Chimeric cells of maternal origin do not appear to be pathogenic in the juvenile idiopathic inflammatory myopathies or muscular dystrophy
Bibliographic record
Abstract
INTRODUCTION: Microchimeric cells have been studied for over a decade, with conflicting reports on their presence and role in autoimmune and other inflammatory diseases. To determine whether microchimeric cells were pathogenic or mediating tissue repair in inflammatory myopathies, we phenotyped and quantified microchimeric cells in juvenile idiopathic inflammatory myopathies (JIIM), muscular dystrophy (MD), and noninflammatory control muscle tissues. METHOD: Fluorescence immunophenotyping for infiltrating cells with sequential fluorescence in situ hybridization was performed on muscle biopsies from ten patients with JIIM, nine with MD and ten controls. RESULTS: Microchimeric cells were significantly increased in MD muscle (0.079 ± 0.024 microchimeric cells/mm(2) tissue) compared to controls (0.019 ± 0.007 cells/mm(2) tissue, p = 0.01), but not elevated in JIIM muscle (0.043 ± 0.015 cells/mm(2)). Significantly more CD4+ and CD8+ microchimeric cells were in the muscle of patients with MD compared with controls (mean 0.053 ± 0.020/mm(2) versus 0 ± 0/mm(2) p = 0.003 and 0.043 ± 0.023/mm(2) versus 0 ± 0/mm(2) p = 0.025, respectively). No differences in microchimeric cells between JIIM, MD, and noninflammatory controls were found for CD3+, Class II+, CD25+, CD45RA+, and CD123+ phenotypes, and no microchimeric cells were detected in CD20, CD83, or CD45RO populations. The locations of microchimeric cells were similar in all three conditions, with MD muscle having more microchimeric cells in perimysial regions than controls, and JIIM having fewer microchimeric muscle nuclei than MD. Microchimeric inflammatory cells were found, in most cases, at significantly lower proportions than autologous cells of the same phenotype. CONCLUSIONS: Microchimeric cells are not specific to autoimmune disease, and may not be important in muscle inflammation or tissue repair in JIIM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".