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Four different methods comparison for extraction of astaxanthin from green alga Haematococcus pluvialis.

ABSTRACT: Haematococcus pluvialis is one of the potent organisms for production of astaxanthin. Up to now, no efficient method has been achieved due to its thick cell wall hindering solvent extraction of astaxanthin. In this study, four different methods, hydrochloric acid pretreatment followed by acetone extraction (HCl-ACE), hexane/isopropanol (6:4, v/v) mixture solvents extraction (HEX-IPA), methanol extraction followed by acetone extraction (MET-ACE, 2-step extraction), and soy-oil extraction, were intensively evaluated for extraction of astaxanthin from H. pluvialis. Results showed that HCl-ACE method could obtain the highest oil yield (33.3±1.1%) and astaxanthin content (19.8±1.1%). Quantitative NMR analysis provided the fatty acid chain profiles of total lipid extracts. In all cases, oleyl chains were predominant, and high amounts of polyunsaturated fatty acid chains were observed and the major fatty acid components were oleic acid (13-35%), linoleic acid (37-43%), linolenic acid (20-31%), and total saturated acid (17-28%). DPPH radical scavenging activity of extract obtained by HCl-ACE was 73.2±1.0%, which is the highest amongst the four methods. The reducing power of extract obtained by four extraction methods was also examined. It was concluded that the proposed extraction method of HCl-ACE in this work allowed efficient astaxanthin extractability with high antioxidant properties.

SUBMITTER: Dong S

PROVIDER: S-EPMC3916103 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:The unicellular green alga Haematococcus pluvialis has been exploited as a cell factory to produce the high-value antioxidant astaxanthin for over two decades, due to its superior ability to synthesize astaxanthin under adverse culture conditions. However, slow vegetative growth under favorable culture conditions and cell deterioration or death under stress conditions (e.g., high light, nitrogen starvation) has limited the astaxanthin production. In this study, a new paradigm that integrated heterotrophic cultivation, acclimation of heterotrophically grown cells to specific light/nutrient regimes, followed by induction of astaxanthin accumulation under photoautotrophic conditions was developed. First, the environmental conditions such as pH, carbon source, nitrogen regime, and light intensity, were optimized to induce astaxanthin accumulation in the dark-grown cells. Although moderate astaxanthin content (e.g., 1% of dry weight) and astaxanthin productivity (2.5?mg?L(-1) ?day(-1) ) were obtained under the optimized conditions, a considerable number of cells died off when subjected to stress for astaxanthin induction. To minimize the susceptibility of dark-grown cells to light stress, the algal cells were acclimated, prior to light induction of astaxanthin biosynthesis, under moderate illumination in the presence of nitrogen. Introduction of this strategy significantly reduced the cell mortality rate under high-light and resulted in increased cellular astaxanthin content and astaxanthin productivity. The productivity of astaxanthin was further improved to 10.5?mg?L(-1) ?day(-1) by implementation of such a strategy in a bubbling column photobioreactor. Biochemical and physiological analyses suggested that rebuilding of photosynthetic apparatus including D1 protein and PsbO, and recovery of PSII activities, are essential for acclimation of dark-grown cells under photo-induction conditions. Biotechnol. Bioeng. 2016;113: 2088-2099. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Project description:BackgroundAutomated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants.MethodsWe experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as [Formula: see text]), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks.ResultsWhen [Formula: see text] was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when [Formula: see text], and a Micro-F1 of 85.41% for the Spanish dataset when [Formula: see text]. When [Formula: see text] fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when [Formula: see text], and a Micro-F1 of 39.1% for the Spanish dataset when [Formula: see text]. Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models.ConclusionsThis study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.

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Four different methods comparison for extraction of astaxanthin from green alga Haematococcus pluvialis.

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