Mycotoxins and bone growth: a review of the literature on associations between xenobiotic exposure and bone growth and development
Bibliographic record
Abstract
Mycotoxins are secondary metabolites of fungi that are known to be associated with linear growth faltering because of their impact on inflammation, intestinal damage, inhibition of protein synthesis, and micronutrient absorption. In this narrative review, we aim to extend this analysis to further explore associations between mycotoxins (aflatoxins, ochratoxins, trichothecenes including deoxynivalenol, T-2 toxin, and fumonisins) and long-bone growth, particularly during the saltatory periods of development. Linear growth is a direct function of skeletal development and long-bone growth. We therefore explored biological pathways and mechanisms of impact of these toxins in both animal and human studies, in addition to the epidemiology literature (post-2020). Given what is known of the effects of individual and combinations of mycotoxins based on the animal literature, we have identified a need for further research and examination of how these toxins and exposures may be studied in humans to elucidate the downstream impact on bone-related biomarkers and anthropometric indices used to identify and predict stunting in population-based studies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".