Immunoglobulin isotypes and functional anti-FVIII antibodies in response to FVIII treatment in Balb/c and C57BL/6 haemophilia A mice
Bibliographic record
Abstract
Previous studies have demonstrated that genetic factors play an important role in determining the likelihood of formation of anti-factor VIII (FVIII) antibodies in haemophilia A patients. We were interested in characterizing the spectrum of FVIII antibody formation and the primary and secondary immune responses after FVIII administration in two different exon 16-disrupted haemophilia A mouse strains, Balb/c and C57BL/6. Balb/c and C57BL/6 E16 haemophilia A mice were used in all experiments. Total FVIII antibodies and FVIII inhibitors were measured using ELISA and Bethesda assays respectively. T- and B-cell cytokines were quantified using ELISA and flow cytometry. FVIII antibodies, but not functional inhibitors were detectable 1 week after the first FVIII treatment in both strains. These antibodies mainly belonged to the IgM and IgA isotypes. After the fourth FVIII treatment, neutralizing anti-FVIII antibodies were detected in both mouse strains: Balb/c (mean inhibitory titer 58 BU) and C57BL/6 (mean inhibitory titer 82 BU). IgG1 levels were similar in both strains but the IgG2A and IgG2B subclasses were higher in C57BL/6 mice. The results of intracellular cytokine staining of T cells indicated that the FVIII-treated C57BL/6 mice produced more IL10 and Th1 cytokines than the FVIII-treated Balb/c mice. These studies show that C57BL/6 mice develop a stronger immune response towards FVIII than Balb/c mice. We propose that the enhanced Th1 and IL10 cytokine micro-environment induced in C57BL/6 mice is responsible for this difference. Therefore, genetic strain-dependent differences must be considered when evaluating immunological outcomes in mouse models of haemophilia A.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".