Detection of Mycotoxigenic Fungi and Residual Mycotoxins in Cannabis Buds Following Gamma Irradiation
Bibliographic record
Abstract
Cannabis plants are susceptible to microbial contamination, including fungi capable of producing harmful mycotoxins. The presence of these toxins in cannabis products poses serious health risks, especially when used for medical purposes in immunocompromised people. This study evaluated the presence of fungi and mycotoxins in dried cannabis buds following gamma irradiation, using culture-based techniques, PCR/qPCR, and ELISA. Irradiation significantly reduced fungal and bacterial loads, eliminating culturable bacteria but did not achieve complete sterilization. Viable spores of toxigenic fungal genera, such as Aspergillus, Penicillium, and Fusarium, persisted. Sequencing of ITS amplicons revealed dominant mycotoxigenic fungi in non-irradiated (NR), irradiated (IR) and licensed producer (LP) samples, while next-generation sequencing (NGS) revealed additional non-culturable toxigenic species. PCR/qPCR detected biosynthetic genes for aflatoxins, trichothecenes, ochratoxins, and deoxynivalenol across all samples, with gene copy numbers remaining stable post-irradiation, suggesting DNA damage without full degradation. ELISA confirmed aflatoxin, ochratoxin, DON, and T2 toxins in both IR and LP samples at variable concentrations. While LP samples showed lower microbial counts and gene abundance, residual DNA and toxins were still detected. Our study shows that while irradiation decreases microbial loads, it does not completely remove toxigenic fungi or their metabolites. Ensuring the safety of cannabis products necessitates a multifaceted assessment that incorporates cultural, molecular, and immunological techniques, in parallel with more stringent microbial standards during production stage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".