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Record W1968855032 · doi:10.1089/rej.2011.1266

A Phytochemical Approach to Experimental Metabolic Syndrome-Associated Renal Damage and Oxidative Stress

2012· article· en· W1968855032 on OpenAlexaff
Francesco Marotta, Archana Kumari, Roberto Catanzaro, Umberto Solimene, Shalini Jain, Emilio Minelli, Masatoshi Harada

Bibliographic record

VenueRejuvenation Research · 2012
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversité du QuébecInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsOxidative stressNitrotyrosineSMA*EndocrinologyInternal medicineKidneyFructoseDownregulation and upregulationChemistryMedicineBiochemistryNitric oxideNitric oxide synthase

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the effect of DTS-phytocompound on oxidant-antioxidant balance and protein damage in the kidneys of rats administered high doses of fructose. Adult male Wistar rats were divided into four groups. Group A received a control diet, whereas groups B and C were fed a high-fructose diet (60 g/100 g), the latter with additional DTS (50 mg/kg per day) for 60 days. Lipo- and nitro-peroxidation together with α-smooth muscle actin (α-SMA) expression in the glomerular and interstitial tissue of the kidneys were measured after 60 days. Fructose-fed rats showed significantly higher lipoperoxidation, 2,4-dinitrophenol and 3-nitrotyrosine protein adducts, and upregulation of α-SMA in the kidney. DTS significantly decreased such redox unbalance in renal tissue, while partially downregulating α-SMA (p<0.01). These data suggest the potential clinical benefit of DTS in protecting the kidneys from metabolic syndrome-associated changes; gender-related analysis is under way.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.392
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2012
Admission routes1
Has abstractyes

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