Integrating Population Heterogeneity Indices with Microfluidic Cell-Based Assays
Why this work is in the frame
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Bibliographic record
Abstract
Recent advances in cell-based assays have involved the integration of single-cell analyses and microfluidics technology to facilitate both high-content and high-throughput applications. These technical advances have yielded large datasets with single-cell resolution, and have given rise to the study of cell population dynamics, but statistical analyses of these populations and their properties have received much less attention, particularly for cells cultured in microfluidic systems. The objective of this study was to perform statistical analyses using Pittsburgh Heterogeneity Indices (PHIs) to understand the heterogeneity and evolution of cell population demographics on datasets generated from a microfluidic single-cell-resolution cell-based assay. We applied PHIs to cell population data obtained from studies involving drug response and soluble factor signaling of multiple myeloma cancer cells, and investigated effects of reducing population size in the microfluidic assay on both the PHIs and traditional population-averaged readouts. Results showed that PHIs are useful for examining changing population distributions within a microfluidic setting. Furthermore, PHIs provided data in support of finding the minimum population size for a microfluidic assay without altering the heterogeneity indices of the cell population. This work will be useful for novel assay development, and for advancing the integration of microfluidics, cell-based assays, and heterogeneity analyses.
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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 it