Editorial: Current progress in genomic and genetic research on human viral diseases
Notice bibliographique
Résumé
understanding of these genetic and genomic aspects, this research topic aimed to advance our knowledge and contribute to the development of effective strategies against human viral diseases.We are delighted to have received many excellent submissions, nine of which have been successfully published in this collection. This editorial piece may serve as a brief overview of these articles, highlighting their substantial contributions to the research topic. This compilation includes three original research articles addressing the pressing issues of COVID-19 and SARS-CoV-2. Čiučiulkaitė, et al. focused on the effect of the human GNB3 c.825C>T polymorphism on immune responses upon COVID-19 mRNA vaccination. This was one of the few studies at the time to document the significant decline in antibody titers and T-cell responses from months 1 to 6 after the second dose of vaccination. It also was one of the few studies to examine the kinetics of T-cell responses against SARS-CoV-2 after mRNA vaccination. While the GNB3 c.825C>T polymorphism had no significant effect on the humoral immune response, individuals with the CC genotype at this locus exhibited significantly enhanced T-cell responses after mRNA-1273 vaccination, suggesting improved protection against COVID-19. Möhlendick, et al. from the same research group explored the potential impact of the GNB3 c.825C>T polymorphism on the clinical outcome of COVID-19. This study, which included 1570 individuals, examined the potential associations between demographic factors, pre-existing conditions, laboratory parameters, GNB3 rs5443 genotype, and COVID-19 clinical outcome. Notably, in addition to identifying associating factors against fatal COVID-19 outcomes among other examined parameters, they reported that the GNB3 rs5443 TT genotype was significantly associated with a 40% reduced risk of COVID-19 fatality, suggesting its potential as a prognostic biomarker. While the findings of Möhlendick et al. on GNB3 genotypes may seem to contradict the observations of Čiučiulkaitė et al., it is important to note that the former focused on clinical outcomes in unvaccinated individuals, while the latter focuses on immune responses to COVID-19 vaccination. These divergent findings underscore the complexity of the interaction between SARS-CoV-2 and the host genetics and immune responses, and further research is needed to reconcile and fully understand these relationships.Another COVID-19-related study was conducted by Li et al., who investigated into the SARS-CoV-2 pathogenesis from a viral genetics perspective. Among many SARS-CoV-2 variants of concern (VOC), the Delta variant emerged from India in 2020 and swept the world in mid-2021 with a high transmission rate and high mortality. This study investigated the evolution of the Delta variant by analyzing mutational patterns in viral genomes originating from India at three different time periods surrounding the emergence of Delta VOC in the country. The study revealed a progressive increase in viral mutations, with viral genomes exhibiting the highest diversity following the establishment of Delta VOC. Notably, the pre-Delta phase showed a higher number of negatively selected sites, which may have protected critical gene regions during evolution. This study suggests ongoing viral adaptation and evolution and highlights the dynamic genetic landscape of SARS-CoV-2.Not surprisingly, HIV-1 is another virus heavily studied in this collection of publications. Yang et al.
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 | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,007 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,011 |
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 ».