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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 OpenAlex
Xiang Guo, Wei Guo, Tiandi Li, Fen Liu, Jinpeng Zhou, Meiqiong Guo

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

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
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