Concurrent training remodels the subcutaneous adipose tissue extracellular matrix of people living with HIV: a non-randomized clinical trial
Bibliographic record
Abstract
Evaluate the effect of 12 wks of concurrent training (CT) in the extracellular matrix (ECM) of subcutaneous adipose tissue (SAT) in people living with HIV/AIDS (PLWHA). In the non-randomized clinical trial, 19 participants, 11 healthy (HIV–) and 18 PLWHA under the use of highly active antiretroviral therapy (HAART) for at least 1 year (HIV+). All participants engaged in a moderate-intensity CT program for 12 weeks, 3 times a week. Before and after CT, aerobic and strength performance were assessed, as well as anthropometric and biochemical blood profiles. In addition, SAT biopsies were performed for histologic and morphometric analyses. Statistical analysis was carried out with R Studio, using descriptive and inferential analysis, ANOVA test, and mixed-effect model (P < 0.05). HIV+ showed higher levels of very-low-density lipoproteins and triglycerides and lower levels of high-density lipoproteins at baseline than HIV– (P < 0.05). All groups showed improved aerobic and strength performances (P < 0.05). Both groups showed reduced adipocyte sizes after CT (P < 0.05). Lastly, HIV+ presented smaller adipocytes and higher elastic fiber deposition at baseline and decreased after training only in HIV+, similar to the HIV group. Thus, CT in PLWHA promoted a decrease in the size heterogeneity of adipocytes and elastic fiber deposition, remodeling the ECM, and improving the SAT fibrosis profile. Brazilian Clinical Trials Registry (ensaiosclinicos.gov.br – UTN: U1111-1214-3022). Novelty: Adipose tissue fibrosis is improved by training in people living with HIV. Concurrent training remodels adipose tissue extracellular matrix.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".