J. Jannet Vennila

Diagnosis of Arthritis Through Fuzzy Inference System

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh’s fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.

Synthesis and spectroscopic characterization of fluorescent 4-aminoantipyrine analogues: Molecular docking and in vitro cytotoxicity studies.

Two substituted aromatic carbonyl compounds (compounds 1 and 2) of 4-aminoantipyrine were synthesized by condensation of fluorine substituted benzoyl chlorides and 4-aminoantipyrine. The structures of synthesized derivatives were established on the basis of UV-Vis, IR, and Mass, (1)H, (13)C NMR and Fluorescence spectroscopy. Both compounds showed significant fluorescence emission and two broad emission bands were observed in the region at 340 nm and 450 nm on excitation at 280 nm. Theoretically to prove that the molecule has anticancer activity against cervical cancer cells, the compounds were analyzed for molecular docking interactions with HPV16-E7 target protein by Glide protocol. Furthermore, 4-aminoantipyrine derivatives were evaluated for their in vitro cytotoxic activity against human cervical cancer cells (SiHa) by MTT assay. Compound 1 showed two fold higher activity (IC50=0.912 μM) over compound 2, and its activity was similar to that of Pazopanib, suggesting that although the two compounds were chemically very similar the difference in substituent on the phenyl moiety caused changes in properties.

Computational Studies on the Resistance of Penicillin-Binding Protein 2B (PBP2B) of Wild-type and Mutant Strains of Streptococcus pneumoniae Against β-Lactam Antibiotics

Mutations within transpeptidase domain of penicillin‐binding protein 2B of the strains of Streptococcus pneumoniae leads to resistance against β‐lactam antibiotics. To uncover the important residues responsible for sensitivity and resistance, the recently determined three dimensional structures of penicillin‐binding protein 2B of both wild‐type R6 (sensitive) and mutant 5204 (resistant) strains along with the predicted structures of other mutant strains G54, Hungary19A‐6 and SP195 were considered for the interaction study with β‐lactam antibiotics using induced‐fit docking of Schrödinger. Associated binding energies of the complexes and their intermolecular interactions in the binding site clearly show that the wild‐type R6 as sensitive, mutant strains 5204 and G54 as highly resistant, and the mutant strains Hungary19A‐6 and SP195 as intermediate resistant. The study also reveals that the mutant strains Hungary19A‐6 and SP195 exhibit intermediate resistant because of the existence of mutations till the intermediate 538th and 516th positions, respectively, and not till the end of the C‐terminus. Furthermore, our investigations show that if the mutations are extended till the end of the C terminus, then the antibiotic resistance of induced‐mutated strains increases from intermediate to high as in the strains 5204 and G54. The binding patterns obtained in the study are useful in designing potential inhibitors against multidrug resistant S. pneumoniae.

Design, Synthesis, Spectral Analysis, In Vitro Anticancer Evaluation and Molecular Docking Studies of Some Fluorescent 4-Amino-2, 3-Dimethyl-1-Phenyl-3-Pyrazolin-5-One, Ampyrone Derivatives

The commenced work deals with the synthesis, characterization and evaluation of biological activities of 4-amino-2,3-dimethyl-1-phenyl-3-pyrazolin-5-one. The synthesis was done by the condensation of aromatic acid chlorides with 4-aminoantipyrine. The structures of synthesized derivatives were elucidated using IR, Mass, 1H NMR and 13C NMR spectroscopy, and their UV–Visible and fluorescence properties were studied. The compounds showed significant dual fluorescence. Molecular docking was used to understand the small molecule–receptor protein interaction. The derivatives were screened for their in vitro cytotoxic activity against the reference drug pazopanib on human cervical cancer cell line (SiHa) using MTT assay.

Rheumatoid Arthritis Candidate Genes Identification by Investigating Core and Periphery Interaction Structures

Rheumatoid arthritis (RA) is a long-term systemic inflammatory disease that primarily attacks synovial joints and ultimately leads to their destruction. The disease is characterized by series of processes such as inflammation in the joints, synovial hyperplasia, and cartilage destruction leading to bone erosion. Since RA being a chronic inflammatory complex disease, there is a constant need to develop novel and dynamic treatment to cure the disease. In the present research, network biology and gene expression profiling technology are integrated to predict novel key regulatory molecules, biological pathways, and functional network associated with RA. The microarray datasets of synovial fibroblast (SF) (GSE7669) and macrophages (GSE10500 and GSE8286), which are the primary cells in the synovium and reported as the key players in the pathophysiology of RA, were considered for identification of signature molecules related to RA. The statistical analysis was performed using false discovery rate (FDR), t-test, one-way anova, and Pearson correlation with favorable p-value. The K-core analysis depicted the change in network topology which consisted of up- and downregulated genes network, resulted in six novel meaningful networks with seed genes OAS2, VCAN, CPB1, ZNF516, ACP2, and OLFML2B. Hence, we propose that, differential gene expression network studies will be a standard step to elucidate novel expressed gene(s) globally.

Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug–Target Interaction

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug–target–protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target–protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin–angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn’t produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes.

Gene interaction map: a paradigm for identifying significant pathways responsible for rheumatoid arthritis

Abstract Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes inflammation of the joints and may cause inflammation of other tissues in the body. It is mainly caused by combination of factors including abnormal autoimmune response, genetic susceptibility and some environmental or biological triggers. A number of pathways have been shown to be affected wherein numerous molecules are known to be involved. Uncovering the molecular pathways in this disease becomes more difficult as it is complicated with genetic, environmental and more over inflammatory and autoimmune parameters. Hence, network biology approach is used to identify the important pathways and molecules which play significant role in RA through Gene Interaction Map (GIP) of 1,200 nodes and 5,286 edges. Our studies elucidate the relationship between topological properties of GIP and the role played by molecules in cellular system, which helps in defining the organizational mechanism used in cellular system. K-core decomposition method identified novel genes and revealed the correlation between toll-like receptor, MAPK signaling, apoptosis, t cell receptor signaling and epithelial cell signaling pathways.