Additional file 2 of Direct comparison of different therapeutic cell types susceptibility to inflammatory cytokines associated with COVID-19 acute lung injury
Bibliographic record
Abstract
Additional file 2.Fig. S1. Cytokine profiling in conditioned media produced by potential COVID-19 cell products. Representative images of the proteome array used to analyze conditioned media produced by BM-MSCs, HDCs, or UC-MSCs. The table on the right panel lists the position coordinates of cytokine antibody on the array.Fig. S2 Relative cytokine content within HDC conditioned media compared to BM-MSC conditioned media. Expression of cytokines within conditioned media produced by HDCs compared to BM-MSCs. Data were compared using an unpaired t-test with individual variances for samples and a two-stage step-up (Benjamini, Krieger, and Yekutieli) false discovery rate to account for multiple comparisons. All data are presented as individual and mean values ± SEM, n=3 biological replicate; each circle represents one data point from one unique biological replicate. Significance is indicated on the panels. Fig. S3. Relative cytokine content within UC-MSC conditioned media compared to BM-MSC conditioned media. Expression of cytokines within conditioned media produced by UC-MSCs compared to BM-MSCs. Data were compared using an unpaired t-test with individual variances for samples and a two-stage step-up (Benjamini, Krieger, and Yekutieli) false discovery rate to account for multiple comparisons. All data are presented as individual and mean values ± SEM, n=3 biological replicates; each circle represents one data point from one unique biological replicate. Significance is indicated on the panels.Fig. S4. Relative cytokine content within UC-MSC conditioned media compared to HDC conditioned media. Expression of cytokines within conditioned media produced by UC-MSCs compared to HDCs. Data were compared using an unpaired t-test with individual variances for samples and a two-stage step-up (Benjamini, Krieger, and Yekutieli) false discovery rate to account for multiple comparisons. All data are presented as individual and mean values ± SEM with n=3 biological replicates; each circle represents one data point from one unique biological replicate. Significance is indicated on the panels. Fig. S5. Cytokine concentrations found within the lung of critically ill COVID-19 patients. This figure depicts literature search results of cytokine concentrations found in bronchial alveolar lavage fluid from critically ill patients with COVID-19. When data were not presented in a tabular form, they were extracted from the figures by using online tool WebPlot Digitizer. All data are presented as individual and mean values ± SEM. The values on the bar graphs represent arithmetic mean of all studies for respective cytokine. Fig. S6. Flow cytometry showing co-segregation of receptors for COVID-19 related cytokines. Representative flow cytometry images demonstrating detection of FITC anti-human IL-1R1, BV421 anti-human IL-2Rβ, PerCP-eFluor710 anti-human IL-6R, PerCP-cy5.5 anti-human IL-8RA, APC anti-human IL-10R, and PE anti-human TNFR1on BM-MSCs, HDCs and UC-MSCs. Isotype controls were used to correct compensation and confirm antibody specificity.Fig. S7. COVID-19 ARDS cytokine challenged UC-MSC conditioned media effect on pulmonary microvascular endothelial cell permeability. Endothelial cell permeability assay showed that treating pulmonary microvascular endothelial cells with conditioned media (CM) collected from COVID-19 ARDS cytokine cocktail challenged UC-MSCs does not alter endothelial cell permeability. Filled circles represent cytokine cocktail treatment, Hollow circles represent no cytokine cocktail treatment. All data are presented as individual and mean values ± SEM. n=3 biological replicates; each circle represents one data point from one unique biological replicate.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.892 | 0.168 |
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".