Fighting Diabetes: Lessons from Xenotransplantation and Nanomedicine
Bibliographic record
Abstract
Increasing incidence of diabetes and shortage of specific beta cells, hormonal switches like that of delta and PP cells of the islets for transplantation, have forced the scientific community to look for alternative sources through xenotransplantation and nanomedicine. The Edmonton protocol of islet transplantation has shown proof of principle of long term survival of islets in type I diabetic patients, leading to insulin prick free life. Copious volume of literature exists on the use of mammalian islets, especially of porcine origin for diabetes reversal in humans with follow-up studies upto 10 yrs. There is an obvious lack of pre-clinical results and data in the pig-to-primate model. The difficulty is in reproducing regularly the successful porcine islet isolation. Although some of the parameters have been taken, making xenotransplantation an attractive and viable alternative therapy. However, scarcity of islets is the main hurdle in the success of islet transplantation programs. Since the islet cell receptor and the insulin molecule have remained conserved throughout the evolution of vertebrates, we reviewed islet studies from other vertebrates especially, jawless fish, cartilaginous as well as bony fishes and chick islets. The similarities of chick B islets with human islets in terms of Streptozotocin insensitivity and retention of glucose responsiveness by new born chick islets tempted us to hypothesize the use of fish and chick islets as alternative sources for transplantation to reverse experimental diabetes. Since ontogeny recapitulates phylogeny, the islets recovered from lower vertebrates are likely to be less immunogenic and may open possibility of using them without immunosuppression. Complementing xenotransplantation, nanotechnology offers an excellent module for addressing the diabetes problem from detection and treatment points of view. This review attempts to throw some light on both these approaches for an effective management and cure of diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".