Prolonged renal allograft survival by donor interleukin-6 deficiency: association with decreased alloantibodies and increased intragraft T regulatory cells
Bibliographic record
Abstract
Both humoral and cellular immune responses are involved in renal allograft rejection. Interleukin (IL)-6 is a regulatory cytokine for both B and Foxp3 (forkhead box P3)-expressing regulatory T (Treg) cells. This study was designed to investigate the impact of donor IL-6 production on renal allograft survival. Donor kidneys from IL-6 knockout (KO) vs. wild-type (WT) C57BL/6 mice (H-2(b)) were orthotopically transplanted to nephrotomized BALB/c mice (H-2(d)). Alloantibodies and Treg cells were examined by fluorescence-activated cell sorting analysis. Graft survival was determined by the time to graft failure. Here, we showed that a deficiency in IL-6 expression in donor kidneys significantly prolonged renal allograft survival compared with WT controls. IL-6 protein was upregulated in renal tubules and endothelium of renal allografts following rejection, which correlated with an increase in serum IL-6 compared with that in those receiving KO grafts or naive controls. The absence of graft-producing IL-6 or lower levels of serum IL-6 in the recipients receiving IL-6 KO allografts was associated with decreased circulating anti-graft alloantibodies and increased the percentage of intragraft CD4(+)CD25(+)Foxp3(+) Treg cells compared with those with WT allografts. In conclusion, the lack of graft-producing IL-6 significantly prolongs renal allograft survival, which is associated with reduced alloantibody production and/or increased intragraft Treg cell population, implying that targeting donor IL-6 may effectively prevent both humoral and cellular rejection of kidney transplants.
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.
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 | 0.001 |
| 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".