Multivalent Role of Human TFIID in Recruiting Elongation Components at the Promoter-Proximal Region for Transcriptional Control

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Finite Element Method (FEM) Modeling of Freeze-drying: Monitoring Pharmaceutical Product Robustness During Lyophilization

Lyophilization is an approach commonly undertaken to formulate drugs that are unstable to be commercialized as ready to use (RTU) solutions. One of the important aspects of commercializing a lyophilized product is to transfer the process parameters that are developed in lab scale lyophilizer to commercial scale without a loss in product quality. This process is often accomplished by costly engineering runs or through an iterative process at the commercial scale. Here, we are highlighting a combination of computational and experimental approach to predict commercial process parameters for the primary drying phase of lyophilization. Heat and mass transfer coefficients are determined experimentally either by manometric temperature measurement (MTM) or sublimation tests and used as inputs for the finite element model (FEM)-based software called PASSAGE, which computes various primary drying parameters such as primary drying time and product temperature. The heat and mass transfer coefficients will vary at different lyophilization scales; hence, we present an approach to use appropriate factors while scaling-up from lab scale to commercial scale. As a result, one can predict commercial scale primary drying time based on these parameters. Additionally, the model-based approach presented in this study provides a process to monitor pharmaceutical product robustness and accidental process deviations during Lyophilization to support commercial supply chain continuity. The approach presented here provides a robust lyophilization scale-up strategy; and because of the simple and minimalistic approach, it will also be less capital intensive path with minimal use of expensive drug substance/active material.     

Induction of Apoptosis and Reduction of Endogenous Glutathione Level by the Ethyl-Acetate Soluble Fraction of the Methanol Extract of the Roots of Potentilla fulgens in Cancer Cells

Potentilla fulgens root traditionally used as a folk remedy in Meghalaya, India. However, systematic evaluation of its anticancer efficacy was limited. We investigated the anticancer potentials of the various extracts prepared by partitioning of the methanol extract of the root with the aim to discover major contributing factors from the most effective fractions. Methanol extract of P. fulgens roots (PRE) was prepared by maceration which was subsequently fractionated into hexane, ethyl-acetate (EA) and n-butanol soluble fractions. Various assays (clonogenic assay, Flow cytometry analysis, western blot, semiquantitative RT-PCR and the level of endogenous glutathione) were used to evaluate different parameters, such as Cell survivability, PARP-1 proteolysis, expression pattern of anti-apoptotic and γ-glutamyl-cysteine synthetase heavy subunit (GCSC) genes in both MCF-7 and U87 cancer cell lines. Since the EA-fraction showed most efficient growth inhibitory effect, it was further purified and a total of nine compounds and some monomeric and dimeric flavan-3-ols were identified and characterized. Three compounds viz., epicatechin (EC), gallic acid (GA) and ursolic acid (UA) were taken on the basis of their higher yield and 10 μg/ml of each was mixed together. The concentration used in this study for PRE, EA- and Hex-fraction was 100 μg/ml, which was higher than the IC₅₀ value. Apoptotic cell death in the PRE, EA-fraction and EC+GA+UA treated cancer cell cultures was significantly greater than in normal cells due to suppression of anti-apoptotic protein Bcl2 following treatment. Depletion of glutathione by downregulating GCSC was also observed. Induction of apoptosis and lowering the level of glutathione are considered to be positive activity for an anticancer agent. Therefore, modulation of GSH concentration in tumor cells by PRE and its EA-fraction opened up the possibility of a new therapeutic approach because these plant products are not harmful to normal cells and may regulate the tumor cellular response to different anticancer treatments. Thus, it would be interesting to examine efficacy of these plant products or EA-fraction in human cancer treatment.     

Characterization of <Emphasis Type="Italic">p</Emphasis>-hydroxybenzaldehyde dehydrogenase,the final enzyme of <Emphasis Type="Italic">p</Emphasis>-hydroxybenzoic acid biosynthesis in hairy roots of <Emphasis Type="Italic">Daucus carota</Emphasis>

Hairy root cultures of Daucus carota respond to methyl-jasmonate treatment with enhanced accumulation of p-hydroxybenzoic acid. The final C₁-side chain of this compound is shaped by p-hydroxybenzaldehyde dehydrogenase (HBD) that catalyzes the formation of p-hydroxybenzoic acid from p-hydroxybenzaldehyde in the presence of NAD⁺. HBD was biochemically characterized from cell-free hairy root extracts of D. carota. The preferred substrate for HBD was p-hydroxybenzaldehyde. The apparent K _m values were 54.8 and 74.4 μM for p-hydroxybenzaldehyde and NAD⁺, respectively. Divalent metal cations did not significantly affect enzyme activity.     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.     

Environmental variables responsible for Zebu cattle thermal comfort acquisition

International Journal of Biometeorology - The aim of this study was to estimate, using data mining, which microclimate and behavioral variables affect the behavior of animals to seek shaded or...