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Record W3142858718 · doi:10.14740/wjnu153e

Upregulation of Vascular Endothelial Growth Factor Expression in the Kidney Could Be Reversed Following Treadmill Exercise Training in Type I Diabetic Rats

2014· article· en· W3142858718 on OpenAlexvenueno aff
Al-Jarrah

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
FundersJordan University of Science and Technology
KeywordsMedicineDiabetes mellitusDiabetic nephropathyVascular endothelial growth factorInternal medicineStreptozotocinEndocrinologyDownregulation and upregulationKidneyTreadmillVEGF receptors

Abstract

fetched live from OpenAlex

Background: Nephropathy is a significant complication of diabetes mellitus, which is associated with high morbidity and mortality. Exercise training has been shown to have renoprotective effects in diabetes. Unregulated vascular endothelial growth factor (VEGF) has been demonstrated in the diabetic kidney. Thus, the aim of our study is to illustrate the impact of endurance exercise training on the renal VEGF expression in type I diabetic rats. Methods: Forty normal Sprague-Dawley rats were randomly divided into the following equal groups: sedentary control (SC), exercised control (EC), sedentary diabetic (SD) rats and exercised diabetic (ED) rats. Then, diabetes mellitus was induced by streptozotocin in the rats in the two diabetic groups. The expression of VEGF in the renal tissue in each of the four different groups was assessed by immunohistochemistry. Results: Renal VEGF expression was significantly (P < 0.01) higher in SD compared with that in SC. However, exercise training significantly (P < 0.01) reduced VEGF expression in the renal tissue in ED compared with that in SD. Conclusion: Our present data suggest that treadmill exercise training suppressed diabetes-induced upregulation in the renal VEGF expression. World J Nephrol Urol. 2014;3(1):25-29 doi: http://dx.doi.org/ 10.14740 / wjnu153e

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.339

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 head0.015
GPT teacher head0.251
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

Citations10
Published2014
Admission routes1
Has abstractyes

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