Partitioning of model toxins to hydrophobically terminated DAB dendrimers
Bibliographic record
Abstract
Dendrimers are attractive in biological and biomedical applications due to the similarity in their molecular size to biologically relevant molecules and the large number of chain ends available functionalization. In the current work, we examined the potential of diamino butane (DAB) dendrimers functionalized with long alkyl chains as partitioning agents for hydrophobic toxins for use as a prefiltering stage in a bioartiticial liver. DAB dendrimers of various generations that had been previously fully modified with palmitoyl chloride were obtained. A study of the kinetics of partitioning of acetylsalicylic acid (ASA) suggested that while significant toxin removal occurred in 30 s, although a slight time dependent increase in removal was noted up to 60 minutes. The partitioning of 6 hydrophobic toxins from aqueous solution to the modified dendrimers in 30 minutes was examined. The results demonstrated that a number of factors, including the pKa of the toxin, its octanol water partitioning coefficient and molecular size contributed to the level of toxin removal. Toxin removal on a molar basis increased with increasing dendrimer generation for all toxins, with the modified G5 dendrimers partitioning 50-100 toxin molecules in most cases. Dendrimer modification with C4 alkyl chains rather than Cl5 chains significantly decreased toxin removal, although chains longer than C10 seemed to partition equal amounts of toxins. The results of the study demonstrate that water-soluble dendrimers modified with hydrophobic end groups may be useful for the removal of toxins from the blood in a prereaction step for a bioartificial liver, but that a better understanding of the molecular mechanisms of removal may be necessary before it is possible to predict the levels of toxin removal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".