Background and Objective: Sphingosine 1-phosphate (S1P), and S1P receptor modulator fingolimod have been suggested to play important cardioprotective role in animal models of myocardial ischemia/reperfusion injuries. To understand the cardioprotective function of S1P and its mechanism in vivo, we analyzed apoptotic, inflammatory biomarkers, and myocardial fibrosis in an in vivo heterotopic rat heart transplantation model.Methods: Heterotopic heart transplantation is performed in 60 Sprague–Dawley (SD) rats (350–400 g). The heart transplant recipients (n = 60) are categorized into Group A (control) and Group B (fingolimod treated 1 mg/kg intravenous). At baseline with 24 h after heart transplantation, blood and myocardial tissue are collected for analysis of myocardial biomarkers, apoptosis, inflammatory markers, oxidative stress, and phosphorylation of Akt/Erk/STAT-3 signaling pathways. Myocardial fibrosis was investigated using Masson’s trichrome staining and L-hydroxyline.Results: Fingolimod treatment activates both Reperfusion Injury Salvage Kinase (RISK) and Survivor Activating Factor Enhancement (SAFE) pathways as evident from activation of anti-apoptotic and anti-inflammatory pathways. Fingolimod treatment caused a reduction in myocardial oxidative stress and hence cardiomyocyte apoptosis resulting in a decrease in myocardial reperfusion injury. Moreover, a significant (p < 0.001) reduction in collagen staining and hydroxyproline content was observed in fingolimod treated animals 30 days after transplantation demonstrating a reduction in cardiac fibrosis.Conclusion: S1P receptor activation with fingolimod activates anti-apoptotic and anti-inflammatory pathways, leading to improved myocardial salvage causing a reduction in cardiac fibrosis.

Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

BackgroundIn digital mammography, finding accurate breast profile segmentation of women’s mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing.MethodsWe propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images.ResultsTo assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth.ConclusionsOur approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.

A series of previously conducted experiments pertaining to various chemicals and drugs uncover a natural linkage between the molecular structures and the bio-medical and pharmacological characteristics. The forgotten topological index computed for the molecular structures of various chemical compounds and drugs has proven significant in medical and pharmaceutical fields by predicting biological features of new chemical compounds and drugs. A topological index can be considered as the transformation of chemical structure into a real number. Dendrimers are highly-branched star-shaped macromolecules with nanometer-scale dimensions. Dendrimers are defined by three components: a central core, an interior dendritic structure (the branches), and an exterior surface with functional surface groups. In this paper, we determine forgotten topological indices of poly(propyl) ether imine, porphyrin, and zinc–porphyrin dendrimers.

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