Metformin Suppresses SARS-CoV-2 in Cell Culture

Parthasarathy, Haripriya; Tandel, Dixit; Harshan, Krishnan Harinivas

doi:10.1101/2021.11.18.469078

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…On the other hand, for DESeq2, see Table 6 (full list of drug repositioning is available in Additional file 8), The use of the second and third one, dexamethasone, resulted in lower 28-day mortality among those who received either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. [29], The seventh one, metformin, suppressed SARS-CoV-2 in cell culture [30]. The eighth one, etanercept, significantly decreased the risk of developing COVID-19 in patients with rheuma- toid arthritis or spondyloarthropathies [31].…”

Section: Drug Repositioningmentioning

confidence: 99%

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

Taguchi

Turki

2022

Preprint

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Background: Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P-values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Results: Optimizing the standard deviation such that the histogram of P-values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Conclusions: Tensor decomposition- and principal component analysis-based unsupervised feature extraction are perhaps better than state-of-art methods in regard to predicting differentially expressed genes because they achieve the desired property that the less expressed differentially expressed genes should be less likely selected or even associated with the same amount of logarithmic fold change, although they assume neither negative binomial distribution nor dispersion relation, which is usually assumed in state-of-art methods.

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Section: Drug Repositioningmentioning

confidence: 99%

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

Taguchi

Turki

2022

Preprint

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show abstract

“…Metformin, a well-known AMPK activator and mTORC1 inhibitor, has also been reported to have therapeutic benefits in elderly COVID-19 patients at nursing homes in USA [135]. According to a recent cell model, metformin pre-treatment resulted in further phosphorylation of AMPK and caused a ten-fold reduction of SARS-CoV-2 viral titers [136]. Nevertheless, mTORC1 hyperactivation is associated to ACE2 overexpression, which is one of the mechanisms explaining the possible therapeutic benefit of mTORC1 inhibitors [137].…”

Section: Agents Targeting the Mtorc1-autophagy-pathwaymentioning

confidence: 99%

Friend or Foe? Implication of the autophagy-lysosome pathway in SARS-CoV-2 infection and COVID-19

Wang¹,

Gao²,

Zhou³

et al. 2022

Int. J. Biol. Sci.

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Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods

Taguchi

Turki

2022

Sci Rep

View full text Add to dashboard Cite

Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes’ identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.

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Metformin Suppresses SARS-CoV-2 in Cell Culture

Cited by 3 publications

References 57 publications

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods

Friend or Foe? Implication of the autophagy-lysosome pathway in SARS-CoV-2 infection and COVID-19

Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods

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