Intradialytic Microvascular Tissue Perfusion Is Affected by Dialyzer Membrane Characteristics
Bibliographic record
Abstract
Background: During hemodialysis (HD), microvascular perfusion in tissues and organs is often compromised. The role of dialyzer clearance characteristics and general biocompatibility in this process is unknown. This study compared the effects of HD using either conventional high flux polysulfone (PSF) or newer mid-cut-off (MCO) dialyzers (characterized by an extended clearance spectrum into the middle molecule range) on microvascular perfusion in an established rat model of HD. We hypothesize that using different dialyzer membranes will result in different microvascular responses, affecting the quality of tissue blood flow. Methods: HD using in-house-designed mini-dialyzers was performed on male Wistar Kyoto rats, and microvascular perfusion in skeletal muscle was observed using intravital video microscopy. A carotid catheter was used for hemodynamic monitoring, while indwelling catheters in the left femoral artery and left femoral vein were connected to the mini-dialyzer. Blood samples were taken for biochemical analysis, a C5b-9 ELISA assay and hematocrit determination to ensure the procedure was euvolemic. Results: HD using PSF dialyzers significantly reduced microvascular muscle perfusion, unlike the MCO dialyzers (Fig 1). Blood pressure reduction was similar between the two groups, with a better conservation of the cardiac response in the MCO group. There were no differences in small solute clearance or circulating electrolyte levels. Analysis of [C5b-9] plasma levels shows significantly higher levels after a two-hour HD procedure with PSF dialyzers. Conclusion: Our analysis indicates that the separate effects of the different dialyzers on microvascular perfusion may be related to differences in complement activation. The exact mechanisms underlying these differences warrant further study. This small animal model allows us to preclinically evaluate membrane materials and dialyzer designs, facilitating the design of subsequent human studies. Funding: Other NIH Support - Lawson Health Research Fund I.R.F. #: 20-18; New Frontiers in Research Fund NFRF-E-2019-01285; MITACS IT24035., Commercial Support - Baxter International,
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) | 0.000 | 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".