Automated Measurement of Microvascular Function Reveals Dysfunction in Systemic Sclerosis: A Cross-sectional Study
Bibliographic record
Abstract
Objective. The aim of our study was to determine whether an automated capture and analysis system could detect differences in structure and function of sublingual microvessels in patients with systemic sclerosis (SSc) compared to healthy controls. Methods. Intravital microscopy of the sublingual microcirculation was automatically captured and analyzed in 40 patients with SSc and 10 age-matched healthy controls. Results. Total and perfused microvascular density were lower in patients with SSc compared with controls (total microvascular density: 2471 ± 134 µm/mm2 vs 3067 ± 197 µm/mm2, p = 0.020; perfused microvascular density: 1708 ± 92 µm/mm2 vs 2192 ± 144 µm/mm2, p = 0.009). However, the relative percentage of perfused to total microvascular density was similar between SSc and controls (72 ± 2% vs 71 ± 2%, respectively, p = 0.429). Mean red blood cell (RBC) fraction, which indicates the longitudinal tube hematocrit of microvessel segments, was lower in patients with SSc compared with controls (69 ± 1% vs 77 ± 1%, respectively, p < 0.001). Perfused boundary region (PBR), a marker of endothelial glycocalyx barrier properties, was higher in patients with SSc compared with controls (2.1 ± 0.0 µm vs 1.9 ± 0.0 µm, respectively, p = 0.012), suggestive of a dysfunctional glycocalyx. There was an inverse association of PBR with perfused microvascular density (r = −0.40, p = 0.004) and RBC fraction (r = −0.80, p < 0.001). Conclusion. Our results indicate that automated capture and analysis of sublingual microvessel segments produces detailed, objective microvascular structural and functional data that have allowed us to distinguish patients with SSc from controls. These data suggest that microvascular structural and functional abnormalities present in patients with SSc could be at least partly due to a dysfunctional glycocalyx.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".