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Record W4394089624 · doi:10.6084/m9.figshare.21907798

In Vitro and In Vivo evaluation of montmorillonite for paraquat poisoning

2023· dataset· en· W4394089624 on OpenAlexaff
Xiang Guo, Wei Guo, Tiandi Li, Fen Liu, Jinpeng Zhou, Meiqiong Guo

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldMedicine
TopicParaquat toxicity studies and treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParaquatMontmorilloniteIn vivoIn vitroChemistryToxicologyBiologyBiochemistryBiotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Evaluation of montmorillonite for paraquat by in vitro and in vivo test. In vitro test were evaluated by a batch test, taking the paraquat concentration, adsorbents, reaction environment and time as indices, the absorption rate was screened by orthogonal design. In vivo test was executed with rabbits. Group 1: 4 rabbits dosed with montmorillonite. Group 2: 8 rabbits dosed with 200 mg/kg paraquat. Group 3: 6 rabbits dosed with 200 mg/kg paraquat then gavage with montmorillonite 5 min later. Group 4: 6 rabbits dosed with 200 mg/kg paraquat then gavage with montmorillonite 30 min later. Blood paraquat concentration, serum cytokines, blood gas analysis and histopathology of lung were implemented. In vitro test found that all the four factors influence the absorption rate of paraquat (P < 0.05). In vitro test found that oral montmorillonite could change toxicokinetics parameters of paraquat (P < 0.05); decrease raised serum TGF-β1 and HMGB1 (P < 0.05) and alleviate the histopathology damage of lung. Montmorillonite might exert its protective effects on paraquat induced damage.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.004

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 head0.121
GPT teacher head0.392
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes1
Has abstractyes

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