Immunopathology of RSV infection: prospects for developing vaccines without this complication
Bibliographic record
Abstract
Respiratory syncytial virus is the most important cause of lower respiratory tract infection in infants and young children. RSV clinical disease varies from rhinitis and otitis media to bronchiolitis and pneumonia. An increased incidence of asthma later in life has been associated with the more severe lower respiratory tract infections. Despite its importance as a pathogen, there is no licensed vaccine against RSV. This is due to a number of factors complicating the development of an effective and safe vaccine. The immunity to natural RSV infection is incomplete as re-infections occur in all age groups, which makes it challenging to design a protective vaccine. Second, the primary target population is the newborn infant, which has a relatively immature immune system and maternal antibodies that can interfere with vaccination. Finally, some vaccines have resulted in a predisposition for exacerbated pulmonary disease in infants, which was attributed to an imbalanced Th2-biased immune response, although the exact cause has not been elucidated. This makes it difficult to proceed with vaccine testing in infants. It is likely that an effective and safe vaccine needs to elicit a balanced immune response, including RSV-specific neutralising antibodies, CD8 T-cells, Th1/Th2 CD4 T-cells and preferably secretory IgA. Subunit vaccines formulated with appropriate adjuvants may be adequate for previously exposed individuals. However, intranasally delivered genetically engineered attenuated or vectored vaccines are currently most promising for newborns, as they are expected to induce a balanced immune response similar to that elicited to natural infection and not be subject to interference from maternal antibodies. Maternal vaccination may be the optimal strategy to protect the very young infants.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".