No abstract

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

No abstract

Fibromatosis colli is a peculiar, benign fibrous growth of the sternocleidomastoid that usually appears during the first few weeks of life and is often associated with muscular torticollis. Fibromatosis colli (FC) is seen in children born after difficult, prolonged labor, assisted delivery, and breech deliveries. Clinically, FC has to be differentiated from congenital lesions, inflammatory lesions, and neoplastic conditions—both benign and malignant—that may occur at that site. Fine-needle aspiration cytology (FNAC) is a simple technique that will help in excluding the above conditions and also in avoiding surgical procedures. Fibromatosis colli also resembles other forms of infantile fibromatosis, but its behavior, microscopic appearance, and its treatment distinguish it from other forms of infantile fibromatosis. In contrast to other forms of fibromatosis, a noninvasive, conservative management is usually the line of treatment for FC in most of the cases. FNAC is a noninvasive method of diagnosis of FC that is thus useful in its management. We report here a case of Fibromatosis colli diagnosed by FNAC.

This work elucidates the idea of finding probable critical genes linked to breast adenocarcinoma. In this study, the GEO database gene expression profile data set (GSE70951) was retrieved to look for genes that were expressed variably across breast adenocarcinoma samples and healthy tissue samples. The genes were confirmed to be part of the PPI network for breast cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. For correlation analysis, the predictive biomarker of these hub genes, as well as GEPIA, was used. A total of 155 (85 upregulated genes and 70 downregulated genes) were identified. By integrating the PPI and CytoHubba data, the major key/hub genes were selected from the results. The KM plotter is employed to find the prognosis of those major pivot genes, and the outcome shows worse prognosis in breast adenocarcinoma patients. Further experimental validation will show the predicted expression levels of those hub genes. The overall result of our study gives the consequences for the identification of a critical gene to ease the molecular targeting therapy for breast adenocarcinoma. It could be used as a prognostic biomarker and could lead to therapy options for breast adenocarcinoma.

Metanephric stromal tumor of kidney is a novel pediatric benign stromal specific renal neoplasm. A few cases have been reported in adults also. This tumor is usually centered in the renal medulla with a characteristic microscopic appearance which differentiates this lesion from congenital mesoblastic nephroma and clear cell sarcoma of the kidney. In most cases complete excision alone is curative. The differentiation of metanephric stromal tumor from clear cell sarcoma of the kidney will spare the child from the ill effects of adjuvant chemotherapy. In this communication we describe the gross and microscopic features of metanephric stromal tumor in a one-month-old child with good prognosis.

A six-year-old male child presented with abdominal pain and fever of four days duration, which was consistent with a clinical diagnosis of a liver abscess. Ultrasonogram of the (Usg) abdomen revealed a large, well-defined, 9 x 6 x 5 cms sized, echogenic mass in the right lobe of liver and computed tomography (CT ) of the abdomen revealed a large, well-defined, isodense mass in the right lobe of liver, with increased vascularity within the tumour, pushing the right kidney downwards. Haematological investigations, liver function tests and serum alpha foeto protein levels were all with in normal limits. Laparotomy and segmental liver resection were done and the specimen was sent for a histopathological examination. Case 2A 2-year-old female child presented with fever and incessant crying of two days duration, with a firm mass in the right hypochondrium, extending 3 cms below the costal margin. USG of the abdomen revealed a lobulated hyperechoic mass which measured 5.4x 5.5 x 5.6 cms, which had increased intra-tumoural vascularity. CT of the abdomen showed a non enhancing lesion and a radiological diagnosis of a mesenchymal hamartoma was made [Table/ Fig-1]. Laparotomy and segmental liver resection were done and the specimen was sent for a histopathological examination.

Objective: To evaluate the common causes of preanalytical errors in a fully automated hematology laboratory. Methods: Laboratory staff was instructed to record the rejected samples and the causes of such rejections of ward and outpatient samples collected in both wards and laboratory. Results: Of the 53344 samples received for hematological tests during the one year period from 1.1.2016 to 31.12.2016, 181 samples were rejected for analysis. This accounted for 0.3% of samples collected for hematological tests. The reasons for rejections with their incidences are as follows: Insufficient samples-35.3 %, Clotted sample-25.7 %, Wrong registration-15.0 %, Double registration-11.6 %, Inappropriate container-5.5 %, Sample spillage-3.9 %. Conclusion: The overall percentage of rejection in our hematology laboratory is 0.3 % and insufficient sample is the most common cause for rejection. Adequate training, regular maintenance of a record of errors and periodic auditing will result in effective reduction of such errors and hence improvement in the overall performance of laboratory works.