Detoxifying Capacity and Kinetics of the Molecular Adsorbent Recycling System: Contribution of the Different Filters Inbuilt
Bibliographic record
Abstract
Extracorporeal liver support therapies have been used for several decades as a bridging therapy prior to liver transplantation or as an addendum to standard medical therapy. The molecular adsorbent recycling system (MARS) represents a cell-free, extracorporeal, liver assistance method for the removal of both albumin-bound and water-soluble endogenous toxins. The aim of the present study was to evaluate the short-and long-term removal capacity and selectivity of the different inbuilt dialysers and adsorption columns (uncoated charcoal, anion exchanger resin). Levels of endogenous toxins and parameters of hepatic synthesis and necrosis were therefore monitored before, during, and after the MARS treatment phase in 10 patients. Moreover, blood and dialysate clearances of urea nitrogen, creatinine, bilirubin and bile acids were determined during a single treatment. The significant increasing time course of total bilirubin blood levels before the start of the treatment could be stopped and reversed in a significant decreasing time course (Linear Mixed Models, P < 0.05). The removal rate of urea nitrogen, bilirubin, and bile acids during a single treatment amounted to 55.5 ± 4.0%, 28.3 ± 3.9%, and 55.4 ± 4.0%(mean ± SEM), respectively. Bile acids and bilirubin were mainly removed by the activated charcoal and anion exchanger column, respectively. The efficacy of removal of albumin-bound toxins sharply declined early after initiation of the treatment to become negligible after 6 h. In conclusion, both albumin-bound and water-soluble toxins are adequately removed by the MARS. Our data suggest that the rate and efficacy of removal of albumin-bound toxins is related to both the strength of the albumin binding and the saturation of the adsorption columns.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".