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Record W2076665733 · doi:10.6000/1927-5129.2014.10.05

Gender Differences in Nicotine Induced Dyslipidemia and Hyperglycemia in Mice

2014· article· en· W2076665733 on OpenAlexvenueno aff
Samina Bano, Shabana Saeed

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsDyslipidemiaEndocrinologyNicotineInternal medicineCorticosteroneCholesterolMedicineAlbuminMetabolic syndromeDiabetes mellitusHormone

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the prevalence of metabolic syndrome in nicotine treated male and female mice and to evaluate gender related differences. For these purposes adult male and female BALB/C mice were subjected to chronic nicotine treatment (3.08mg/100ml in drinking water) for 4 weeks. Serum glucose, albumin, corticosterone and lipid profile levels were determined. Body weight changes were also monitored. We have found that nicotine treatment raises total cholesterol and glucose levels more in male as compared to female mice. Low density lipoprotein cholesterol (LDL-C) levels were increased by 35% (P<0.01) only in male mice. However rise in triglycerides were greater in females (28%) than males (21%) when compared with their respective controls. Serum albumin levels were increased in both sexes showing 13% greater increase in males as compared to females. However nicotine treatment had no effect on high density lipoprotein cholesterol, corticosterone levels and body weights in both genders. It is concluded that nicotine use is positively associated with LDL-C in males; the results are discussed in relation to prevalence of metabolic syndrome andrisk of cardiovascular events in nicotine users.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.056
GPT teacher head0.301
Teacher spread0.245 · 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 designObservational
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

Citations1
Published2014
Admission routes1
Has abstractyes

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