Abstract TP99: Inflammatory Changes in Brain and Lymphoid Organs After Ischemic Stroke: PET Imaging for Cell Therapy
Bibliographic record
Abstract
Recently, bone marrow stromal cell (BMSC) therapy for stroke is expected due to the immunomodulation effect. Here, we use PET and other detecting method to investigate the inflammatory changes of brain and lymphoid organs in rat stroke model. In this study, F344 rats were used as transient MCAO models. One part of rats were serially monitored by small-animal PET/CT system with new neuroinflammatory ligand [ 18 F]DPA-714. The brains were removed and sliced subsequently for autoradiograph (ARG) and TTC staining. The rest of rats were sacrificed, their brains and lymphoid organs were taken out, weighed, stained, etc. In histology, cytotoxic T cell (CD8α), macrophage (Iba1 and CD68), apoptosis (TUNEL) and proliferation (ki67) antibodies were chosen and analyzed. PET and ARG represented high concentration in the ischemic area which had been confirmed by TTC. In body imaging, no significant change was found. With the increase of ischemic severity, the size of lymphoid organs have a significantly decrease as well as the body weight. Histological assay showed cd8α + and ki67 + cells decreased in the lymphoid organs and increased in brain. Besides macrophages and apoptosis cells increased all the time in liver, in other organs, the rate of cells reached peak and gradually decreased more or less. In summary, [ 18 F]DPA-714 PET has a great potential for evaluating brain damage. Inflammatory responses in brain and lymphoid organs are distinct after stroke. Based on these results, we will continue the study for evaluating immunomodulation effect of BMSC transplantation therapy.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".