High-Content Flow Cytometry and Temporal Data Analysis for Defining a Cellular Signature of Graft-Versus-Host Disease
Bibliographic record
Abstract
Acute graft-versus-host disease (GVHD) is diagnosed by clinical and histologic criteria that are often nonspecific and typically apparent only after the disease is well established. Because GvHD is mediated by donor T cells and other immune effector cells, we sought to determine whether changes within a wide array of peripheral blood lymphocyte populations could predict the development of GvHD. Peripheral blood samples from 31 patients undergoing allogeneic blood and marrow transplant were analyzed for the proportion of 121 different subpopulations defined by 4-color combinations of lymphocyte phenotypic and activation markers at progressive time points posttransplant. Samples were processed using a newly developed high content flow cytometry technique and subjected to a spline- and functional linear discriminant analysis (FLDA)-based temporal analysis technique. This strategy identified a consistent posttransplant increase in the proportion and extent of fluctuation of CD3+CD4+CD8beta+ cells in patients who developed GVHD compared to those that did not. Although larger prospective clinical studies will be necessary to validate these results, this study demonstrates that high-content flow cytometry coupled with temporal analysis is a powerful approach for developing new diagnostic tools, and may be useful for developing a sensitive and specific predictive test for GVHD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".