Hemorrhagic Fevers: Endothelial Cells and Ebola-Virus Hemorrhagic Fever
Bibliographic record
Abstract
Viral hemorrhagic fever (VHF) is a severe multiorgan disease with strong immune involvement and diffuse vascular dysregulation, particularly of the vascular endothelium. Several families of RNA viruses are regularly associated with a VHF syndrome in humans: Arenaviridae (Lassa virus, Machupo virus, Junin virus, Guanarito virus, and Sabia virus), Bunyaviridae (Rift Valley fever virus, Crimean-Congo hemorrhagic fever virus, hantaviruses), Flaviviridae (Yellow fever virus, Dengue virus, Omsk hemorrhagic fever virus, and Kyasanur forest disease virus), and Filoviridae (Marburg virus and Ebola virus). The clinical manifestations of VHF vary and are dependent on the causative agent (see CDC homepage http://www.cdc.gov). However, some common clinical features include fever, various degrees of vascular dysregulation with bleeding tendency and shock development, and the vascular endothelium seems to be affected in most cases (1–3). Some of the VHF-causing pathogens target the endothelium directly, whereas others induce primarily indirect alterations through proinflammatory mediators released from infected target cells (e.g., monocytes/macrophages). Marburg (MARV) and Ebola viruses (EBOV) cause the most severe form of VHF and, thus, serve as important model pathogens for studying the pathogenesis and management of VHFs. Filoviruses, as well as some other hemorrhagic fever (HF) viruses, are biological safety level 4 (BSL4) agents, which somewhat complicates investigations. Filoviruses seem to target both the vascular system and the immune system, leading to the opinion that filovirus HF fever is a vascular disease as well as an immune syndrome (2–5). Although our understanding of the molecular mechanisms of VHF pathogenesis is still limited, some important scientific achievements have been made in the past decade.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".