Reply
Notice bibliographique
Résumé
We appreciate the interest and points raised by Albillos and colleagues concerning our recent publication. We are very familiar with the body of literature identifying an increase in circulating TNF-α levels and numbers of TNF-α-producing monocytes in the setting of cirrhosis (and fully recognize the important contributions made by these authors in this area).1, 2 Although we feel that our identification of similar findings in mice without cirrhosis is important, we do not feel that this is the “key finding” of our studies. We have identified that circulating TNF-α-producing monocytes are capable of interacting via specific adhesion molecules with activated endothelial cells, and subsequently infiltrating a tissue other than the liver, in the setting of experimental liver disease. Moreover, we have demonstrated in cholestatic mice that infiltration of the brain by TNF-α-secreting monocytes is associated with resident tissue macrophage (i.e., microglia) activation and TNF-α production within the brain, and suggest that cytokines produced by these activated immune cells may be able to alter the normal function of the tissue in which they are produced (i.e., change neurotransmission to induce fatigue). We do recognize, however, that cytokines released within the circulation are able to communicate with the brain (either directly or indirectly) to induce behavioral changes, including fatigue.3 Interestingly, in contrast to the setting of cirrhosis, we were unable to identify increased circulating TNF-α levels in cholestatic mice without cirrhosis; possibly reflecting a more profound inflammatory response in the setting of cirrhosis. Furthermore, in patients with cirrhosis TNF-α production by monocytes has been reported to correlate significantly with circulating lipopolysaccharide-binding protein levels,1, 2 potentially implicating circulating endotoxin in the observed increase in monocyte TNF-α production. However, we did not find a reduction in the increased production of TNF-α in circulating monocytes in cholestatic mice without cirrhosis which were unable to respond to endotoxin (i.e., TLR4 deficient), compared to TLR4 deficient non-cholestatic controls (MG Swain, unpublished observation, June 2005). This finding suggests that increased monocyte TNF-α production in the setting of liver disease may not be the result of direct endotoxin activation of monocytes (either within the circulation or within mesenteric lymph nodes) but instead may be mediated by other Toll-like receptors (e.g., stimulation of TLR2 on monocytes in cirrhotic patients mediated by gram positive microbial products, as suggested by Riordan et al.).4 We agree that our findings may very well have important implications for the development of both peripheral (e.g., muscle) and central (i.e., neurotransmission) fatigue in cirrhosis. However, as these authors suggest, cirrhosis (especially with associated ascites) is a significantly more complex clinical situation than liver disease without cirrhosis, with a more profound systemic inflammatory response, hematological and renal changes, possible superimposed hepatic encephalopathy, and muscle wasting; all of which may actively contribute to the development of and/or clinical expression of fatigue. This is one of the reasons why we purposely chose to do our current series of experiments in animals without cirrhosis and why we have previously performed our fatigue-related behavioral studies in animals without cirrhosis.5, 6 In fact, a recent editorial has highlighted the complex interplay between infection, the systemic inflammatory response, and the development of hepatic encephalopathy in patients with cirrhosis,7 a discussion which has direct relevance with regards to factors which are likely to play a role in the development of fatigue in the setting of liver disease. Obviously this is an important and fertile area for future investigation, which may have significant potential for therapeutic intervention in patients. Stephen Kerfoot*, Charlotte D'Mello*, Henry Nguyen*, Maureen Ajuebor*, Paul Kubes*, Tai Le*, Mark G. Swain*, * University of Calgary, Liver Unit, Calgary, Alberta, Canada.
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 | 0,003 | 0,040 |
| 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 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,015 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,049 | 0,038 |
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 ».