Acute high-intensity interval exercise reduces human monocyte Toll-like receptor 2 expression in type 2 diabetes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Type 2 diabetes (T2D) is characterized by chronic low-grade inflammation that contributes to disease pathophysiology. Exercise has anti-inflammatory effects, but the impact of high-intensity interval training (HIIT) is not known. The purpose of this study was to determine the impact of a single session of HIIT on cellular, molecular, and circulating markers of inflammation in individuals with T2D. Participants with T2D ( n = 10) and healthy age-matched controls (HC; n = 9) completed an acute bout of HIIT (7 × 1 min at ~85% maximal aerobic power output, separated by 1 min of recovery) on a cycle ergometer with blood samples obtained before (Pre), immediately after (Post), and at 1 h of recovery (1-h Post). Inflammatory markers on leukocytes were measured by flow cytometry, and TNF-α was assessed in both LPS-stimulated whole blood cultures and plasma. A single session of HIIT had an overall anti-inflammatory effect, as evidenced by 1) significantly lower levels of Toll-like receptor (TLR) 2 surface protein expression on both classical and CD16+ monocytes assessed at Post and 1-h Post compared with Pre ( P < 0.05 for all); 2) significantly lower LPS-stimulated TNF-α release in whole blood cultures at 1-h Post ( P < 0.05 vs. Pre); and 3) significantly lower levels of plasma TNF-α at 1-h Post ( P < 0.05 vs. Pre). There were no differences between T2D and HC, except for a larger decrease in plasma TNF-α in HC vs. T2D (group × time interaction, P < 0.05). One session of low-volume HIIT has immunomodulatory effects and provides potential anti-inflammatory benefits to people with, and without, T2D.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it