Therapeutic efficacy of humanized monoclonal antibodies targeting dengue virus nonstructural protein 1 in the mouse model
Bibliographic record
Abstract
Dengue virus (DENV) which infects about 390 million people per year in tropical and subtropical areas manifests various disease symptoms, ranging from fever to life-threatening hemorrhage and even shock. To date, there is still no effective treatment for DENV disease, but only supportive care. DENV nonstructural protein 1 (NS1) has been shown to play a key role in disease pathogenesis. Recent studies have shown that anti-DENV NS1 antibody can provide disease protection by blocking the DENV-induced disruption of endothelial integrity. We previously demonstrated that anti-NS1 monoclonal antibody (mAb) protected mice from all four serotypes of DENV challenge. Here, we generated humanized anti-NS1 mAbs and transferred them to mice after DENV infection. The results showed that DENV-induced prolonged bleeding time and skin hemorrhage were reduced, even several days after DENV challenge. Mechanistic studies showed the ability of humanized anti-NS1 mAbs to inhibit NS1-induced vascular hyperpermeability and to elicit Fcγ-dependent complement-mediated cytolysis as well as antibody-dependent cellular cytotoxicity of cells infected with four serotypes of DENV. These results highlight humanized anti-NS1 mAb as a potential therapeutic agent in DENV infection.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".