Abstract

Short Communication

A Short communication on Pichia pastoris vs. E. coli: Efficient expression system

Viswanath Vittaladevaram*

Published: 2021-09-29 00:00:00 | Volume 5 - Issue 1 | Pages: 049-050

One of the major challenges for vaccine producing companies is having favourable conditions for efficient expression system for living organisms in order to produce biologicals. Several companies across the globe looking for several alternative sources for better yield through efficient expression based system.

Read Full Article HTML DOI: 10.29328/journal.apb.1001016 Cite this Article Read Full Article Pdf

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OCLC Number/Unique Identifier: 7355974901

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Published on: 9th April, 2018

OCLC Number/Unique Identifier: 7906092414

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Published on: 19th December, 2019

OCLC Number/Unique Identifier: 9267261637

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OCLC Number/Unique Identifier: 8513004205

Background: The proposal that MALDI-ToF mass spectrometry could be used as a direct, rapid and affordable diagnostic tool in clinical laboratory medicine has moved from a theoretical possibility to a reality for Microbiology. Several studies have proposed the application of this technology in obstetric and gynaecological evaluation of patients. In particular, we have proposed that the adoption of MALDI-ToF mass spectrometry in examination of maternal pregnancy urine samples for the detection of Downs syndrome. Methods: A retrospective collection of 20 Down Syndrome and 100 non-aneuploid pregnancy urines at 12 to 14 weeks gestation, collected in 2007-2008 from high risk pregnancy cohorts, were examined by MALDI-ToF mass spectrometry in the mass/charge range between 1000 and 100000 m/z. Normalisation of spectral data was defined using mass bins of 100 m/z expressed as a percentage of the total ion count of the mass spectra from 2000 to 11000 m/z. Of the ninety 100 m/z bins, forty-six were identified as m/z bins at which statistically significant differences in spectra occurred between Downs and control/non-aneuploid samples. Based on the differences and variance, for values at these bins, weighted scores of the probability of being Downs were assigned. Comparative algorithms consisting of various mass bins were tested for ability to distinguish Down syndrome from non-aneuploid pregnancy. Results: Although various algorithms could distinguish Downs from non-aneuploid controls, it was found that gestational age was a confounding factor and that if separated into gestational age matched cohorts the ability to distinguish the groups improved dramatically e.g. whilst a 19 bins algorithm separated 100% of Downs from non-aneuploid pregnancies for a 9% false positive rate in the mixed gestational ages group; a two bin algorithm distinguished 100% of Downs for a 6% false positive rate for the 12 weeks gestational age pregnancies. Conclusion: Normalised MALDI-ToF mass spectra, at 2000 to 11000 m/z, of maternal urine gives rise to gestational age specific screening tests algorithms for Downs’s syndrome.
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OCLC Number/Unique Identifier: 8530268032

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Published on: 11th February, 2020

OCLC Number/Unique Identifier: 8550274050

In the classification of cancer data sets, we note that they contain a number of additional features that influence the classification accuracy. There are many evolutionary algorithms that are used to define the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space. In this paper, a method of feature selection was proposed through two consecutive stages in the first stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size. In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specific number of features affecting the process of classification, which came from the first stage. The proposed algorithm, FMI_BGWO, describes efficiency and effectiveness by obtaining a higher classification accuracy and a small number of selected genes compared to other competitor algorithms.
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