Correlations of Calf Muscle Macrophage Content With Muscle Properties and Walking Performance in Peripheral Artery Disease
Bibliographic record
Abstract
Background Peripheral artery disease ( PAD ) is a manifestation of atherosclerosis characterized by reduced blood flow to the lower extremities and mobility loss. Preliminary evidence suggests PAD damages skeletal muscle, resulting in muscle impairments that contribute to functional decline. We sought to determine whether PAD is associated with an altered macrophage profile in gastrocnemius muscles and whether muscle macrophage populations are associated with impaired muscle phenotype and walking performance in patients with PAD. Methods and Results Macrophages, satellite cells, and extracellular matrix in gastrocnemius muscles from 25 patients with PAD and 7 patients without PAD were quantified using immunohistochemistry. Among patients with PAD , both the absolute number and percentage of cluster of differentiation (CD) 11b+ CD 206+ M2‐like macrophages positively correlated to satellite cell number ( r =0.461 [ P =0.023] and r =0.416 [ P =0.042], respectively) but not capillary density or extracellular matrix. The number of CD 11b+ CD 206− macrophages negatively correlated to 4‐meter walk tests at normal ( r =−0.447, P =0.036) and fast pace ( r =−0.510, P =0.014). Extracellular matrix occupied more muscle area in PAD compared with non‐ PAD (8.72±2.19% versus 5.30±1.03%, P <0.001) and positively correlated with capillary density ( r =0.656, P <0.001). Conclusions Among people with PAD , higher CD 206+ M2‐like macrophage abundance was associated with greater satellite cell numbers and muscle fiber size. Lower CD 206− macrophage abundance was associated with better walking performance. Further study is needed to determine whether CD 206+ macrophages are associated with ongoing reparative processes enabling skeletal muscle adaptation to damage with PAD . Registration URL : https://www.clinicaltrials.gov ; Unique identifiers: NCT 00693940, NCT 01408901, NCT 0224660.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".