Patient and Mouse Antibodies against Dengue Virus Nonstructural Protein 1 Cross-React with Platelets and Cause Their Dysfunction or Depletion
Bibliographic record
Abstract
Thrombocytopenia is a clinical manifestation in dengue virus (DV) infection, yet its pathogenic mechanisms are unresolved. We previously showed that dengue patient sera contained antibodies cross-reactive with platelets. In this study, we demonstrated that the anti-platelet activity of dengue patient sera was due to the antibodies against DV nonstructural protein 1 (NS1). Studies using DV-infected or recombinant NS1-immunized mouse sera showed that anti-NS1 antibodies cross-reacted with human platelets. The platelet-binding activity of dengue patient sera or anti-NS1 antibodies was inhibited by treatment of platelets with anti-NS1 or patient sera. Further investigation showed that anti-NS1 antibodies were able to inhibit platelet aggregation and cause platelet lysis in the presence of complement. The platelet-binding activity and the induction of platelet lysis mediated by dengue patient sera or anti-NS1 antibodies were increased when platelets were activated by ADP or thrombin. Taken together, anti-NS1 antibodies account for the cross-reactivity with platelets and cause platelet dysfunction or depletion, which may be involved in the pathogenesis of dengue diseases.
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 | 0.000 |
| 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 | 0.000 |
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".