Long‐Term Transport Study of Bioartificial Tubule Devices in the Development of a Bioartificial Kidney
Bibliographic record
Abstract
Introduction: A bioartificial kidney, which consists of a continuous hemofilter and a bioartificial tubule device using proximal tubular epithelial cells (LLC‐PK1), is desired to develop for preventing long‐term complications in hemodialysis patients. A bioartificial tubule device should function for a long duration in terms of the simplicity and the economy. Continuous hemofiltration with 10 L/day of filtrate could maintain plasma urea, creatinine and β2‐microglobulin in patients at low levels compared to those in standard hemodialysis patients. Methods: 6 bioartificial tubule devices, in which LLC‐PK1 cells were grown on the inner surfaces of hollow fiber capillaries (membrane area: 0.4 m2, 1600 fibers), were used to evaluate the transport ability of H2O, glucose and Na+, and leak rates of urea and creatinine for 2 weeks when the medium containing 50 mg/dL of urea and 5.0 mg/dL of creatinine was perfused inside of the cell‐attached membranes and another medium containing 2.5 g/dL of albumin was perfused outside of the membranes. Scanning electron micrograph of cross‐sectional findings of the hollow fibers was taken at 6, 10, and 14 days after formation of confluence. Results: By conversion into 1 m2 of membrane area, transport of H2O, glucose, and Na+ was 6266 ± 995 mL/day, 22832 ± 7240 mg/day, and 941.3 ± 180 mEq/day, respectively at 6 days after confluence. Leak rates of urea and creatinine across the cell‐attached membranes were 22 ± 6.1% and 19.2 ± 4.9 with albumin addition, whereas 13.1 ± 1.9% and 12.2 ± 1.6 without albumin addition. Transport capacity of these components and the leak rates had continued for 10–13 days, and decreased thereafter because of the formation of the multilayers. Bioartificial tubule devices with membrane area 1.0 m2 can reach the targeted amounts of H2O, glucose, and Na+ transports when 6 L of 10 L/day of hemofiltrate has to be regenerated, substituting 4 L with meal and drinks.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".