Deciphering the impact of the mitochondrial negative regulator MCJ on host-microbiota interactions in experimental ulcerative colitis
Notice bibliographique
Résumé
Inflammatory bowel disease (IBD) encompasses two types of idiopathic intestinal diseases, ulcerative colitis (UC) and Crohn's disease.Both are chronic, heterogeneous, and severe inflammatory disorders that primarily affect the intestine.Although the specific underlying cause of UC is unknown, it is considered the result of a complex interaction between the microbiota, immune system, host genetics and environmental factors.Recent evidence has demonstrated potential links between mitochondrial dysfunction and IBD.Indeed, mitochondrial function are decreased in active UC patients, en estadios tempranos la evolucin de la enfermedad y la respuesta a la terapia con agentes anti-TNF en pacientes con CU.Por otra parte, este estudio nos ha permitido identificar posibles firmas microbianas en las heces asociadas con la progresin de la enfermedad y la respuesta a terapias, que podran servir como biomarcadores predictivos, permitiendo la estratificacin de los pacientes.It is emerging as a global disease with a sharp increase in worldwide incidence and prevalence.Industrialization has largely affected humans health by dramatically changing transportation, agriculture, manufacturing, urbanization and diet.Hence, shifts in manufacturing have led to increased air pollution, and fibers from diets started to be less plant-based (Windsor et al., 2019).Industrialization has accelerated IBD incidence in newly industrialized countries, principally occurring in the Western World, which includes Europe, North America and Australia.Remarkably, UC is more prevalent than CD and the first UC reports were detected in the 1800s in the Western World.Precisely, Samuel Wilks first described UC in 1859 (Gajendran et al., 2019).From the nineteenth century onward, the incidence of IBD has been rising continuously (Kaplan, 2015).Regarding the incidence of UC between 1990 and 2016, the highest incidence was recorded in developed countries such as Canada, USA, North Europe, Australia and Figure 5. Toll-like receptors, NOD-like receptors and their signaling pathways.TLR1 and TLR6 recognize their ligands (Triacylated and diacylated lipopeptides respectively) as heterodimers with TLR2.TLR4 recognizes lipopolysaccharide (LPS) from gram negative bacteria.TLR3, TLR4, TLR5, TLR7, and TLR9 are currently thought to deliver their signal by forming homodimers after interacting with their ligands.TLR3, TLR7/8, and TLR9 are intracellular TLRs, located inside the endosome that recognize nucleic acids.NOD1 and NOD2 function as intracellular receptors for bacterial peptidoglycan fragments.While NOD1 activity is triggered by D-glutamyl-meso-diaminopimelic acid (DAP), NOD2 is activated by muramyl dipeptides (MDPs).Therefore, these receptors activate several transcription factors including the nuclear factor (NF)-kB and AP-1 by both TLRs and NLRs, and IRF3 and IRF7 (Interferon pathway) by TLRs, which results in the control of the inflammation, immune regulation, survival and proliferation.AP-1: activator protein 1; CpG-ODN: CpG oligodeoxynucleotides; dsRNA: double-strand RNA; IkB: NFKB Inhibitor Alpha; IKK: IkappaB kinase; IRF3/7: interferon regulatory factor 3/5/7; NF-k: nuclear factor kappa B; ssRNA: single-stranded RNA virus; TIRAP: TIR domain-containing adaptor protein; TRAF3/6: TNF receptor-associated factor 3/6; TRIF: TIR-domain-containing adapter-inducing interferon-.
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».