Journal: Toxicological Sciences

Article Title: Arsenic Stimulates Myoblast Mitochondrial Epidermal Growth Factor Receptor to Impair Myogenesis

doi: 10.1093/toxsci/kfaa031

Figure Lengend Snippet: Arsenic effects on EGFR and mitochondrial regulation of nuclear cyclinD1. A, Representative images of cyclin D1 immunofluorescence in undifferentiated RC replated in arsenic-medium. AG-1478 was added simultaneously with arsenic in the differentiation protocol. B–D, Quantitative comparison of nuclear cyclin D1 levels in replated RC. Groups of cells in (C) and (D) were treated with SS-31 or XJB-5-131 after replating and all groups in (B–D) were fixed and analyzed after 3 days in arsenic-free culture. Representative images of cells from (C) and (D) are provided in Supplementary Figure 2. All experiments were repeated 3 times and the data are presented as mean ± SEM of relative fluorescence per cell captured in 5 separate images. Group comparisons were made using ANOVA followed by Tukey’s post hoc test for significance (*p < .05, **p < .01 relative to control, ^p < .05, ^^p < .01 relative to arsenic).

Article Snippet: Cells were then blocked with 5% donkey serum (Millipore, S30-100KC) diluted in PPB for 60 min, and then washed again 3 times with PBB before adding primary antibodies for 60 min. Primary antibodies recognized: Cyclin D1, (1:100, R&D Systems AF4196), pY 845 EGFR (1:150, Cell Signaling Technology 2231), and MTCO2 (1:200, ThermoFisher Scientific 12C4F12).

Techniques: Immunofluorescence, Comparison, Fluorescence

Journal: Toxicological Sciences

Article Title: Arsenic Stimulates Myoblast Mitochondrial Epidermal Growth Factor Receptor to Impair Myogenesis

doi: 10.1093/toxsci/kfaa031

Figure Lengend Snippet: Machine-learning modeling of arsenic adverse outcomes. Machine-learning models were built using different classifiers of arsenic signaling data in order to predict whether an individual cell belonged to the control conditions, an arsenic group, and/or an intervention group. The input data consisted of the pY845EGFR, MTCO2, or cyclin D1 protein expression levels and pY845EGFR/MTOC colocalization. The capacity of these single-cell markers to discriminate the experimental conditions was comparatively tested by several classifier models using cross-validation of the training set according to the various metrics. Table I: The training set (approximately two-third of the experimental data) was used to construct features attuned to the molecular fingerprint of the noncanonical EGFR pathway and combine them into a generalizable classifier model learned on the empirical dataset, and applied to the test dataset (ie, the remaining one-third of the experimental data) to evaluate the model’s predictive accuracy. Table II: To account for the potential statistical limitations caused by the relatively small training dataset and possible misrepresentation of the overall cell population heterogeneity, a bootstrapping approach of the original empirical data was utilized to generate a larger sample size. The increased accuracy of estimating errors in the classified data is described by the confusion matrices (A and B). Table III: The condition-labeled datasets and scores of the classification attributes according to their correlation with the class using Gini coefficient, information gain, ANOVA, and χ2.

Article Snippet: Cells were then blocked with 5% donkey serum (Millipore, S30-100KC) diluted in PPB for 60 min, and then washed again 3 times with PBB before adding primary antibodies for 60 min. Primary antibodies recognized: Cyclin D1, (1:100, R&D Systems AF4196), pY 845 EGFR (1:150, Cell Signaling Technology 2231), and MTCO2 (1:200, ThermoFisher Scientific 12C4F12).

Techniques: Expressing, Construct, Labeling

Journal: Diagnostic Pathology

Article Title: Podoplanin immunoexpression in odontogenic lesions: a systematic review, meta-analysis, and integrated bioinformatic analysis

doi: 10.1186/s13000-024-01540-y

Figure Lengend Snippet: Clinical characteristics and related data from included studies in this systematic review

Article Snippet: Zolfaghari et al., 2023[ ] , India , CS , 31.47 8-59 , 24/36 , Yes , IHC , OKC, DC , 30, 30 , 4μm , Mouse Mo PDPN D2-40 (Medaysis).

Techniques: