Endodontic Flare-Ups: An Update.

Root canal treatment deals with mechanical and chemical cleaning followed by obturation that promotes healing and repair of periradicular tissues. Flare-ups can occur in between or some days after endodontic therapy leading to unscheduled visit by the patient. This complication is characterized by severe pain and/ or swelling. There is a correlation between number of appointments, intracanal medicament used and flare-ups. However, there is no sure procedure that can avoid this complication. Therefore, this review article has discussed about causes and some procedures to prevent and treat flare-ups.

Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy.

Advancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from "one-target, one-drug" to "target-network, multi-component therapeutics". We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein-protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.

Network spectra for drug-target identification in complex diseases: new guns against old foes.

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Prognostic interaction patterns in diabetes mellitus II: A random-matrix-theory relation.

We analyze protein-protein interactions in diabetes mellitus II and its normal counterpart under the combined framework of random matrix theory and network biology. This disease is the fifth-leading cause of death in high-income countries and an epidemic in developing countries, affecting around 8% of the total adult population in the world. Treatment at the advanced stage is difficult and challenging, making early detection a high priority in the cure of the disease. Our investigation reveals specific structural patterns important for the occurrence of the disease. In addition to the structural parameters, the spectral properties reveal the top contributing nodes from localized eigenvectors, which turn out to be significant for the occurrence of the disease. Our analysis is time-efficient and cost-effective, bringing a new horizon in the field of medicine by highlighting major pathways involved in the disease. The analysis provides a direction for the development of novel drugs and therapies in curing the disease by targeting specific interaction patterns instead of a single protein.

Network Topologies Decoding Cervical Cancer.

According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

Randomness and preserved patterns in cancer network.

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.