Regulatory role of vitamin E in the immune system and inflammation
Bibliographic record
Abstract
Vitamin E, a potent lipid-soluble antioxidant, found in higher concentration in immune cells compared to other cells in blood, is one of the most effective nutrients known to modulate immune function. Vitamin E deficiency has been demonstrated to impair normal functions of the immune system in animals and humans, which can be corrected by vitamin E repletion. Although deficiency is rare, vitamin E supplementation above current dietary recommendations has been shown to enhance the function of the immune system and reduce risk of infection, particularly in older individuals. The mechanisms responsible for the effect of vitamin E on the immune system and inflammation have been explored in cell-based, pre-clinical and clinical intervention studies. Vitamin E modulates T cell function through directly impacting T cell membrane integrity, signal transduction, and cell division, and also indirectly by affecting inflammatory mediators generated from other immune cells. Modulation of immune function by vitamin E has clinical relevance as it affects host susceptibility to infectious diseases such as respiratory infections, in addition to allergic diseases such as asthma. Studies examining the role of vitamin E in the immune system have typically focused on α-tocopherol; however, emerging evidence suggests that other forms of vitamin E, including other tocopherols as well as tocotrienols, may also have potent immunomodulatory functions. Future research should continue to identify and confirm the optimal doses for individuals at different life stage, health condition, nutritional status, and genetic heterogeneity. Future research should also characterize the effects of non-α-alpha-tocopherol vitamin E on immune cell function as well as their potential clinical application. © 2018 IUBMB Life, 71(4):487-494, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".