Notice bibliographique
Résumé
Recent papers in the Journal provide tangible avenues for COVID-19 vaccine production as immunoreactive epitopes are brought to the forefront in these and many other emerging studies.1, 2 The development of a consistent predictable animal model of COVID-19 infection is evidently also a welcome event for preliminary antiviral and vaccine assessments and surely brings us to another level of progression.3 The hamster model is not new to coronavirology but has the potential to provide a more stable and predictable model of infection in contrast to the murine models.4 Pulmonary infection, whether in the context of chemotherapy or vaccine trials, can be easily graded with a histopathological scoring method previously defined in another context and shown to be useful for small experimental animal groups.5 The latter has been applied to experimental endeavor with severe acute respiratory syndrome coronavirus (SARS-CoV).6 Initial enthusiasm to assess whole virus vaccines prepared in a variety of options have historically been followed by focused work on component vaccines. Regardless of the vaccine format, however, one major concern is that vaccination for some viruses and bacteria can be associated with adverse early recall responses after subsequent infections.7, 8 Such a phenomenon was also postulated in early human vaccine trials after parenteral vaccination with Mycoplasma pneumoniae and respiratory syncytial virus.9, 10 Hyperaccentuated immune responses after vaccination with SARS-CoV was previously recognized in murine models.6, 11 Although antibody-dependent enhancement as an explanation of such post-vaccine pathology has been postulated by some for several vaccines, a confirmation of the latter and a workable solution have at times been elusive.12-14 Nevertheless, the critical lesson in vaccine assessment in animal models for COVID-19 is that the review of post-vaccine disease and prevention should therefore include an assessment of both the early and late lung in whichever model so adopted.6-8, 11 The current yet preliminary understanding of COVID-19 genome and structure offers several candidates for vaccination.1, 2, 15 In any such assessments, the examination of systemic humoral or cell-mediated responses to the vaccine are often sought, and thereafter, their association with vaccination outcomes is determined. One lesser sought method for looking at protective antibody at least at the entry-level is to examine the mucosal immune response postinfection that develops in lactating females.16 Immunoblotting for secretory Immunoglobulin A (IgA) (rather than IgA generally) with breast milk samples from those previously documented to have had COVID-19 infection has the potential to identify immunogens as a surrogate to the finding of protective secretory IgA in the respiratory tract. This would not preclude other research that may focus on systemic protection rather than mucosal or on protection simultaneously from both aspects. The authors declare that there are no conflict of interests.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».