Multi-omics integration reveals molecular networks and regulators of psoriasis.

BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning.  Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing.  Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging.  When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care.  It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work.

Salen and Related Ligands

The salen and related ligands are very popular among the inorganic chemists due to multiple reasons such as ease in synthesis, coordinating ability with very long range of metal ions, facilitating the metal ions to adopt various geometries, ability of stabilising the metal ion in variable oxidation states and potential applications of metallosalen in several fields. The most common application of metallosalen is in the field of catalysis because of their recoverability, reusability, high efficiency, high selectivity and their capability of working as homogeneous as well as heterogeneous catalysts for numerous functional group manipulations including asymmetric synthesis. Molecular magnetism, sensory applications, bioinorganic activities and medicinal applications of metallosalen are also very promising areas of their applications. Porous materials involving metal organic frameworks (MOFs) and supramolecular building blocks are increasingly getting attention of researchers for the gas absorption and heterogeneous catalysis

The study of clinical and endoscopic spectrum of upper gastrointestinal manifestations in HIV patients

Background: Opportunistic disorders are the most frequent GI complications of HIV infection and remain a major cause of morbidity and mortality in these patients. These disorders account for high prevalence of upper gastrointestinal symptoms such as dysphagia, odynophagia, retrosternal chest pain, abdominal pain and upper GI bleeding. Hence an attempt is being made to study clinical, endoscopic and biopsy changes in HIV patients with upper GI symptoms which helps us to make early diagnosis of upper GI disorders in HIV patients.Methods: HIV positive patients above 14 yrs diagnosed on the basis of recent NACO criteria having Upper G.I. symptoms, attending OPD of Department of Medicine admitted in Wards. All fifty three patients with upper G.I. symptoms were subjected to detail history, thorough clinical examination, routine and special investigations and Upper G.I Endoscopy.Results: Out of fifty three patients, nineteen (35.8%) cases had normal endoscopy. The most common finding was Antral Gastritis in fourteen (26.4%), followed by Candida esophagitis in twelve (22.6%), esophagitis in three (5.7%), candida esophagitis with antral gastritis in two (3.8%), duodenitis, varices and mass (ulcerated growth) in II part of Duodenum seen in one (1.9%) each.Conclusions: The evaluation of specific gastrointestinal complaints must be based on an assessment of degree of immunosuppression. With the progression of immunodeficiency, EGD becomes a useful diagnostic modality for the early diagnosis of these opportunistic infections and other inflammatory conditions

Study of anaemia in type 2 diabetes mellitus

Background: Anaemia is increasingly recognized entity in the patients with diabetes mellitus and constitutes an additional burden in patients. The prevalence of anaemia in the patients with diabetes is two or three times higher than in patients with comparable renal impairment and iron stores in the general population. As India is foreseen a diabetic capital of the world, it becomes imperative to recognize co-morbidities such as anaemia at the earliest. Hence this study is being conducted with the aim to determine the prevalence and various causes of anaemia in diabetics.Methods: After obtaining informed written consent, all diabetics patients were subjected to detailed history, through clinical examination and investigation with CBC, Renal function test including creatinine clearance. The difference of mean between anaemic and non anaemic diabetic patients was evaluated by unpaired student t test. Finally, correlation between the level of haemoglobin and index of renal damage (albumin-creatinine ratio) was accessed by Pearson correlation. Statistical software of SPSS 10 ver. and EXCEL (office 9) was used to analyse the data.Results: In the present study, nearly two third patients of type 2 diabetes mellitus were anaemic. The maximum number of anaemic patients with type 2 diabetes mellitus had microcytic hypochromic type of anaemia.Conclusions: It is therefore concluded that anaemia is a prevalent finding in patients with type 2 diabetes mellitus and represents significant unrecognised burden. The anaemia may be attributed to variable contribution of iron deficiency state and chronic inflammation as result of the disease itself

Review and Analysis of Failure Detection and Prevention Techniques in IT Infrastructure Monitoring

Maintaining the health of IT infrastructure components for improved reliability and availability is a research and innovation topic for many years. Identification and handling of failures are crucial and challenging due to the complexity of IT infrastructure. System logs are the primary source of information to diagnose and fix failures. In this work, we address three essential research dimensions about failures, such as the need for failure handling in IT infrastructure, understanding the contribution of system-generated log in failure detection and reactive & proactive approaches used to deal with failure situations. This study performs a comprehensive analysis of existing literature by considering three prominent aspects as log preprocessing, anomaly & failure detection, and failure prevention. With this coherent review, we (1) presume the need for IT infrastructure monitoring to avoid downtime, (2) examine the three types of approaches for anomaly and failure detection such as a rule-based, correlation method and classification, and (3) fabricate the recommendations for researchers on further research guidelines. As far as the authors\u27 knowledge, this is the first comprehensive literature review on IT infrastructure monitoring techniques. The review has been conducted with the help of meta-analysis and comparative study of machine learning and deep learning techniques. This work aims to outline significant research gaps in the area of IT infrastructure failure detection. This work will help future researchers understand the advantages and limitations of current methods and select an adequate approach to their problem

Chromium Uptake Efficiency of Spinacea olaracea from Contaminated Soil

The aim of the study was to evaluate the uptake of chromium by Spinacea olaracea and its accumulation in roots and shoots of plants grown in pots at various concentrations of chromium (30, 60, 90,120,150 mg/l). The results revealed that the levels of chromium accumulation in roots and shoots were higher at minimum concentration level @ JASE