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Low Blood Counts of Memory/Effector CD8 T Cells, Gamma/Delta T Cells, Memory B Cells and Plasmacytoid Dendritic Cells and High Counts of Th2 Cells in Systemic Sclerosis

2011· article· en· W2575520160 on OpenAlexaff
Jan Storek, Rob Woolson, Paul K. Wallace, Gregory D. Sempowski, Peter A. McSweeney, Maureen D. Mayes, Leslie J. Crofford, Sharon LeClercq, Richard A. Nash, Keith M. Sullivan

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsRPM International (Canada)University of Calgary
Fundersnot available
KeywordsCD8DLCOImmunologyMedicineCytotoxic T cellImmune systemLungInternal medicineBiologyDiffusing capacityIn vitro

Abstract

fetched live from OpenAlex

Abstract Abstract 4917 Introduction: Systemic sclerosis (SSc) is presumed to result from aberrant activation of autoreactive T cells. However, the exact pathogenesis of SSc is not known. Patients and Methods: To contribute to the understanding of the immunopathology of systemic sclerosis (SSc), we compared blood counts of multiple lymphocyte subsets between 20 adult SSc patients not treated with immunomodulatory drugs and healthy controls. The patients had to fit entry criteria for SCOT trial (Scleroderma – Cyclophosphamide or Transplantation?, www.sclerodermatrial.org), i.e, 1. symptoms for no longer than 5 years (except for Raynaud's phenomenon), 2. diffuse scleroderma, and 3. either moderate lung involvement (forced vital capacity (FVC) or diffusion of carbon monoxide (DLCO) between 45 and 70% predicted) or moderate kidney involvement (history of hypertensive renal crisis, but normal renal function at study entry). Multiparameter flow cytometry was used for the determination of the lymphocyte subset counts. Results: Counts of the following subsets were significantly lower in the patients compared to the controls: total T cells (median 1316 vs 2088/ul, p=0.015), total CD8 T cells (273 vs 580/ul, p<0.001), central memory CD8 T cells (23 vs 87/ul, p<0.001), effector memory CD8 T cells (17 vs 39/ul, p=0.015), effector CD8 T cells (28 vs 68/ul, p=0.001), gamma/delta T cells (31 vs 77/ul, p<0.001), switched (IgM/DàIgG/A isotype switched) memory B cells (6 vs 26/ul, p<0.001), non-switched memory B cells (7 vs 17/ul, p=0.004), and plasmacytoid dendritic cells (2 vs 6/ul, p=0.002). Counts of Th2-biased (producing interleukin-4 upon polyclonal stimulation) CD4 as well as CD8 T cells were significantly higher in the patients compared to the controls (248 vs 139/ul for CD4, p=0.002, and 259 vs 164/ul for CD8, p<0.001). Conclusion: Immunopathology of SSc is complex. Low blood counts of memory/effector CD8 T cells, gamma/delta T cells, memory B cells and plasmacytoid dendritic cells and Th2-biased T cells may play a role in the pathogenesis of SSc. However, cause and effect relations need to be established. Given previous reports of increased numbers of CD8 and gamma/delta T cells in the affected tissues of patients with systemic sclerosis and increased numbers of plasmacytoid dendritic cells in the affected tissues of patients with autoimmune diseases (compared to healthy individuals) (Prescott RJ et al: J Pathol 166 (1992) 255–63, Atamas SP et al: Arthritis Rheum 42 (1999) 1168–78, Giacomelli R et al: Arthritis Rheum 41 (1998) 327–34, Yurovski VV et al: J Immunol 153 (1994) 881–91, Nestle FO et al: J Exp Med 202 (2005) 35–43, Farkas L et al: Am J Pathol 159 (2001) 237–43), it is possible that the low blood counts of CD8 T cells, gamma/delta T cells and plasmacytoid dendritic cells result from redistribution of these cells from blood to affected tissues. Disclosures: No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.200
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes1
Has abstractyes

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