Emerging Therapeutic Approaches to Combat the Pandemicity of the Deadly Ebola Virus
Bibliographic record
Abstract
Background: Since the identification of Ebola Virus (EBOV) in 1976, significant filovirus research has focused on developing antiviral therapies. However, despite promising vaccine candidates, no licensed prophylactics currently exist for preventing or treating filovirus infections. Pathogenesis: The Ebola genome encodes only seven genes, which mediate the entry, replication, and egress of the virus from the host cell. Bats have been identified as a reservoir for Ebola viruses but it remains unclear if transmission to an end host involves intermediate hosts. Diagnosis: Diagnosis within a few days after symptoms begin involves antigen-capture enzyme-linked immunosorbent assay (ELISA) testing, IgM ELISA, polymerase chain reaction (PCR) and Virus isolation. Clinical pictures: Initial signs and symptoms are nonspecific and may include fever, chills, myalgias, and malaise. Sufferers experience nausea, vomiting, internal bleeding and organ failure before they die. Treatment: There are no approved treatments or vaccines available for Ebola virus disease (EVD) until today; however, there are a bunch of therapeutic approaches on the track which could have the real impact on control and prevention of this global threat. Among these, the one announced by the WHO opens some ones eyebrow and gives the real glimmer of hope to tackle EVD. The two “front running” vaccines on the track are cAd3-ZEBOV, a chimpanzee derived adenovirus vaccine developed by GlaxoSmithKline in conjunction with the US National Institute of Allergies and Infectious Diseases, and rVSV-ZEBOV, developed by the Public Health Agency of Canada and now licensed to a US company called New Link. Conclusions: Many promising vaccines are moving through pre-clinical or clinical trials, but mass immunization is unlikely due to the localized and sporadic nature of EBOV infections. Post-exposure interventions are therefore necessary for the treatment of cases as they occur.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".