Lessons from the field: <i>Trichoderma</i> in agriculture and human health
Bibliographic record
Abstract
The use of Trichoderma in agriculture as both a biocontrol agent and biofertilizer hinges on its ability to colonize the rhizosphere, promote plant growth, endure adverse environments, compete for space and nutrients, and produce enzymes and secondary metabolites to mycoparasitize and infect other fungus. In humans, Trichoderma exhibits the capacity to infect various bodily tissues, leading to Trichodermosis. There has been a notable increase in cases ranging from superficial to fatal, invasive, and disseminated infections, particularly among immunocompromised individuals. Trichoderma species employ diverse strategies to colonize and survive in various environments, infecting phytopathogens; however, the mechanisms and virulence factors contributing to human infections remain poorly understood. In this mini review, we provide a brief overview and contextualization of the virulence mechanisms employed by Trichoderma in parasitizing other fungi, as well as those implicated in modulating plant immunity and inducing human infections. Furthermore, we discuss the similarity of these virulence factors capable of modulating the mammalian immune system and their potential implications for human infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".