Bibliographic record
Abstract
Injury to the capillary endothelium can be devastating for renal and cardiac function. To halt the progression of chronic kidney disease (CKD) and heart failure (HF) preservation of the microvascular endothelial cell (EC) function and structure is of great importance.1 Increasing knowledge about microvascular EC function and structure during renal and cardiac injury and repair is needed to develop new therapeutic strategies for CKD, HF and combined injury during cardiorenal syndrome (CRS). Therefore, this thesis studied experimental models of CKD, HF and CRS, without or with a therapeutic intervention. In this thesis we showed the close interaction between renal and cardiac function in health and disease, and their relation to microvascular endothelial function and structure. Using complex preclinical models of CKD and HF, surgical or metabolically induced, allowed us to increase knowledge about complicated processes such as dysfunction, hypertrophy, fibrosis and endothelial responses in kidney and heart. These processes were adversely affected by factors such as systemic hypertension, inflammation and oxidative stress. By non-pharmacological and pharmacological interventions, directly or indirectly targeting the microvascular endothelium, systemic factors, and thereby renal and cardiac function, could be positively affected. In the wide range of experimental models of CKD, HF and cardiorenal (metabolic) syndrome, renal and cardiac endothelial responses were not always consistent. This might be due to the severity of the hits (renal or cardiac) or their sequence. The results of this thesis suggest that proliferation of endothelial cells, and not the influx or incorporation of circulating cells, is an important factor for maintenance and restoration of microvascular endothelium. 1.Kingma JG, Simard D, Rouleau JR. Renocardiac syndromes: physiopathology and treatment stratagems. Canadian Journal of Kidney Health and Disease 2015; 2: 1-10.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".