Artificial Cells for Bioencapsulation of Cells and Genetically Engineered E. coli: For Cell Therapy, Gene Therapy, and Removal of Urea and Ammonia
Bibliographic record
Abstract
Enzymes, proteins, cells, microorganisms, adsorbents, magnetic materials and other biologically active materials can be encapsulated within artificial cells with artificial polymer membranes ( 1 – 9 ). This type of artificial cells are not liposomes. The artificial cells protect the encapsulated biological materials from immunological reactions. At the same time, the enclosed materials continue to act in the immunologically isolated environment. The use of artificial cells containing adsorbent for hemoperfusion is a routine procedure used in patients for the removal of toxic substances or unwanted metabolites. Clinical studies are ongoing in patients using artificial cells as red blood cell substitutes, for enzyme therapy, and for delivery of biotechnological agents. Chang first developed a drop method for the bioencapsulation of cells and proposed its use in cell therapy ( 4 , 5 ). “Micro encapsulation of intact cells … the enclosed material might be protected from destruction and from participation in immunological processes, whereas the enclosing membrane would be permeable to small molecules of specific cellular product which could then enter the general extracellular compartment of the recipient. … The situation is comparable to that of a graft placed in an immunologically favorable site” ( 4 ). This was not explored by others for sometime. However, with increasing interests in biotechnology, many groups are now actively studying this for bioencapsulation of cells or genetically engineered microorganisms in cell and gene therapy. This chapter describes only two examples: bioencapsulation of hepatocytes or genetically engineered microorganisms. These examples are used to demonstrate the methods of preparations and their potential applications in cell and gene therapy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".