Artificial Cell Therapy: New Strategies for the Therapeutic Delivery of Live Bacteria
Bibliographic record
Abstract
There has been rapid growth in research regarding the use of live bacterial cells for therapeutic purposes. The recognition that these cells can be genetically engineered to synthesize products that have therapeutic potential has generated considerable interest and excitement among clinicians and health professionals. It is expected that a wide range of disease modifying substrates such as enzymes, hormones, antibodies, vaccines, and other genetic products will be used successfully and will impact upon health care substantially. However, a major limitation in the use of these bacterial cells is the complexity of delivering them to the correct target tissues. Oral delivery of live cells, lyophilized cells, and immobilized cells has been attempted but with limited success. Primarily, this is because bacterial cells are incapable of surviving passage through the gastrointestinal tract. In many occasions, when given orally, these cells have been found to provoke immunogenic responses that are undesirable. Recent studies show that these problems can be overcome by delivering live bacterial cells, such as genetically engineered cells, using artificial cell microcapsules. This review summarizes recent advances in the therapeutic use of live bacterial cells for therapy, discusses the principles of using artificial cells for the oral delivery of bacterial cells, outlines methods for preparing suitable artificial cells for this purpose, addresses potentials and limitations for their application in therapy, and provides insight for the future direction of this emergent and highly prospective technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".