Present status and future perspectives on the development of bioartificial kidneys for the treatment of acute and chronic renal failure patients
Bibliographic record
Abstract
A bioartificial renal tubule device (BTD) consisting of a hollow-fiber module and human proximal tubular epithelial cells has been completed technically by Humes and colleagues and a few other groups. Humes and colleagues developed BTD, treated acute kidney injury patients with multiorgan failure by continuous hemofiltration (CHF) in conjunction with BTD, and reported a significantly higher survival rate than that by CHF with BTD without cells in the Food and Drug Administration phase IIa trial. However, BTD has never been approved by the US Government, as the CHF+BTD treatment did not show a significant difference from the control group in the phase IIb trial. Human proximal tubular epithelial cells were confirmed to be overgrown on artificial membrane, which resulted in the inhibition of active transports and the metabolism of essential substances. Function of the BTD could be maintained in a U0126-contained medium, even if the BTD had to have been waited by a new acute kidney injury patient for several weeks. For wearable kidneys, heparin-covalently bound membrane or methacryloyloxyethyl phosphorylcholine (MPC) polymer-coated membranes are candidates for antithrombogenic hemofilters, while endothelial progenitor cells from a cord blood, CD133(+) cells-attached hemofilter in which the permeability of the cells was enhanced by the enlarged diameter of fenestrae by treating with cytochalasin B are another candidate. The MPC blend membrane containing 1% of the MPC polymer in polysulfone was developed as a BTD module. MPC was 7 times larger at the sponge layer than at the skin layer of the membrane, resulting in hemocompatibility at the sponge layer and cytocompatibility at the skin layer.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".