Bibliographic record
Abstract
Abstract Over the past two decades, microencapsulation research has made great strides in developing approaches for the controlled release of therapeutic agents, targeted delivery of drugs, bacterial cells, mammalian cells, DNA and other nucleic acids, proteins, and so on to the host. Microencapsulation is a process whereby biologically active materials, such as tissue, cells, or cellular constituents, are enclosed within microscopic, semipermeable polymeric membranes which allows for bidirectional diffusion necessary for the entry of nutrients and oxygen and the exit of therapeutic protein products and cellular metabolic waste materials. In addition, the membrane is protective against larger molecules such as antibodies, white blood cells, or tryptic enzymes. The concept of microencapsulation was officially introduced in the 1960s by T. M. S. Chang, who coined the term “artificial cell” to describe the technology. The field has since broadened with the development of novel microencapsulation devices, improved membrane parameters and more suitable cell lines. Consequently, microencapsulation is currently being tested for the treatment of a wide variety of disorders such as kidney and liver failure, diabetes mellitus, anemia, dwarfism, hemophilia, and central nervous system insufficiencies. Although transplantation remains the primary mechanism in which cellular microcapsules are introduced, oral administration through the gastrointestinal tract has been successfully applied. In discussing microencapsulation technology, a major distinction is in the use of artificial cells that now range from macro‐dimensions, to micron‐dimensions, to nano‐dimensions, and to molecular dimensions. Details of the various microcapsule membranes and microencapsulation methods for encapsulation of live mammalian cells, bacterial cells, drugs and other bioactive molecules are described. Further, the uses of microencapsulation technology in pharmaceutical, biotechnological, biomedical and clinical applications including current challenges and future prospects are illustrated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".