Filoviruses: Recent Advances and Future Challenges
Bibliographic record
Abstract
Forty years have elapsed since the discovery of Marburg virus, and >30 years have elapsed since the discovery of Ebola virus. Filoviruses remained largely unknown until 1995, when Ebola virus re-emerged in Kikwit, Democratic Republic of the Congo (formerly Zaire). The fact that they are highly pathogenic for humans and nonhuman primates, to the point of being the subject of former biological weapons programs, makes Marburg and Ebola viruses feared pathogens worldwide today. Basic research on filovirus biology and pathogenesis has advanced over the past 10 years, leading to the development of reverse-genetics systems, potential treatment strategies, and vaccine candidates. However, because the animal reservoir is still unknown, the clinical course of human infections is not yet adequately understood, and knowledge concerning the immunologic response is incomplete, there is still a long way to go. In September of 2006, a global symposium on recent advances and future challenges in filovirus research was held in Winnipeg, Manitoba, Canada. The collection of articles in this special supplement of the Journal of Infectious Diseases focuses on Ebola and Marburg viruses, with an emphasis on work that was presented at the Winnipeg meeting. These articles bring us up to date in our understanding of the mechanisms of how filoviruses emerge and re-emerge, the molecular mechanisms of how these viruses replicate and cause disease, and, finally, on the recent progress that has been made in diagnosing filoviral hemorrhagic fever and in developing vaccines and therapies against these lethal microbes. From a public health perspective, among the most important advances are usable rapid and sensitive diagnostics and the development of promising treatment strategies and different vaccine platforms that have demonstrated efficacy in nonhuman primates against Ebola virus, Marburg virus, or both. Because outbreaks of Ebola and Marburg hemorrhagic fever are rare and, to date, restricted to parts of Africa, finding a suitable population in which to systematically evaluate the utility of any countermeasure presents a formidable obstacle and a huge challenge. Studies suggesting that bats are a potential reservoir host for filoviruses are particularly exciting, although this avenue of study needs further confirmation.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".