Inhibition of TNF-α and IL-1 by compounds from selected plants for rheumatoid arthritis therapy: In vivo and in silico studies

Purpose: To investigate the inhibitory activities of herbal compounds from Curcuma longa, Sophora japonica and Camellia sinensis against tumor necrosis factor alpha (TNF-α) and interleukin-1 (IL-1) using in vivo and in silico tools.Methods: The extracts of the medicinal herbs (Curcuma longa, Sophora japonica and Camellia sinensis) were evaluated for immune-modulatory activities based using neutrophil oxidative burst assay. The compounds present in the medicinal herbs were screened for their inhibitory effects against TNF-α (PDB ID: 2AZ5) and IL-1 (PDB ID: 2L5X) using Molegro Virtual Docker 6.0 (MVD). The stabilities of the top docking poses were confirmed by Molecular Dynamics (MD) simulation run for 20 nanoseconds (ns).Results: The herbal compounds exerted strong inhibitory effects against TNF-α (PDB ID: 2AZ5) and IL- 1 (PDB ID: 2L5X), implying their therapeutic potential for use in rheumatoid arthritis (RA). Of the compounds, curcumin diglucoside and curcumin monoglucoside showed the strongest inhibitory effects on monocytes, with inhibitory levels of 82.75 and 81.34 %, respectively, while eugenin had the weakest inhibitory activity (11.12 %). In addition, molecular docking scores were consistent with the in vivo results, and revealed strong inhibitory effects of curcumin diglucoside and curcumin monoglucoside against TNF-α and IL-1.Conclusion: Herbal compounds present in Curcuma longa, Sophora japonica and Camellia sinensis possess strong inhibitory effects against the pro-inflammatory cytokines TNF-α and IL-1. Thus, these compounds have therapeutic potentials that can be exploited for the treatment of RA.Keywords: Curcuma longa, Sophora japonica, Camellia sinensis, Rheumatoid arthritis, Cytokines, TNF-α, IL-1, Immuno-modulation, Molecular dockin

Optimal pricing strategy for green products under salience theory

Environmental pressures and people’s demands for green consumption have prompted manufacturers to engage in the research and development of green products. Manufacturers need to consider the price and greenness of products when making production decisions. This paper analyzes the level of greenness and price competition of duopoly manufacturers in the consumer market in which both green-sensitive consumers (salience to greenness) and price-sensitive consumers (salience to price) exist simultaneously according to salience theory. We find that the regular manufacturer will enter the green market when all consumers’ average degree of price responsiveness is small or in a moderate part of the region. In addition, this paper also discusses the influence of salience on manufacturers’ level of greenness and pricing strategy choice. We find that the degree of salient thinking of consumers influences optimal pricing, optimal greenness and profits under the uniform pricing and price discrimination mechanisms

A Comprehensive Indoor Environment Dataset from Single-family Houses in the US

The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes

Machine learning approach in the development of building occupant personas

The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.Comment: 12 pages, 4 figure