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doi:10.4172/jpb.100013

Skin provides passage for the delivery of drugs. The in vitro and in vivo testing of chemicals for estimation of dermal absorption is very time consuming, costly and has many ethical difficulties related to human and animal testing. The solution to the problem is Quantitative structure-permeability relationships. This method relates dermal penetration properties of a range of chemical compounds to their physicochemical parameters. In the present study, an effort has been made to develop models for the accurate prediction of skin permeability using a large, diverse dataset through the combination of various regression methods coupled with the Genetic Algorithm (GA)/Interval Partial Least-Squares Algorithm (iPLS). The descriptors were calculated using e-DRAGON and ADME Pharma Algorithms-Abrahams descriptors. The original dataset was divided into a training set and a testing set using the Kennard-Stone Algorithm. The selection of descriptors was made by the GA and iPLS. The model applicability domain was determined. The results showed that a three-parameter model built through Partial Least-squares Regression was most accurate with r(2) of 0.936.

The study of temperature regulation of human body will help to better understand the physiology and functioning of every biological system. Skin is the largest organ of the integumentory system playing an important role to maintain the body core temperature (Tb) at 37°C. Any disturbance in the temperature regulation may cause lots of abnormality in the body. The purpose of this paper is to present an overview of temperature variations of tissues of human peripheral region during wound healing process after plastic surgery. An attempt has been made to study temperature variations of normal region (region before surgery) as well as abnormal region (region after surgery) of human peripheral region after the plastic surgery at different atmospheric temperatures and rates of evaporation for an undressed wound by extending finite domain to infinite using infinite element method (IEM). The two-dimensional peripheral region (skin and subcutaneous tissues) consists of finite triangular elements of very small size and the infinitely long rectangular elements. The appropriate shape functions are used for the elements. Physiological parameters like thermal conductivity, rate of metabolism, blood mass flow rate, latent heat, rate of evaporation etc. are used along with the proper initial and boundary conditions. The temperature variations are noted for tissue of donor site (normal region) as well as tissues after surgery (abnormal region). The information obtained from this model can be of great use for biomedical scientists for application in treatment of various diseases as well as helpful to develop protocols for medical purpose.

We proposed c-jun to be the most potent target for drug designing against wrinkle formation.

Abstract:In this study, a two dimensional infinite element model has been developed to study thermal effect in human dermal regions due to tumors. This model incorporates the effect of blood mass flow rate, metabolic heat generation and thermal conductivity of the tissues.The dermal region is divided into three natural layers, namely, epidermis, dermis and subdermal tissues. A uniformly perfused tumor is assumed to be present in the dermis. The domain is assumed to be finite along the depth and infinite along the breadth. The whole dermis region involving tumor is modelled with the help of triangular finite elements to incorporate the geometry of the region. These elements are surrounded by infinite domain elements along the breadth. Appropriate boundary conditions has been incorporated. A computer program has been developed to obtain the numerical results.

Background: Major depressive disorder (MDD) is a common psychiatric disorder characterized by constant sadness and a lack of interest in work and social interactions. Maintaining the transcriptome levels via the controlled regulation of mRNA processing and transport is essential to alleviating MDD. Various molecular phenotypes such as aberrant RNA splicing and stability are identified as critical determinants of MDD. Aim: This study aims to compare the mRNA expression profiles between major depressive disorder non-suicide (MDD), major depressive disorder suicide (MDD-S), and control groups using RNA-Seq. Materials and Methods: A transcriptomics and sequencing analysis of gene expression profiling was conducted in 9 patients with MDD, 10 patients with MDD-S, and 10 control patients. Results: A comparison of the sample groups revealed that the PRKACB gene was upregulated in patients with MDD. At the same time, GRM3, DLGAP1, and GRIA2 were downregulated in these patients—these genes are majorly involved in the glutamatergic pathway. Five genes (GRIA1, CAMK2D, PPP3CA, MAPK10, and PPP2R2A) of the dopaminergic pathway were downregulated in patients with the MDD-S condition when compared with the MDD and control groups. Cholinergic synapses were altered in patients with MDD when compared to the control group due to the presence of dysregulated genes (KCNQ5, PLCB4, ADCY9, CAMK2D, PIK3CA, and GNG2). Conclusion: The results provide a new understanding of the etiology of depression in humans and identify probable depression-associated biomarkers.

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