Current State and Evidence of Cellular Encapsulation Strategies in Type 1 Diabetes
Bibliographic record
Abstract
Islet cell replacement therapies represent an effective way to restore physiologic glycemic control in patients with type 1 diabetes (T1DM) and severe hypoglycemia. Despite being able to provide long-term insulin independence, patients still require lifelong immunosuppression, which has myriad detrimental effects including an increased risk for opportunistic infections and some types of cancer. This vital issue precludes widespread application of these therapies as a true cure for T1DM. Encapsulation of islets into immunoisolating/immunoprotective devices provides the potential of abrogating the requisite for lifelong immunosuppression. The field of cellular encapsulation lies at a complex intersection between the areas of chemistry, physics, bioengineering, cell biology, immunology, and clinical medicine. In diabetes, cellular encapsulation has existed for nearly 50 years, nevertheless, a resurgence of interest in the field has been motivated by promising results in small- and large-animal models. Recent studies have demonstrated that long-term diabetes reversal without immunosuppression is indeed routinely achievable. Future researchers interested in exploring cellular encapsulation strategies will require a clear understanding of the basic theoretical and practical principles, guiding this rapidly expanding field. This article will provide essential considerations concerning the physicochemical properties of the most commonly used biomaterials, relevant aspects of the immune response to bioencapsulation, current encapsulation strategies, potential implantation sites for encapsulated cell therapies and, finally, a comprehensive review on the current state of clinical translation. © 2020 American Physiological Society. Compr Physiol 10:839-878, 2020.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".