia and reperfusion results in complex metabolic, 4) immunological 5) and micro vascular 6) changes, which together may contribute to hepatocellular damage and dysfunction. 7) Nitric oxide (NO) is an important modulator of tissue blood flow, arterial pressure, neurotransmission, and immune cell function.8) There is evidence that implicates NO as a modulator of the adhesive interactions among leukocytes, platelets, and endothelial cells 9) and Larginine at a dose level 100 mg/kg/p.o. daily showed significantly protect from alcoholic injury.10) It has been shown that NO-donating compounds provide significant protection against the micro vascular dysfunction that is normally associated with ischemia and reperfusion (I/R).10) L-Arginine is the precursor of NO in vivo and confirmed sustained NO production via L-arginine administration ameliorated effects of intestinal I/R injury.11) However the effect of L-arginine therapy in the view of NO mediated vasodilatation on hepatic ischemia has not yet been completely elucidated. Therefore, this study was designed to investigate how an altered bioavailability of hepatocytes NO produces vasodilatation in hepatic artery supplementation by L-arginine affects the sinusoidal reperfusion and apoptosis under defined surgical techniques i.e. portal triad clamping (Pringle manoeuvere).
MATERIALS AND METHODS
ChemicalsCollagenase was purchased from Hymedia (Mumbai, India), peroxides-conjugated goat anti-rabbit secondary antibody was purchased from Bio source international (NY, U.S.A.). Alanine transaminase (ALT), aspartate transaminase (AST) kits were procured from Merck India Ltd. (India) and solvents of analytical grade were purchased from Merck (India).Animals Treatment Eighteen Wistar rats were divided into sham-operated control group (I) (nϭ6), ischemia and reperfusion group (II) were given 0.9% saline (5 ml/kg, p.o.) for 7 d (nϭ6), L-arginine pretreated group (100 mg/kg body weight/daily by oral route for 7 d before induced ischemia reperfusion maneuver) (III) (nϭ6). All rats were treated in accordance with the guideline for the Care and Use of Laboratory Animals (NIH Publication No. 86-23, revised 1985) with the permission of institute ethical committee. The hepatic I/R protocol were performed as described earlier.
12)Procurement of Tissue and Blood A portion of the ischemic and non-ischemic liver lobe was fixed in buffered 10% formalin and Karnovsky's solution for histopathology and transmission electron microscopy (TEM) studies. Blood samples were obtained from the right ventricle via a left anterior thoracotomy at the time of sacrifice. The blood from different groups rats were collected in steri...
In the era of healthcare and its related research fields, the dimensionality problem of high-dimensional data is a massive challenge as it is crucial to identify significant genes while conducting research on diseases like cancer. As a result, studying new Machine Learning (ML) techniques for raw gene expression biomedical data is an important field of research. Disease detection, sample classification, and early disease prediction are all important analyses of high-dimensional biomedical data in the field of bioinformatics. Recently, machine-learning techniques have dramatically improved the analysis of high-dimension biomedical data sets. Nonetheless, researchers’ studies on biomedical data faced the challenge of vast dimensions, i.e., the vast features (genes) with a very low sample space. In this paper, two-dimensionality reduction methods, feature selection, and feature extraction are introduced with a systematic comparison of several dimension reduction techniques for the analysis of high-dimensional gene expression biomedical data. We presented a systematic review of some of the most popular nature-inspired algorithms and analyzed them. The paper is mainly focused on the original principles behind each of the algorithms and their applications for cancer classification and prediction from gene expression data. Lastly, the advantages and disadvantages of nature-inspired algorithms for biomedical data are evaluated. This review paper may guide researchers to choose the most effective algorithm for cancer classification and prediction for the satisfactory analysis of high-dimensional biomedical data.
HPMC (K4M, K15M, blend of K4M and K15M) or their mixture with low/medium molecular mass chitosan may constitute excellent carrier systems for the stomach-specific sustained delivery of MX over a longer period.
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