Bibliographic record
Abstract
The availability of immunotoxicity data for fungal toxins varies considerably for different toxins. The following is a comprehensive review of the most recent literature on the immunotoxicity of aflatoxins, fumonisins, gliotoxin, ochratoxins, patulin, and trichothecenes. Aflatoxin is an immunomodulating agent that acts primarily on cell-mediated immunity and phagocytic cell function. In addition to further characterization of aflatoxin-induced immunotoxicity in various species, some recent studies have focused on ameliorating the effects of aflatoxin by supplementing or amending the diet. The immunomodulatory effects of ochratoxins have also been considered for many years. Notably, recent studies have examined immune function in the offspring of rats and mice exposed to ochratoxin pre- and perinatally. Fumonisin toxicity has been characterized relatively recently in comparison to aflatoxin and ochratoxin, and fumonisin-induced immunotoxicity is an area of active research. As these studies progress, they may also clarify the role of sphingolipid metabolism in immune function. The most recent study of patulin immunotoxicity in mice indicates that exposure to levels found in foods and feeds would not likely result in immunotoxicity. Exposure to gliotoxin would most likely be by infection with gliotoxin-producing fungi. Although the toxin is immunosuppressive in vitro, the link between immunosuppression and the presence of gliotoxin in infected tissues in vivo has yet to be made. The trichothecenes can both suppress and stimulate immune function. By comparison, more information is available on the molecular events associated with trichothecene-induced immunomodulation than for any other fungal toxins. The molecular basis of immune function modulation by fungal toxins remains a frontier for future research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".