Notice bibliographique
Résumé
The immune system protects the body against pathogens such as bacteria, viruses, and parasites, and removes malignant cells such as cancer cells. It recognizes harmful cells and initiates responses to eliminate them. The immune system consists of different immune cell types, proteins, and organs that together form a highly advanced and complex network. To prevent damage to the body, the immune system must be carefully regulated. Overactivation can cause inflammatory or autoimmune diseases, while insufficient activation increases the risk of infections and cancer. Immune inhibitory receptors (IIRs) help maintain this balance by controlling the start, duration, and strength of immune responses. IIRs are also important targets for therapy. Some tumor cells escape immune attack by producing ligands that activate IIRs, suppressing the immune system. Cancer immunotherapy seeks to block IIRs so that tumor ligands can no longer inhibit immune cells. This strategy is very effective in some cancers but less so in others. Conversely, therapies that activate IIRs are being developed to suppress excessive immune activity in autoimmune and inflammatory diseases. A detailed understanding of how IIRs function is essential for these therapeutic approaches. IIRs are transmembrane proteins with three parts: an extracellular domain that binds ligands, a transmembrane region, and an intracellular domain that transmits signals. These signals often rely on specific motifs, such as ITIM or ITSM, although some IIRs use other motifs. This research investigated three IIRs: CD200R, PD-1, and LAIR-1. • CD200R inhibits the immune system without ITIM or ITSM motifs. We wanted to know how CD200R then transmits signals. By comparing amino acid sequences of CD200R across species we found several overlapping amino acids. By changing these in human cells, we found that some of them are required for the inhibitory function of CD200R. Ultimately, we discovered a new and more extensive signaling motif: EEDExxPYxxYxxKxNxxY. • PD-1, a major target in cancer immunotherapy, suppresses T cells when activated by tumor ligands. Blocking PD-1 can restore T-cell activity. We showed that inflammatory factors, especially interferon-alpha, reduced the effectiveness of PD-1 blockade in vitro. However, in a study of 22 melanoma patients, we found no link between inflammation levels and therapy outcomes. • The outer parts of LAIR-1 and LILRB4 can bind to each other. We wanted to know what that means, so we studied their interaction. We found that LAIR-1 and LILRB4 do not act as ligands for each other when present on different cells. However, when expressed on the same cell, LILRB4 inhibits less effectively in response to the LILRB4 ligand in the presence of LAIR-1. This indicates that LAIR-1 disrupts the function of LILRB4 when they are present on the same cell. Conclusion: The context in which an IIR functions is important for its effect. Their signaling motifs, interactions with inflammation, and influence on each other all shape their effects on the immune system. This knowledge is important for developing improved therapies for both cancer and autoimmune diseases.
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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,694 | 0,450 |
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 ».