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Enregistrement W2122463469 · doi:10.1155/2015/570653

Mediators of Inflammation: Inflammation in Cancer, Chronic Diseases, and Wound Healing

2015· article· en· W2122463469 sur OpenAlexaff
Caigan Du, Madhav Bhatia, Sydney Tang, Ming‐Zhi Zhang, Theodore S. Steiner

Notice bibliographique

RevueMediators of Inflammation · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueGout, Hyperuricemia, Uric Acid
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésInflammationWound healingMedicineCancerChronic woundImmunologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Chronic diseases and conditions, such as kidney and liver failure, cancers, and diabetes, are the leading causes of morbidity and mortality, and, according to the data published by the World Health Organization [1], these diseases caused 38 million deaths in 2009 alone, more than 62% of all deaths around the world. However, the pathogenesis of these diseases is not fully understood yet. In more recent years, evidence indicates the link of a form of low-degree systemic and chronic inflammation to many types of chronic diseases including cancer [2–6], suggesting the inflammation as a common risk factor for these diseases. What is inflammation? The word inflammation comes from the Latin “inflammo,” meaning “blaze, burn,” and is defined in biology as “the body's immune system's response to stimulus” by the US National Library of Medicine. The inflammation is commonly ignited by the pathogen (bacteria, viruses, or fungi) infection, tissue injuries and remodeling, or nonphysiological cell death, and is typically viewed as a self-protective response and is important for wound healing. However, in most cases, prolonged or chronic inflammation leads to the pathological changes in body tissues or organs, an inflammatory disease. To understand how the inflammation contributes to the chronic diseases including cancer, we have collected eight clinical observation and experimental studies in this special issue focusing on the mediators of inflammation in chronic diseases and cancer. Using systemic review and meta-analysis, W. Wang et al. have shown an association of gout with an increased risk of cancer, particularly urological cancers, digestive system cancers, and lung cancer. Similarly, there is a positive correlation of serum uric acid levels with total cancer incidence, but it is only found in males not in females (S. Yan et al.). Gout is an inflammatory response to the accumulated urate crystals in the joint that may be formed due to the high levels of uric acid in the blood, in which uric acid crystals have been identified as an endogenous stimulus for activation of the immune responses, particularly interleukin-1β-mediated inflammation via activation of the NOD-like receptor protein 3 (NLRP3) inflammasome [7]. How this inflammatory response is related to tumor development and why this association is gender-dependent are not known. The association of a panel of inflammation mediators, such as interleukin-8 and tumor necrosis factor- (TNF-) α, with chronic diseases (liver disease and gastritis) and different types of cancer (liver and gastric cancer and melanoma) has been documented in this issue (T. Liu et al.; A. Essadik et al.; C.-D. Ene et al.). Whether upregulation of these mediators is part of pathogenesis of these diseases or is just an associated risk factor requires further investigation. Interestingly, T. Liu et al. summarize that c-Myc, a protooncogene, may play a central role in the development of liver inflammation as well as liver cancer. Is mutation of c-Myc required for the stimulation of inflammation in the liver? A similar question is for the study by A. Essadik et al.; whether the mutation at TNF-α−238 (G/A) allele found in patients with gastric pathologies promotes the transcription of TNF-α or that at TNF-α−193 (G/A) downregulates its expression needs further investigation. Finally, we do need well-designed experimental studies to answer our question, the role of inflammation in chronic diseases, carefully. G. C. W. Chan et al. present a good example for that by showing different effects from others in literature of N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) or Captopril treatment on the attenuation of interstitial injury or macrophage in a mouse model of chronic kidney disease. In summary, chronic diseases including cancer somehow affect everyone's life, and we do not know the exact cause of these diseases and how to effectively treat or prevent them as of today. Numerous studies including the papers published in this issue clearly show the association of inflammation with the development of chronic diseases, but we have more questions about the role of inflammation in the pathogenesis of these diseases than the answers we have to that as of today. Caigan Du Madhav Bhatia Sydney C. W. Tang Mingzhi Zhang Theodore Steiner

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0040,008
Études des sciences et des technologies0,0000,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,273
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations32
Publié2015
Routes d'admission1
Résumé présentoui

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