Bibliographic record
Abstract
Lymph node stromal cells (LNSCs) shape the lymph nodes (LNs), where adaptive immune responses are initiated. They are crucial key players in adaptive immunity and guardians of peripheral tolerance. Therefore, malfunctioning of LNSCs might lead to deregulation of adaptive immunity and potentially lead to autoimmune disease. So far, LNSCs have mainly been described in mice and this thesis presents a model to characterize human LNSCs in the context of the autoimmune disease rheumatoid arthritis (RA) by comparing LNSCs obtained from LN biopsies from three donor groups: Healthy controls, individuals which show enhanced risk of developing RA due presence of RA-specific autoantibodies (RA-risk) and RA patients. We found that human LNSCs in line with their murine counterparts expressed various immunomodulatory genes and proteins and expression was maintained in prolonged culture. Furthermore, human LNSCs preserved their capacity to influence T cell proliferation in ratio-dependent manner. In relation to autoimmunity we demonstrate disturbances of the LN microenvironment already in early stages of RA. RA-risk and RA LNSCs showed reduced induction of T cell guiding chemokines after stimulation, their contraction and proliferation ability was reduced, their regulation of T cell proliferation was disturbed and they presented a distinct expression pattern of disease-related self-antigens. Together, this might contribute to the loss of peripheral tolerance and in autoimmunity lead to attenuated function of the LN during inflammation. Overall, our approach of characterizing LNSCs during RA development has great potential to reveal new and unanticipated pathogenic processes ongoing in RA and might discover promising new therapeutic targets.
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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 | 0.000 |
| 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) | 0.001 | 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".