Successful prenatal therapy for anti-CD36-mediated severe FNAIT by deglycosylated antibodies in a novel murine model
Bibliographic record
Abstract
Recent studies have shown that maternal anti-CD36 antibodies represent a frequent cause of fetal/neonatal alloimmune thrombocytopenia (FNAIT) in Asian and African populations. However, little is known about the pathomechanism and antenatal treatment of anti-CD36-mediated FNAIT. Here, we established a novel animal model to examine the clinical features of pups from immunized Cd36-/- female mice after breeding with wild-type male mice. Mild thrombocytopenia was observed, but high pup mortality was also documented (40.26%). Administration of intravenous immunoglobulin (IVIG) (1 g/kg) on days 7, 12, and 17 to immunized Cd36-/- mothers after breeding reduced fetal death (12.70%). However, delaying the IVIG administration series on days 10, 15, and 20 did not reduce fetal death (40.00%). In contrast, injection of deglycosylated anti-CD36 (deg-anti-CD36) polyclonal antibodies (5 mg/kg) on days 10, 15, and 20 significantly reduced fetal death (5.26%). Subsequently, monoclonal antibodies (mAbs) against mouse CD36 were developed, and one clone producing high-affinity anti-CD36 (termed 32-106) effectively inhibited maternal antibody binding and was therefore selected. Using the same approach of deg-anti-CD36, the administration of deg-32-106 significantly reduced fetal death (2.17%). Furthermore, immunized Cd36-/- mothers exhibited placental deficiency. Accordingly, maternal anti-CD36 antibodies inhibited angiogenesis of placenta endothelial cells, which could be restored by deg-32-106. In summary, maternal anti-CD36 antibodies caused a high frequency of fetal death in our animal model, associated with placental dysfunction. This deleterious effect could be diminished by the antenatal administration of IVIG and deg-mAb 32-106. Interestingly, treatment with deg-32-106 seems more beneficial considering the lower dose, later start of treatment, and therapy success.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".