Acute intermittent porphyria with posterior reversible encephalopathy syndrome in pregnancy: a case report

Background: The porphyrias are a clique of metabolic disorders caused by a faulty heme-synthesis process. The acute intermittent porphyria (AIP) is the most common type of porphyria. It results from a decrease in the porphobilinogen deaminase enzyme levels (PBG-D) which plays a vital role in the hepatic phase of heme synthesis. Case Report: A 26-years-old lady without any co-morbidity or prior history of any disease came with acute abdominal pain. The causes of acute abdominal pain such as the ectopic pregnancy and other causes were excluded after investigations. She was eventually diagnosed with AIP and managed accordingly. The findings of MRI Brain were suggestive of posterior reversible encephalopathy syndrome (PRES). The patient was treated with hematin, glucose and symptomatic treatment were also given. However, the patient had persistent autonomic dysfunction and hyponatremia, followed by cardiac arrest and death. Conclusion: The diagnosis of AIP is often missed in cases of an acute abdominal pain in pregnant women. Early diagnosis of AIP during pregnancy followed by proper management is associated with favourable maternal and foetal outcome. Delay in the management leads to the worse outcomes like maternal morbidity, mortality or foetal loss

Evaluation of Cardioprotective Effect of 3,5,3′-Tri-iodo-L-thyronine in Isoproterenol-Induced Cardiotoxicity

T3 (3,5,3′-triiodothyronine) has drawn relatively little attention in relation to cardiovascular (CVS) diseases. The present study was designed to evaluate the cardioprotective action of T3 in isoproterenol-(ISO-) induced cardiac toxicity. Female Wistar rats were exposed with ISO (100 mg/kg, body weight, subcutaneously) for 2 days at the interval of 24 h followed by T3 (3 μg/kg, body weight, orally) treatment for 3 days. Positive control rats received only ISO (100 mg/kg, body weight, subcutaneously) for 2 days at the interval of 24 hrs. Control group animals received normal saline as a vehicle. As expected, ISO-induced significant changes were observed in low-density lipoprotein, total cholesterol, ALT, CK-MB to TCK ratio, and prolongation of QT interval in electrocardiogram, which is toward normalization after T3 treatment. Lower heart weight, upregulation of cardiac myosin heavy chain alpha (MHC-α), and reduced inflammatory cell infiltration, myonecrosis, vacuolar changes, and a trend toward normal cardiac muscle fiber architecture in microscopic examination of cardiac tissue further support the cardioprotective effect of T3

Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes