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Lipid Profile and Liver Histochemistry in Animal Models Exposed to Cigarette Smoke

2012· article· en· W2314258510 on OpenAlexvenueno aff
Gabriel Olaiya Omotoso, Bernard Ufuoma Enaibe, Oluwole B. Akinola, RE Kadir, Abdulazeez Adelaja Akinlolu

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2012
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsnot available
Fundersnot available
KeywordsSmokeFatty liverCigarette smokeFibrosisPathologyChemistryMedicineInternal medicinePhysiologyBiologyDiseaseToxicology

Abstract

fetched live from OpenAlex

Cigarette smoke is known to be an important predisposing factor to many diseased conditions, such as cardiovascular diseases, liver disease, atherosclerosis and other metabolic disorders. The aim of this study was to examine the effects of exposure to smoke from burnt cotton wool and cigarette on plasma lipids, liver biochemistry and histology, in adult Wistar rats. The animals were divided into three groups of Control A: exposed to fresh atmospheric air; Group B: exposed to cotton wool smoke; and, Group C, exposed to cigarette smoke; and the experiment lasted for 35 days. The animals exposed to cigarette smoke and cotton wool smoke showed higher values of low density lipoprotein (LDL), and lower values of high density lipoprotein (HDL) compared to the control. The observation of the micro architecture and enzymes of the liver tissue revealed reduction in the number and size of liver cells, numerous fibrous tissues, elevated liver transaminases and reduction in endogenous anti-oxidants, with evidence of fatty degeneration, in animals exposed to cigarette smoke compared to those exposed to cotton wool smoke and fresh atmospheric air. Cigarette smoke caused accumulation of lipids in the liver cells, with evidence of on-going necrosis and fibrosis, which indicated the presence of non-alcoholic fatty liver disease.

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)0.000
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 score0.205
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.286
Teacher spread0.236 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

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