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- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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| 2. |
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- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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| 6. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 9. |
- Bousquet, J, et al.
(author)
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Nrf2-interacting nutrients and COVID-19: time for research to develop adaptation strategies
- 2020
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In: Clinical and translational allergy. - : Wiley. - 2045-7022. ; 10:1, s. 58-
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Journal article (peer-reviewed)abstract
- There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPARγ:Peroxisome proliferator-activated receptor, NFκB: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2α:Elongation initiation factor 2α). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT1R axis (AT1R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity.
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| 10. |
- Flint, AC, et al.
(author)
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Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation
- 2015
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In: International journal of stroke : official journal of the International Stroke Society. - : SAGE Publications. - 1747-4949. ; 10:6, s. 815-821
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Journal article (peer-reviewed)abstract
- The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of outcome probability should be possible by computing the logistic equation with patient-specific continuous variables. Methods We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke – Monitoring Study to develop and validate the performance of a model-derived estimation of outcome probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. Results The THRIVE-c model performed similarly in the randomly chosen development cohort ( n = 6194, area under the curve = 0·786, 95% confidence interval 0·774–0·798) and validation cohort ( n = 6013, area under the curve = 0·784, 95% confidence interval 0·772–0·796) ( P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777–0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737–0·755) ( P < 0·001). Conclusion By computing the logistic equation with patientspecific continuous variables in the THRIVE-c calculation, outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.
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