Bone Marrow-derived Human Hematopoietic Stem Cells Engraft NOD/SCID Mice and Traffic Appropriately to an Inflammatory Stimulus in the Joint
Bibliographic record
Abstract
OBJECTIVE: Studies of human inflammatory arthritis would be significantly aided by the development of better animal models. Our hypothesis is that it is possible to develop humanized arthritis models through novel techniques of hematopoietic stem and progenitor cell (HSPC) delivery. METHODS: Bone marrow was obtained from patients with osteoarthritis who were undergoing total hip replacement. HSPC were enriched by negative selection and injected into the femur of irradiated anti-CD122 treated nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice. Human cell engraftment was analyzed by flow cytometry. Arthritis was induced by an intraarticular injection of Chlamydia trachomatis and injected knee joints were examined 5 days later by histology and immunohistochemistry. RESULTS: Human bone marrow HSPC successfully engrafted NOD/SCID mice, with some mice showing up to 90% engrafted human cells. Human B lymphoid and myeloid cells were detected in the bone marrow and spleen 6 weeks following transfer of HSPC, and engrafted recipient mice remained healthy up to 12 weeks postinjection. Chlamydia-injected mice that had been repopulated with HSPC had synovial inflammation, consisting of human neutrophils and macrophages. CONCLUSION: Bone marrow-derived human HSPC engraft NOD/SCID mice and traffic appropriately to an inflammatory stimulus in the joint, thus offering the potential for direct studies on the immunopathogenesis and treatment of human arthritis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".