Additional file 4 of Metabolic switching, growth kinetics and cell yields in the scalable manufacture of stem cell-derived insulin-producing cells
Bibliographic record
Abstract
Additional file 4. Figure S4: Effect of glucose concentration on stage-specific markers. (A) Flow cytometry quantification of SOX17+/FOXA2+ at S1D3 with representative flow cytometry plots (n= 6 biological replicates; one-way ANOVA with Tukey post-test). (B) Gene expression profile of SOX17 and FOXA2 at S1D3 (n= 6-8 biological replicates; Friedman test with Dunn’s post-test). (C) Representative crystal violet staining before and after S4D3 lifting. (D) Flow cytometry quantification of NKX6.1+/PDX1+ at S4D3 with representative flow cytometry plots (n= 4 biological replicates; one-way ANOVA with Tukey post-test). (E) Gene expression profile of PDX1 and NKX6.1 at S4D3 (n= 5 biological replicates; Friedman test with Dunn’s post-test). (F) Representative change in phenol red pH indicator during S1D1. (G) Lactate-to-glucose ratio from cells (n = 6-8 biological replicates; two-way ANOVA with Tukey post-test). All box and whisker plots show individual points with median and interquartile range. *P< 0.05, **P< 0.01, ***P< 0.001, ****P< 0.0001. S1D3 = stage 1 day 3, S2D3 = stage 2 day 3, S3D2 = stage 3 day 2, S4D3 = stage 4 day 3
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.817 | 0.193 |
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".