Current State and Evidence of Cellular Encapsulation Strategies in Type 1 Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it