Potentials and limitations of microorganisms as renal failure biotherapeutics
Bibliographic record
Abstract
Poonam Jain, Sapna Shah, Razek Coussa, Satya PrakashBiomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering and Physiology, Artificial Cells and Organs Research Centre, Faculty of Medicine, McGill University, Montreal, Québec, CanadaAbstract: Renal insufficiency leads to uremia, a complicated syndrome. It thus becomes vital to reduce waste metabolites and regulate water and electrolytes in kidney failure. The most common treatment of this disease is either dialysis or transplantation. Although these treatments are very effective, they are extremely costly. Recently artificial cells, microencapsulated live bacterial cells, and other cells have been studied to manage renal failure metabolic wastes. The procedure for microencapsulation of biologically active material is well documented and offers many biomedical applications. Microencapsulated bacteria have been documented to efficiently remove urea and several uremic markers such as ammonia, creatinine, uric acid, phosphate, potassium, magnesium, sodium, and chloride. These bacteria also have further potential as biotherapeutic agents because they can be engineered to remove selected unwanted waste. This application has enormous potential for removal of waste metabolites and electrolytes in renal failure as well as other diseases such as liver failure, phenylketonuria, and Crohn’s disease, to name a few. This paper discusses the various options available to date to manage renal failure metabolites and focuses on the potential of using encapsulated live cells as biotherapeutic agents to control renal failure waste metabolites and electrolytes. Keywords: renal failure, microencapsulation, artificial cells, oral administration, bacterial cells, metabolites, electrolytes, polymeric membrane
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".