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- Amin, Heba M., et al.
(författare)
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1,2 Propanediol utilization by Lactobacillus reuteri DSM 20016, role in bioconversion of glycerol to 1,3 propanediol, 3-hydroxypropionaldehyde and 3-hydroxypropionic acid
- 2013
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Ingår i: Journal of Genetic Engineering and Biotechnology. - : Springer Science and Business Media LLC. - 1687-157X. ; 11:1, s. 53-59
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Tidskriftsartikel (refereegranskat)abstract
- The objective of the presented work is to demonstrate the metabolism of 1,2 propandiol by Lactobacillus reuteri and to elucidate the metabolites produced during the process. This Metabolic pathway is crucial for biotechnological applications using L. reuteri in bioconversion of glycerol to industrially important plate-form chemicals. L. reuteri grown on minimal media containing 1,2 propanediol was able to utilize the compound as a sole carbon and energy source. The growth of the bacteria was linear with time; however the specific growth rate was significantly low compared to bacteria grown on the same media in the presence of glucose.The fermentation of 1,2 propanediol by L. reuteri in presence and absence of glucose was followed for 72 h and the metabolites produced during the process were detected using HPLC. 1,2 Propanediol was completely converted to propionaldhyde in a time dependent fashion, this process had a higher rate in presence of glucose. Consequently the produced propionaldhyde was converted to propionic acid and propanol in a skewed equimolar manner. In presence of glucose: acetic acid, lactic acid, succinic acid and ethanol were detected while in absence of glucose only minute amounts of acetic acid and lactic acid were detected which indicates presence of different metabolic pathways for glucose and 1,2 propanediol metabolism. Resting cells of L. reuteri induced in presence of 1,2 propanediol have shown significant capabilities to convert aqueous glycerol to 1,3 propanediol, 3-hydroxypropionaldhyde and a compound proposed to be 3-hydroxypropionic acid as detected by gas chromatographic technique.
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| 3. |
- Jasmine Pemeena Priyadarsini, M., et al.
(författare)
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Lung Diseases Detection Using Various Deep Learning Algorithms
- 2023
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Ingår i: Journal of Healthcare Engineering. - : Hindawi Publishing Corporation. - 2040-2295 .- 2040-2309. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
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