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Enregistrement W3029427224 · doi:10.1093/cid/ciaa664

Evaluating Immune Dysregulation in Patients With COVID-19 Requires a More Accurate Definition of the CD45RA+ T-cell Phenotype

2020· letter· en· W3029427224 sur OpenAlexafffund
Chad Poloni, Chrisos Tsoukas

Notice bibliographique

RevueClinical Infectious Diseases · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueCOVID-19 Clinical Research Studies
Établissements canadiensMcGill University
Organismes subventionnairesCanadian Institutes of Health ResearchFondation de l'Hôpital Général de Montréal
Mots-clésMedicineImmune dysregulationPhenotypeCoronavirus disease 2019 (COVID-19)Immune system2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ImmunologyVirologyPathologyGeneticsDiseaseBiologyOutbreakGene

Résumé

récupéré en direct d'OpenAlex

To the Editor—The coronavirus disease 2019 (COVID-19) pandemic has disproportionally affected the elderly. The recently published study conducted in Wuhan, China, by Qin et al indicated dysregulation of the immune response specifically related to T lymphocytes, suggesting that they are highly involved in the pathophysiology of COVID-19 [1]. T-cell dysregulation is a major contributor to age-related changes of the immune system in the elderly, where T-cell responses become defective. The causes of immunodeficiency are multifactorial, including T-cell phenotypic changes, signal transduction failure, and thymic involution [2, 3]. Dysregulated T-cell responses have been linked to a variety of different diseases typically seen in the elderly, notably cardiovascular disease and Alzheimer’s [4, 5]. Furthermore, an immune phenotype known as the immune risk phenotype (IRP) has been used as a marker to track these changes, and is defined by a low CD4:CD8 T-cell ratio and an expansion of CD8+CD28− T cells in those cytomegalovirus seropositive [6]. It has been shown that IRP-positive individuals have an expansion of CD8+ effector memory T cells (TEM cells) that are low functioning and late-differentiated, causing memory inflation [7]. The recent COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2, has disproportionately impacted the elderly, with severe cases being linked to increases in proinflammatory cytokines in serum [1]. Qin et al sought to characterize the T-lymphocyte responses in COVID-19, with aims to differentiate between nonsevere and severe cases. The severe cases had a significantly higher average age compared to the nonsevere cases, indicating a worse outcome in the elderly. Additionally, the severe group had an increased incidence of cardiovascular disease as compared to the nonsevere population. As the IRP is seen at increased levels in the elderly and has been associated with increased incidence of cardiovascular disease, it would be interesting to see the significance of the IRP in terms of COVID-19 response. The Wuhan study also identified several differences in T-cell populations between the severe and nonsevere COVID-19 cases. Most notably, there were significantly higher levels of CD3+CD4+CD45RA+ T cells in the severe cases, which was attributed to increases in naive cells. Although naive T cells are characterized by the presence of a combination of surface markers including CD45RA, this marker alone cannot be used to define naive subsets. Furthermore, CD45RA is re-expressed during late differentiation and is part of a proinflammatory phenotype identified in the elderly [8]. This terminally differentiated T-cell population has been associated with immune dysregulation in the elderly and is further characterized by low CD28 and increased CD57 expression [9]. The Wuhan study failed to further characterize the CD45RA+ T-cell subset, making it impossible to attribute the increase specifically to the naive T-cell subset. Furthermore, the study did not report on CD8+CD45RA+ T-cell subsets, which are thought to play an important role in the inflammatory aging process. Improved characterization of terminally differentiated CD45RA+ T cells, along with screening for IRP positivity, may be beneficial in identifying those with potential for severe COVID-19. Financial support. This work was supported by the Canadian Institutes of Health Research (CIHR grant number 0996); the Anna-Maria Solinas Laroche Allergy and Clinical Immunology Research fund; and the Montreal General Hospital Foundation to C. P. Potential conflicts of interest. The authors: No reported conflicts of interest. Both authors have submitted the ICMJE Form for Disclosure of Potential 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 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)0,004
score de la tête « metaresearch » (Gemma)0,018
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,018
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0110,011
Charge utile insuffisante (le modèle a refusé de juger)0,0020,002

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 Opus0,144
Tête enseignante GPT0,467
Écart entre enseignants0,324 · 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
GenreCommentaire

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

En bref

Citations2
Publié2020
Routes d'admission2
Résumé présentnon

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