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Record W4406036546 · doi:10.4103/ijmr.2010_132_05_489

Botulinum toxin: Bioweapon & magic drug

2010· article· en· W4406036546 on OpenAlexaboutno aff
Ram Kumar Dhaked, Manglesh Kumar Singh, Padma Singh, Pallavi Gupta

Bibliographic record

VenueThe Indian Journal of Medical Research · 2010
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiologyDrugVirologyMedicinePhysicsPharmacologyBiology

Abstract

fetched live from OpenAlex

Botulinum neurotoxins, causative agents of botulism in humans, are produced by Clostridium botulinum , an anaerobic spore-former Gram positive bacillus. Botulinum neurotoxin poses a major bioweapon threat because of its extreme potency and lethality; its ease of production, transport, and misuse; and the need for prolonged intensive care among affected persons. A single gram of crystalline toxin, evenly dispersed and inhaled, can kill more than one million people. The basis of the phenomenal potency of botulinum toxin is enzymatic; the toxin is a zinc proteinase that cleaves neuronal vesicle associated proteins responsible for acetylcholine release into the neuromuscular junction. As a military or terrorist weapon, botulinum toxin could be disseminated via aerosol or by contamination of water or food supplies, causing widespread casualties. A fascinating aspect of botulinum toxin research in recent years has been development of the most potent toxin into a molecule of significant therapeutic utility. It is the first biological toxin which is licensed for treatment of human diseases. In the late 1980s, Canada approved use of the toxin to treat strabismus, in 2001 in the removal of facial wrinkles and in 2002, the FDA in the United States followed suit. The present review focuses on both warfare potential and medical uses of botulinum neurotoxin.

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)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.010
Insufficient payload (model declined to judge)0.0070.001

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 head0.062
GPT teacher head0.408
Teacher spread0.347 · 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
GenreEmpirical

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

Citations37
Published2010
Admission routes1
Has abstractyes

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