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Enregistrement W2318601888 · doi:10.1097/coh.0000000000000098

Editorial overview

2014· editorial· en· W2318601888 sur OpenAlexaff
Nabila Seddiki, Daniel E. Kaufmann

Notice bibliographique

RevueCurrent Opinion in HIV and AIDS · 2014
Typeeditorial
Langueen
DomaineImmunology and Microbiology
ThématiqueImmune Cell Function and Interaction
Établissements canadiensCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesNational Heart, Lung, and Blood Institute
Mots-clésMedicineIntensive care medicineAntiretroviral therapyDiseasePsychological interventionImmune systemOptimismImmunologyHuman immunodeficiency virus (HIV)PsychologyViral loadPsychotherapistPsychiatryPathology

Résumé

récupéré en direct d'OpenAlex

Since the initial descriptions of CD4 T cell depletion as a critical factor associated with progression to AIDS, our understanding of immune dysfunction in HIV infection has dramatically evolved. Over the past decade, in particular, technical progress and conceptual advances have allowed the exploration of a wide array of qualitative and functional changes that occur in multiple cell types and several compartments of the body. The realization that chronic immune activation is a major driving force in disease progression and associated with clinical complications even in patients on antiretroviral therapy has fostered progress in clarifying the complex interplay between the virus and the infected host. Unfortunately, although improvement in antiretroviral therapy has been dramatic since the mid 1990s, the better understanding of immune impairment has yet to result in therapeutic interventions that efficiently complement antiretroviral therapy (ART) or improve the efficacy of HIV vaccine candidates. However, recent progress in other fields of medicine gives reasons for optimism. In particular, new immunotherapies have shown dramatic results in treatment of several autoimmune diseases and previously refractory types of cancer, with, in many cases, very good tolerance by the patients. The efficacy of these clinical interventions suggests that in the field of chronic infectious diseases – in particular HIV – the knowledge gained in animal models and human studies will translate into better patient care and preventive strategies. In this issue, a series of reviews cover new progress in the understanding of immune cell dysfunction in HIV infection. Several themes are also addressed in the perspective of studies of other chronic viral infections in humans, nonhuman primates or mice. The importance of both cell-extrinsic factors provided by the altered microenvironment of HIV infection and cell-intrinsic factors, including genetic exhaustion programs, is addressed. These articles provide an overview of mechanisms that affect function of both the innate and adaptive immunity. The critical importance of inhibitory coreceptors in the functional impairment of T cells in chronic infection has been well demonstrated over recent years. Kuchroo et al. (pp. 439–445) review recent findings on their role in CD8 T cell exhaustion and discuss their interplay. However, recent data show that CD4 T cell dysfunction is not a copycat of T cell impairment and is, in part, governed by distinct mechanisms. Morou et al. (pp. 446–451) underline the importance of CD4 T cell plasticity in infectious diseases and the contributing roles of both skewing of CD4 T cell differentiation and exhaustion mechanisms. Seddiki and Draenert (pp. 452–458) describe recent advances on suppressor cells and the availability of new markers and functional assays to investigate regulatory T cells, regulatory B cells and myeloid-derived suppressor cells. A critical component of T cell dysfunction resides in a complex network of transcription factors, discussed by Collins and Henderson (pp. 459–463). Major progress has been made recently in the understanding of the role of noncoding microRNA (miRNA) in regulating cell function in both physiologic and pathological conditions. Swaminathan and Kelleher (pp. 464–471) discuss microRNAs as potential new important players in the T cell dysfunction observed with HIV-1 infection and their potential as therapeutic targets. The B cell compartment is also affected in HIV infection. Moir and Fauci (pp. 472–477) review the role played by immune activation in B cell exhaustion, and compare it to T cell exhaustion and B cell alterations in other diseases. Antigen-presenting cells are profoundly altered in HIV infection, and Piguet et al. (pp. 478–484) review the adverse effects of chronic hyperactivation of this critical population. It is only in the early 2000s that T follicular helper cells have been identified as a critical population for B cell help. Tremendous progress has been made in this area since then. Cubas and Perreau (pp. 485–491) report on recent findings addressing the role of T follicular helper cells (Tfh) cells in HIV infection as well as the impact HIV infection has on germinal center Tfh and circulating memory Tfh cell frequency and function. The precise links between these populations still need to be fully defined, and studies in animal models are particularly informative in this regard. In line with this, McGary et al. (pp. 492–499) describe the most recent advances in the use of animal models for the study of cell exhaustion following HIV or simian immunodeficiency virus (SIV) infection, and their critical role on the path to possible new immunotherapeutic approaches. Finally, Mudd and Lederman (pp. 500–505) describe the adverse effects of the expansion of the CD8 compartment in HIV infection that is associated with adverse clinical events, even in ART-treated individuals. Our understanding of the complexity of immune cell dysfunction – here defined as exhaustion in a broad sense – has expanded dramatically in recent years. There are reasons to be optimistic and to hope that the time is near when this knowledge will be translated into new therapeutic approaches to complement ART and into better patient care. Acknowledgements None. Conflicts of interest There are no conflicts of interest.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,024
Tête enseignante GPT0,311
Écart entre enseignants0,287 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations7
Publié2014
Routes d'admission1
Résumé présentoui

Explorer davantage

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