Growth studies on microalgae Euglena sanguinea in various natural eco-friendly composite media to optimize the lipid productivity.

The environmental and culture conditions of a potential microalgae Euglena sanguinea were optimized for maximizing the biomass productivity. It was found that the organic macronutrient from poultry litter and organic micronutrient of seaweed extract are very effective to enhance the lipid production. Complementing, these organic nutrients with commercial NPK fertilizer, boost up the lipid productivity by 30.9% in comparison with the established standard Euglena medium. The fatty acid profile of the extracted oil by GC-MS reveals its conformity with commercial diesel.

Feasibility of mcroRNAs as biomarkers for Barrett's Esophagus progression: a pilot cross-sectional, phase 2 biomarker study.

OBJECTIVES: Risk stratification of Barrett's esophagus (BE) using biomarkers remains an important goal. We evaluated feasibility and clinical accuracy of novel microRNA (miRNA) biomarkers for prediction of BE dysplasia. METHODS: Paired fresh-frozen and hematoxylin/eosin specimens from a prospective tissue repository where only biopsies with the lesion of interest (i.e., intestinal metaplasia (IM) or high-grade dysplasia (HGD)/esophageal adenocarcinoma (EAC)) occupying >50% of biopsy area were included. Tissue miRNA expression was determined by microarrays and validated by quantitative reverse transcription-PCR (qRT-PCR). Three groups were compared-group A, IM tissues from BE patients without dysplasia; group B, IM tissues from group C patients; and group C, dysplastic tissues from BE patients with HGD/EAC. RESULTS: Overall, 22 BE patients, 11 with and without dysplasia (mean age 64 ± 8.2 and 63 ± 11.6 years, respectively, all Caucasian males) were evaluated. Nine miRNAs were identified by high-throughout analysis (miR-15b, -21, -192, -205, 486-5p, -584, -1246, let-7a, and -7d) and qRT-PCR confirmed expression of miR-15b, -21, 486-5p, and let-7a. Two of 4 miRNAs (miR-145 and -203, but not -196a and -375) previously described in BE patients also exhibited differential expression. Sensitivity and specificity of miRNAs for HGD/EAC were miR-15b: 87 and 80%, miR-21: 93 and 70%, miR-203: 87 and 90%, miR-486-5p: 82 and 55%, and miR-let-7a: 88 and 70%. MiRNA-15b, -21, and -203 exhibited field effects (i.e., groups A and B tissues while histologically similar yet exhibited different miRNA expression). CONCLUSIONS: This pilot study demonstrates feasibility of miRNAs to discriminate BE patients with and without dysplasia with reasonable clinical accuracy. However, the specific miRNAs need to be evaluated further in future prospective trials.

DMSP--Database for Modeling Signaling Pathways. Combining biological and mathematical modeling knowledge for pathways.

OBJECTIVES: Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. METHODS: The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. RESULTS: DMSP--Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNFalpha pathway model was implemented and tested using this approach. CONCLUSION: DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and system-theoretic point of view.