Pathway of Toll-Like Receptor 7/B Cell Activating Factor/B Cell Activating Factor Receptor Plays a Role in Immune Thrombocytopenia In Vivo
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by anti-platelet autoantibody-mediated platelet destruction. Antigen-presenting cell (APC) dysfunction is considered to play crucial roles in ITP. However, how APC affects autoreactive B cells in ITP is still unknown. Using a mouse model of immune thrombocytopenia, we demonstrated an increase in levels of TLR7 in splenic mononuclear cells (SMCs). Using both TLR7 agonist and TLR7 silencing lentivirus, we found stimulation of TLR7 decreased platelet counts and increased levels of platelet-associated IgG (PAIgG) in ITP mice, which correlates TLR7 with platelet destruction by autoantibodies. Levels of serum BAFF increased significantly in ITP mice and stimulation of TLR7 promoted secretion of BAFF. Among the three BAFF receptors, only BAFF receptor (BAFF-R) increased in ITP mice. However, activation of TLR7 showed no effect on the expression of BAFF receptors. These findings indicate that upregulation of TLR7 may augment BAFF secretion by APC and through ligation of BAFF-R promote autoreactive B cell survival and thus anti-platelet autoantibody production. The pathway of TLR7/BAFF/BAFF-R provides us with an explanation of how activation of APC affects autoantibody production by B cells in ITP and thus might provide a reasonable therapeutic strategy for ITP.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".