Advances in the Treatment of Enterovirus-D68 and Rhinovirus Respiratory Infections
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Enterovirus-D68 (EV-D68) and rhinoviruses are major contributors to respiratory illnesses in children, presenting a spectrum of clinical manifestations ranging from asymptomatic cases to severe lower respiratory tract infections. No specific antiviral treatments are currently approved for these viruses. METHOD: We conducted a comprehensive literature review of antiviral agents investigated for EV-D68 and rhinovirus infections. RESULTS: Several antiviral candidates are under investigation, each targeting distinct stages of the viral replicative cycle. Capsid-binding agents and monoclonal antibodies prevent viral attachment by blocking receptor-virus interactions. Inhibitors of viral replication proteins disrupt polyprotein processing and replication organelle biogenesis by targeting non-structural viral proteins. Host factor inhibitors impair viral attachment, replication organelle formation, or RNA replication by interfering with critical host pathways. CONCLUSIONS: While no specific antivirals are yet approved for EV-D68 and rhinovirus infections, emerging therapeutic candidates offer potential avenues for treatment. Continued preclinical and clinical investigation will be essential to validate these approaches and expand the available options for affected patients.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".