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Record W2794969988 · doi:10.1186/s40360-018-0196-3

A newly developed oxime K203 is the most effective reactivator of tabun-inhibited acetylcholinesterase

2018· article· en· W2794969988 on OpenAlexaff
Kamil Kuča, Kamil Musílek, Daniel Jun, Jana Žďárová Karasová, Eugenie Nepovimová, Ondřej Soukup, Martina Hrabinová, John Mikler, Tanos C. C. França, Elaine F. F. da Cunha, Alexandre A. de Castro, Martin Vališ, Teodorico C. Ramalho

Bibliographic record

VenueBMC Pharmacology and Toxicology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsDefence Research and Development Canada
FundersGrantová Agentura České RepublikyUniverzita Hradec Králové
KeywordsTabunOximeAcetylcholinesteraseChemistryPharmacologyNerve agentMedicineBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Background: Based on in vitro and in vivo rat experiments, the newly developed acetylcholinesterase (AChE) reactivator, K203, appears to be much more effective in the treatment of tabun poisonings than currently fielded oximes. Methods: To determine if this reactivating efficacy would extend to humans, studies were conducted in vitro using human brain homogenate as the source of AChE. The efficacy of K203 was compared with commercially available oximes; pralidoxime, obidoxime and asoxime (HI-6). Results: Reactivation studies showed that K203 was the most effective reactivator with a second order kinetic constant (k r ) of 2142 min -1 . M -1 , which was 51 times higher than that obtained for obidoxime (k r = 42 min -1 . M -1 ). Both pralidoxime and asoxime (HI-6) failed to significantly reactivate tabun-inhibited human AChE. Discussion: According to these results and previous studies, using K203, it appears that oxime K203 is the most effective reactivator of tabun-inhibited cholinesterase in several species including humans and should be considered as a possible medical countermeasure to tabun exposure.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations65
Published2018
Admission routes1
Has abstractyes

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