Hair Follicle Dermal Sheath Derived Cells Improve Islet Allograft Survival without Systemic Immunosuppression
Bibliographic record
Abstract
Immunosuppressive drugs successfully prevent rejection of islet allografts in the treatment of type I diabetes. However, the drugs also suppress systemic immunity increasing the risk of opportunistic infection and cancer development in allograft recipients. In this study, we investigated a new treatment for autoimmune diabetes using naturally immune privileged, hair follicle derived, autologous cells to provide localized immune protection of islet allotransplants. Islets from Balb/c mouse donors were cotransplanted with syngeneic hair follicle dermal sheath cup cells (DSCC, group 1) or fibroblasts (FB, group 2) under the kidney capsule of immune-competent, streptozotocin induced, diabetic C57BL/6 recipients. Group 1 allografts survived significantly longer than group 2 (32.2 ± 12.2 versus 14.1 ± 3.3 days, P < 0.001) without administration of any systemic immunosuppressive agents. DSCC reduced T cell activation in the renal lymph node, prevented graft infiltrates, modulated inflammatory chemokine and cytokine profiles, and preserved better beta cell function in the islet allografts, but no systemic immunosuppression was observed. In summary, DSCC prolong islet allograft survival without systemic immunosuppression by local modulation of alloimmune responses, enhancing of beta cell survival, and promoting of graft revascularization. This novel finding demonstrates the capacity of easily accessible hair follicle cells to be used as local immunosuppression agents in islet transplantation.
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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) | 0.000 | 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) | 0.001 | 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 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".