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 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".