Editorial: Molecular insights of microbiota and innate lymphoid cell interactions
Notice bibliographique
Résumé
In the dynamic field of immunology, the discovery of innate lymphoid cells (ILCs) in 2008 marked a transformative milestone, as highlighted by Vivier et al. (2018). These cells, present in both humans and mice, have become central to research due to their crucial role in maintaining barrier immunity and their involvement in a range of microbiota-driven disorders.ILCs are a diverse group of cells categorized into three main subsets based on their expression of transcription factors and cytokines (Vivier et al., 2018). Group 1 ILCs (including ILC1 and NK cells) are characterized by the expression of T-bet and IFN-γ, mirroring the function of Th1 cells.Group 2 ILCs express GATA3 and cytokines such as IL-4, IL-5, and IL-13, resembling Th2 cells.Group 3 ILCs, defined by the expression of RORγt and production of are analogous to Th17 cells. Although ILCs perform functions similar to adaptive T cells, they are distinguished by the absence of antigen-specific receptors. Instead, their activation and responses are mediated through cytokine and alarmin signaling. These cells are primarily located at mucosal surfaces and other barrier sites and act as a crucial first line of defense by rapidly producing cytokines that orchestrate immune responses and maintain tissue homeostasis. Their role in health and disease is increasingly recognized, particularly in conditions such as inflammatory bowel diseases, cancer, autoimmunity, and metabolic disorders. A key aspect of their function is their interaction with the microbiota-a complex and diverse community of microorganisms essential for shaping host immunity. In particular, the gut microbiota influences immune cell development, modulates inflammatory responses, and impacts systemic immunity (Zheng et al., 2020). Positioned strategically at these barrier sites, ILCs are well-equipped to sense microbial signals and respond accordingly, helping to maintain a balanced immune environment. Disruptions in this crosstalk can lead to dysbiosis, driving the onset and progression of various diseases. Understanding these interactions holds significant potential for developing microbiota-targeted therapies that could restore immune balance and offer new approaches to disease treatment and prevention. In addition to exploring these biological interactions, the integration of multi-omics technologies and artificial intelligence (AI) has opened new avenues for investigating the complex relationships between immune cells and their environment. Xu et al. contribution within this Research Topic utilizes these advanced approaches to uncover new neutrophil clusters and potential biomarkers in sepsis, offering valuable insights into the immune landscape during severe infection. This research not only enhances our understanding of sepsis but also illustrates the potential of integrating multi-
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».