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Record W1537401029 · doi:10.3390/toxins7061989

A Novel Peptide-Binding Motifs Inference Approach to Understand Deoxynivalenol Molecular Toxicity

2015· review· en· W1537401029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueToxins · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMycotoxinTrichotheceneToxicityBiologyDetoxification (alternative medicine)Computational biologyPotencyBiochemistryBiotechnologyChemistryIn vitroMedicine

Abstract

fetched live from OpenAlex

Deoxynivalenol (DON) is a type B trichothecene mycotoxin that is commonly detected in cereals and grains world-wide. The low-tolerated levels of this mycotoxin, especially in mono-gastric animals, reflect its bio-potency. The toxicity of DON is conventionally attributed to its ability to inhibit ribosomal protein biosynthesis, but recent advances in molecular tools have elucidated novel mechanisms that further explain DON's toxicological profile, complementing the diverse symptoms associated with its exposure. This article summarizes the recent findings related to novel mechanisms of DON toxicity as well as how structural modifications to DON alter its potency. In addition, it explores feasible ways of expanding our understating of DON-cellular targets and their roles in DON toxicity, clearance, and detoxification through the utilization of computational biology approaches.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.310
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it