Minimally-Invasive Imaging of Sublingual Vessels in Mice – A New Method to Study Microvascular Changes in Experimental Sepsis
Bibliographic record
Abstract
Sepsis causes profound microcirculatory dysfunction, where heterogeneous capillary perfusion and abnormal vasomotion contribute to tissue hypoxia and organ failure. Sublingual videomicroscopy is established in large animals and humans to monitor these alterations, but this approach has not been applied in murine models. We developed a method to assess sublingual perfusion and vasomotion in mice using sidestream dark field (SDF) videomicroscopy. Male C57BL/6 mice were anesthetized, and sublingual vessels were imaged for 90 minutes. Perfusion and vasomotion parameters were assessed, and a Fast Fourier Transform was performed on vasomotion data to characterize the frequency components of rhythmic microvessel diameter changes. Another group of animals was administered lipopolysaccharide (LPS) intraperitoneally as a model of systemic inflammation in sepsis. LPS-treated mice exhibited a significant decline in the proportion of perfused vessels at 90 minutes and in the microvascular flow index. Very low-frequency vasomotion (0.005–0.15 Hz) declined in controls but was preserved during endotoxemia, suggesting an active microvascular tone response to inflammatory stress. This study establishes the first murine protocol for sublingual SDF imaging, allowing early detection of perfusion deficits and vasomotor changes in experimental sepsis. The minimal-invasive approach offers a translational platform for mechanistic and therapeutic studies in sepsis.
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.
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) | 0.000 | 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 | 0.001 | 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".