Early Treatment of NOD Mice With B7-H4 Reduces the Incidence of Autoimmune Diabetes
Bibliographic record
Abstract
OBJECTIVE: Autoimmune diabetes is a T cell-mediated disease in which insulin-producing β-cells are destroyed. Autoreactive T cells play a central role in mediating β-cell destruction. B7-H4 is a negative cosignaling molecule that downregulates T-cell responses. In this study, we aim to determine the role of B7-H4 on regulation of β-cell-specific autoimmune responses. RESEARCH DESIGN AND METHODS: Prediabetic (aged 3 weeks) female NOD mice (group 1, n = 21) were treated with intraperitoneal injections of B7-H4.Ig at 7.5 mg/kg, with the same amount of mouse IgG (group 2, n = 24), or with no protein injections (group 3, n = 24), every 3 days for 12 weeks. RESULTS: B7-H4.Ig reduced the incidence of autoimmune diabetes, compared with the control groups (diabetic mice 28.6% of group 1, 66.7% of group 2 [P = 0.0081], and 70.8% of group 3 [group 1 vs. 3, P = 0.0035]). Histological analysis revealed that B7-H4 treatment did not block islet infiltration but rather suppressed further infiltrates after 9 weeks of treatment (group 1 vs. 2, P = 0.0003). B7-H4 treatment also reduced T-cell proliferation in response to GAD65 stimulation ex vivo. The reduction of diabetes is not due to inhibition of activated T cells in the periphery but rather to a transient increase of Foxp3(+) CD4(+) T-cell population at one week posttreatment (12.88 ± 1.29 vs. 11.58 ± 1.46%; n = 8; P = 0.03). CONCLUSIONS: Our data demonstrate the protective role of B7-H4 in the development of autoimmune diabetes, suggesting a potential means of preventing type 1 diabetes by targeting the B7-H4 pathway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".