Evidence-based assessment of antiosteoporotic activity of petroleum-ether extract of Cissus quadrangularis Linn. on ovariectomy-induced osteoporosis

The increasing incidence of postmenopausal osteoporosis and its related fractures have become global health issues in the recent days. Postmenopausal osteoporosis is the most frequent metabolic bone disease; it is characterized by a rapid loss of mineralized bone tissue. Hormone replacement therapy has proven efficacious in preventing bone loss but not desirable to many women due to its side-effects. Therefore we are in need to search the natural compounds for a treatment of postmenopausal symptoms in women with no toxic effects. In the present study, we have evaluated the effect of petroleum-ether extract of Cissus quadrangularis Linn. (CQ), a plant used in folk medicine, on an osteoporotic rat model developed by ovariectomy. In this experiment, healthy female Wistar rats were divided into four groups of six animals each. Group 1 was sham operated. All the remaining groups were ovariectomized. Group 2 was fed with an equivolume of saline and served as ovariectomized control (OVX). Groups 3 and 4 were orally treated with raloxifene (5.4 mg/kg) and petroleum-ether extract of CQ (500 mg/kg), respectively, for 3 months. The findings were assessed on the basis of animal weight, morphology of femur, and histochemical localization of alkaline phosphatase (ALP) (an osteoblastic marker) and tartrate-resistant acid phosphatase (TRAP) (an osteoclastic marker) in upper end of femur. The study revealed for the first time that the petroleum-ether extract of CQ reduced bone loss, as evidenced by the weight gain in femur, and also reduced the osteoclastic activity there by facilitating bone formation when compared to the OVX group. The osteoclastic activity was confirmed by TRAP staining, and the bone formation was assessed by ALP staining in the femur sections. The color intensity of TRAP and ALP enzymes from the images were evaluated by image analysis software developed locally. The effect of CQ was found to be effective on both enzymes, and it might be a potential candidate for prevention and treatment of postmenopausal osteoporosis. The biological activity of CQ on bone may be attributed to the phytogenic steroids present in it

Isolation and determination of the primary structure of a lectin protein from the serum of the American alligator (Alligator mississippiensis)

Mass spectrometry in conjunction with de novo sequencing was used to determine the amino acid sequence of a 35. kDa lectin protein isolated from the serum of the American alligator that exhibits binding to mannose. The protein N-terminal sequence was determined using Edman degradation and enzymatic digestion with different proteases was used to generate peptide fragments for analysis by liquid chromatography tandem mass spectrometry (LC MS/MS). Separate analysis of the protein digests with multiple enzymes enhanced the protein sequence coverage. De novo sequencing was accomplished using MASCOT Distiller and PEAKS software and the sequences were searched against the NCBI database using MASCOT and BLAST to identify homologous peptides. MS analysis of the intact protein indicated that it is present primarily as monomer and dimer in vitro. The isolated 35. kDa protein was ~. 98% sequenced and found to have 313 amino acids and nine cysteine residues and was identified as an alligator lectin. The alligator lectin sequence was aligned with other lectin sequences using DIALIGN and ClustalW software and was found to exhibit 58% and 59% similarity to both human and mouse intelectin-1. The alligator lectin exhibited strong binding affinities toward mannan and mannose as compared to other tested carbohydrates. © 2011 Elsevier Inc

Identification of missing variants by combining multiple analytic pipelines

Abstract Background After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total. Results We analyzed 10,000 exomes from the Alzheimer’s Disease Sequencing Project (ADSP) using multiple analytic pipelines consisting of different read aligners and variant calling strategies. We compared variants identified by using two aligners in 50,100, 200, 500, 1000, and 1952 samples; and compared variants identified by adding single-sample genotyping to the default multi-sample joint genotyping in 50,100, 500, 2000, 5000 and 10,000 samples. We found that using a single pipeline missed increasing numbers of high-quality variants correlated with sample sizes. By combining two read aligners and two variant calling strategies, we rescued 30% of pass-QC variants at sample size of 2000, and 56% at 10,000 samples. The rescued variants had higher proportions of low frequency (minor allele frequency [MAF] 1–5%) and rare (MAF < 1%) variants, which are the very type of variants of interest. In 660 Alzheimer’s disease cases with earlier onset ages of ≤65, 4 out of 13 (31%) previously-published rare pathogenic and protective mutations in APP, PSEN1, and PSEN2 genes were undetected by the default one-pipeline approach but recovered by the multi-pipeline approach. Conclusions Identification of the complete variant set from sequencing data is the prerequisite of genetic association analyses. The current analytic practice of calling genetic variants from sequencing data using a single bioinformatics pipeline is no longer adequate with the increasingly large projects. The number and percentage of quality variants that passed quality filters but are missed by the one-pipeline approach rapidly increased with sample size

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-