Intravital microscopic observation of the microvasculature during hemodialysis in healthy rats
Bibliographic record
Abstract
Hemodialysis (HD) provides life-saving treatment for kidney failure. Patient mortality is extremely high, with cardiovascular disease (CVD) being the leading cause of death. This results from both a high underlying burden of cardiovascular disease, as well as additional physiological stress from the HD procedure itself. Clinical observations indicate that HD is associated with microvascular dysfunction (MD), underlining the need for a fundamental pathophysiological assessment of the microcirculatory consequences of HD. We therefore successfully developed an experimental small animal model, that allows for a simultaneous real-time assessment of the microvasculature. Using in-house built ultra-low surface area dialyzers and miniaturized extracorporeal circuit, we successfully dialyzed male Wistar Kyoto rats and combined this with a simultaneous intravital microscopic observation of the EDL microvasculature. Our results show that even in healthy animals, a euvolemic HD procedure can induce a significant systemic hemodynamic disturbance and induce disruption of microvascular perfusion (as evidence by a reduction in the proportion of the observed microcirculation receiving blood flow). This study, using a new small animal hemodialysis model, has allowed direct demonstration that microvascular blood flow in tissue in skeletal muscle is acutely reduced during HD, potentially in concert with other microvascular beds. It shows that preclinical small animal models can be used to further investigate HD-induced ischemic organ injury and allow rapid throughput of putative interventions directed at reducing HD-induced multi-organ ischemic injury.
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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 | 0.000 |
| 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 | 0.001 |
| 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".