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Record W2617913537 · doi:10.15407/ubj89.si01.093

Effect of hydrogen sulfide-releasing aspirin on esophageal and gastric mucosa compromised by stress injury

2017· article· en· W2617913537 on OpenAlexafffund
Оksana Zayachkivska, Nazar Bula, Ya. I. Pavlovskiy, Irena Pshyk‐Titko, Elena Gavriluk, Oksana Grushka, John L. Wallace

Bibliographic record

VenueThe Ukrainian Biochemical Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersCanadian Institutes of Health ResearchCoastal Response Research Center, University of New Hampshire
KeywordsAspirinHydrogen sulfideGastric mucosaGastroenterologyMedicineInternal medicineChemistryStomachOrganic chemistry

Abstract

fetched live from OpenAlex

Recent data of study H 2 S in gastrointestinal tract has proven its potent cytoprotection on mucosal defense among acid-related diseases in the gut. The aim was to evaluate the effects of H 2 S-releasing aspirin derivative (ATB-340) on esophageal and gastric mucosa compromised by stress injury. Rats were treated with vehicle (control), aspirin (10 mg/kg), ATB-340 (17.5 mg/kg) single or 9 days duration, with or without induction of stress injury. Esophageal mucosa, gastric mucosa were estimated by histopathological damage scoring. Serological levels of VCAM-1, IL-6 by ELISA. ATB-340 treatment resulted in protective effect and lower grade of damage score in esophageal mucosa and gastric mucosa lesions vs effect of aspirin in single or 9 days applications. The serum levels of VCAM, IL-6 in rats who were aspirin-treated and subjected to stressinjury were higher than those in control animals. Treatment with ATB-340 produced an anti-inflammatory effect by decreasing VCAM and IL-6 vs aspirin. Cytoprotective effect of ATB-340 on esophageal mucosa and gastric mucosa was modulated by inhibi ting inflammation and improving endothelial functions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.273
Teacher spread0.265 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

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