Mycotoxins and Microbial Disruption: Insights Into Fungal Pathogenesis and Gut Health: A Literature Review
Bibliographic record
Abstract
The human body contains more microbial cells than human cells, with the gut microbiota playing a central role in maintaining homeostasis and regulating immune responses. Mycotoxins, toxic secondary metabolites produced by fungi, often enter the body through contaminated crops and poorly stored food. These compounds can disrupt the gut microbial balance, compromise intestinal barrier integrity, and suppress immune function, increasing susceptibility to infection and disease. Understanding these interactions is essential in evaluating the full impact of mycotoxins on host health. This review explores the interplay between host cells, gut microbiota, and six major food-associated mycotoxins. Findings from both human and animal studies are discussed, focusing on how these toxins disturb microbial communities, induce epithelial damage, and interfere with immune regulation. Mycotoxins contribute to dysbiosis by suppressing beneficial bacteria such as Lactobacillus and Bifidobacterium while promoting the overgrowth of inflammatory species like Escherichia coli and Clostridium perfringens. These microbial shifts are closely linked to increased intestinal permeability and pro-inflammatory signalling. A significant concern involves masked mycotoxins—plant-conjugated derivatives that microbial enzymes reactivate in the gut, leading to enhanced toxicity. These forms can escape early detoxification and damage intestinal cells by disrupting membranes and inducing oxidative stress. This review emphasizes the need for therapeutic strategies addressing fungal invasion and mycotoxin toxicity. Potential approaches include inhibiting fungal adhesion, blocking microbial activation of masked toxins, and restoring microbial balance through targeted interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".