MétaCan
Menu
Back to cohort
Record W2284943080 · doi:10.1016/j.dib.2016.02.024

Dietary acrylamide exposure in male F344 rats: Dataset of systemic oxidative stress and inflammation markers

2016· article· en· W2284943080 on OpenAlexafffund
Xiaolei Jin, Melanie C. Coughlan, Jennifer Roberts, Rekha Mehta, Jayadev Raju

Bibliographic record

VenueData in Brief · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsHealth Canada
FundersGovernment of Canada
KeywordsOxidative stressAcrylamideUrineEndocrinologyInternal medicineChemistryIsoprostaneInflammationHomocysteineSystemic inflammationAntioxidantHigh-density lipoproteinLipid peroxidationCholesterolMedicineBiochemistry

Abstract

fetched live from OpenAlex

We previously reported that dietary acrylamide, at doses (10 and 50 mg/kg diet) known to cause rodent tumors, lowered serum total high density lipoprotein and total testosterone, increased serum lipase, and lowered lymphocytes levels together with other hematological parameters in male F344 rats exposed for 10 weeks (doi: 10.1016/j.etap.2014.11.009 [1]). Here we present data related to the role of food-borne acrylamide exposure (at 0, 5, 10 and 50 mg/kg diet) in the presence of low (7% wt/wt) or high (23.9% wt/wt) dietary fat on serum and urinary markers of oxidative stress and inflammation in F344 rats. Briefly, urine and serum samples were collected from the experimental animals a day prior to or at the time of necropsy, respectively and processed for enzyme-linked immunosorbent assay estimations of biochemical markers. Urine samples were analyzed for 8-hydroxydeoxyguanosine and isoprostane, and serum samples for total antioxidant capacity, paraoxonase 1 activity, c-reactive protein, homocysteine, oxidized low-density lipoprotein, intercellular adhesion molecule-1, thromboxin 2, and Nε-(carboxymethyl)lysine.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.472

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 communication0.0000.001
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 head0.038
GPT teacher head0.265
Teacher spread0.227 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueData in BriefSame topicPotato Plant ResearchFrench-language works237,207