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The Search for Antidotes Against Ricin

2024· review· en· W4391170020 on OpenAlexaff
Fernanda D. Botelho, Tanos C. C. França, Steven R. LaPlante

Bibliographic record

VenueMini-Reviews in Medicinal Chemistry · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicToxin Mechanisms and Immunotoxins
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniverzita Hradec Králové
KeywordsRicinoleic acidRicinRicinusCastor oilBiotechnologyBusinessChemistryBiologyToxinBiochemistry

Abstract

fetched live from OpenAlex

The castor plant (Ricinus communis) is primarily known for its seeds, which contain a unique fatty acid called ricinoleic acid with several industrial and commercial applications. Castor seeds also contain ricin, a toxin considered a chemical and biological warfare agent. Despite years of investigation, there is still no effective antidote or vaccine available. However, some progress has been made, and the development of an effective treatment may be on the horizon. To provide an updated overview of this issue, we have conducted a comprehensive review of the literature on the current state of research in the fight against ricin. This review is based on the reported research and aims to address the challenges faced by researchers, as well as highlight the most successful cases achieved thus far. Our goal is to encourage the scientific community to continue their efforts in this critical search.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.053
GPT teacher head0.357
Teacher spread0.303 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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