马尔堡病毒豚鼠模型的建立及其特征研究
Bibliographic record
Abstract
The Angolan strain of Marburg virus (MARV/Ang) can cause lethal disease in humans with a case fatality rate of up to 90%, but infection of immunocompetent rodents do not result in any observable symptoms. Our previous work includes the development and characterization of a MARV/Ang variant that can cause lethal disease in mice (MARV/Ang-MA), with the aim of using this tool to screen for promising prophylactic and therapeutic candidates. An intermediate animal model is needed to confirm any findings from mice studies before testing in the gold-standard non-human primate (NHP) model. In this study, we serially passaged the clinical isolate of MARV/Ang in the livers and spleens of guinea pigs until a variant emerged that causes 100% lethality in guinea pigs (MARV/Ang-GA). Animals infected with MARV/Ang-GA showed signs of filovirus infection including lymphocytopenia, thrombocytopenia, and high viremia leading to spread to major organs, including the liver, spleen, lungs, and kidneys. The MARV/Ang-GA guinea pigs died between 7–9 days after infection, and the LD50 was calculated to be 1.1×10–1 TCID50 (median tissue culture infective dose). Mutations in MARV/Ang-GA were identified and compared to sequences of known rodent-adapted MARV/Ang variants, which may benefit future studies characterizing important host adaptation sites in the MARV/Ang viral genome.
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 | 0.000 | 0.000 |
| 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".