Continuously functioning artificial nephron system: The promise of nanotechnology
Bibliographic record
Abstract
Nearly 900,000 patients worldwide have end-stage renal disease and require dialysis or kidney transplantation. Despite the availability of these forms of renal replacement therapy for nearly four decades, mortality and morbidity are high and patients often have a poor quality of life. We have developed a human nephron filter (HNF) utilizing nanotechnology that would eventually make feasible a continuously functioning, wearable or implantable artificial kidney. The device consists of two membranes operating in series within one device cartridge. The first membrane mimics the function of the glomerulus, using convective transport to generate a plasma ultrafiltrate containing all solutes approaching the molecular weight of albumin. The second membrane mimics the function of the renal tubules, selectively reclaiming designated solutes to maintain body homeostasis. No dialysis solution is used in this device. The HNF has been computer-modeled, and operating 12 hr per day, 7 days per week the HNF provides the equivalent of 30 mL/min glomerular filtration rate (compared to half that amount for conventional thrice-weekly hemodialysis). Animal studies should begin in the next 1 to 2 years, and clinical trials would then follow 1 to 2 years subsequent. The HNF system, by eliminating dialysate and utilizing a novel membrane system created through applied nanotechnology, represents a breakthrough in renal replacement therapy based on the functioning of native kidneys. The enhanced solute removal and wearable design should substantially improve patient outcomes and quality of life.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".