Effects of resistance training and estrogen replacement on adipose tissue inflammation in ovariectomized rats
Bibliographic record
Abstract
Estrogen deficiency is directly related to central obesity and low-grade inflammation. Hormonal replacement and exercise training are both able to decrease fat accumulation and inflammation in postmenopausal women. However, the efficiency of resistance training (RT) and estrogen replacement (ER) in minimizing adiposity and inflammation in the visceral adipose tissue (VAT) of ovariectomized (OVX) rats has not yet been elucidated. In this study, Sprague-Dawley rats were divided into the following 6 groups: sham-operated sedentary (Sham-Sed), OVX-Sed, Sham-RT, OVX-RT, OVX-Sed-ER, and OVX-RT-ER groups. ER was performed by implanting silastic capsules containing 17β-estradiol. For RT, the animals were required to climb a 1.1-m vertical ladder with conical flasks containing weights attached to their tails for 12 weeks. Histological analyses were used to evaluate morphological changes. Gene expression levels were determined by quantitative real-time reverse transcriptase polymerase chain reaction, and protein concentrations were determined using Multiplex/Luminex assays. Ovariectomy increased the body mass (BM), adipocyte area, and inflammation in the VAT, the latter of which was indicated by reduced interleukin-10 (48%) and increased tumor necrosis factor (TNF)-α concentration (∼3%). RT efficiently decreased BM, adipocyte area, and inflammation in the OVX groups. The combination of RT and ER decreased BM (19%) and the TNF-α concentration (18%) and increased the gene and protein expression levels of adiponectin (173% and 18%). These results indicate that RT and the combination of RT and ER are efficient strategies for reducing the BM and improving the inflammatory status of OVX rats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".