Human leukocyte antigen-G upregulates immunoglobulin-like transcripts and corrects dysfunction of immune cells in immune thrombocytopenia
Bibliographic record
Abstract
Human leukocyte antigen-G (HLA-G) is a non-classical major histocompatibility complex class I antigen with potent immune-inhibitory function. HLA-G benefit patients in allotransplantation and autoimmune diseases by interacting with its receptors, immunoglobulinlike transcripts. Here we observed significantly less HLA-G in plasma from immune thrombocytopenia (ITP) patients positive for anti-platelet autoantibodies compared with autoantibodies-negative patients or healthy controls, while we found that HLA-G is positively correlated with platelet counts in both patients and healthy controls. We also found less membranebound HLA-G and immunoglobulin-like transcripts on CD4+ and CD14+ cells in patients. Recombinant HLA-G upregulated immunoglobulin-like transcript 2 expression on CD4+ and immunoglobulin-like transcript 4 on CD14+ cells. HLA-G upregulated IL-4 and IL-10, and downregulated tumor necrosis factor-a, IL-12 and IL-17 secreted by patient peripheral blood mononuclear cells, suggesting a stimulation of Th2 differentiation and downregulation of Th1 and Th17 immune response. HLA-G-modulated dendritic cells from ITP patients showed decreased expression of CD80 and CD86, and suppressed CD4+ T-cell proliferation compared to unmodulated cells. Moreover, HLA-G-modulated cells from patients induced less platelet apoptosis. HLA-G administration also significantly alleviated thrombocytopenia in a murine model of ITP. In conclusion, our data demonstrated that impaired expression of HLA-G and immunoglobulin-like transcripts is involved in the pathogenesis of ITP; recombinant HLA-G can correct this abnormality via upregulation of immunoglobulin-like transcripts, indicating that HLA-G can be a diagnostic marker and a therapeutic option for ITP.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".