Journal: bioRxiv

Article Title: Rescuing functional defects in a zebrafish model of CDKL5 deficiency disorder: Contribution to the identification of new therapeutic compounds

doi: 10.64898/2026.03.12.711124

Figure Lengend Snippet: A) Representative images of the WT, DMSO-treated cdkl5 ⁻/⁻ larvae and cdkl5 -/- larvae following drug treatment with fisetin, divalproex, resveratrol and VX-702, stained with Alcian blue. B) Schematic illustration of the analyzed craniofacial cartilage parameters, adapted from Raterman et al. C) Quantification of craniofacial cartilage parameters, including ceratohyal cartilage length (Ch-l), palatoquadrate length (Pq-l), and ceratohyal cartilage width (Ch-w). Data are presented as median with interquartile range (n=29). Statistical analysis was performed using one-way ANOVA followed by Tukey’s multiple comparisons test. Significant differences are indicated by distinct lowercase letters.

Article Snippet: Resveratrol was order from MedChemExpress (HY-16561/CS-1050), fisetin from Biorbyt (orb593928), divalproex sodium from MyBioSource (MBS579016) and VX-702 from MedChemExpress (HY-10401/CS-0074).

Techniques: Staining

Journal: bioRxiv

Article Title: Rescuing functional defects in a zebrafish model of CDKL5 deficiency disorder: Contribution to the identification of new therapeutic compounds

doi: 10.64898/2026.03.12.711124

Figure Lengend Snippet: RT-qPCR analysis was performed on larvae treated with 10 µM of fisetin, divalproex, resveratrol or VX-702 for 48h. WT and cdkl5 -/- control groups were treated with 0.1% DMSO as vehicle. Relative gene expression levels are presented as mean±SD of three biological replicates, except for the resveratrol group, which included two biological replicates. Each replicate consisted of a pool of 15 larvae. Statistical analysis was performed using one-way ANOVA followed by Tukey’s multiple comparisons test. Statistically significant differences are indicated by distinct lowercase letters.

Article Snippet: Resveratrol was order from MedChemExpress (HY-16561/CS-1050), fisetin from Biorbyt (orb593928), divalproex sodium from MyBioSource (MBS579016) and VX-702 from MedChemExpress (HY-10401/CS-0074).

Techniques: Quantitative RT-PCR, Control, Gene Expression

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Biomarker selection, correlation analysis, and functional similarity (Friend) analysis. A LASSO regression for prognostic gene selection. The optimal value of the lambda parameter is determined at the point with the minimum mean squared error on the curve. B Boxplots of candidate biomarker expression levels. The left panel shows expression in the training set, and the right panel shows expression in the validation set. C ROC curves of signature genes in the training set. The bottom-right legend lists each candidate biomarker along with its corresponding AUC value. D Correlation analysis of biomarkers in the IS training set, indicating that CXCL16 exhibits relatively strong functional similarity with other biomarkers. E Boxplot of functional similarity (Friend analysis) among biomarkers. Red represents ACTA2, green represents ST3GAL4, and blue represents CXCL16

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Biomarker Discovery, Selection, Functional Assay, Expressing

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Construction and evaluation of the nomogram model. A Nomogram based on selected biomarkers. The “Total Points” represents the sum of the individual scores corresponding to each gene expression level, which can be mapped to the predicted probability of IS at the bottom of the figure. B Calibration curve of the nomogram. The Hosmer–Lemeshow test yielded a p-value > 0.05, indicating no significant difference between predicted and actual outcomes, and a good model fit. The mean absolute error (MAE) < 0.1 suggests minimal deviation between predicted and observed risk, supporting high predictive accuracy. C Decision curve analysis (DCA) for the biomarkers CXCL16, ST3GAL4, and ACTA2. All regression models showed positive net benefit, indicating that the nomogram provides good clinical utility. D Clinical impact curve (CIC). The CIC demonstrates that as the threshold probability increases, the number of predicted high-risk patients closely matches the actual cases, indicating improved clinical prediction efficiency. E ROC curve of the nomogram model, suggesting a certain diagnostic value for predicting IS

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Gene Expression, Diagnostic Assay

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: GSEA and GSVA enrichment analyses of prognostic genes. A GSEA results for CXCL16, showing enrichment of CXCL16-related genes in 67 pathways. B GSEA results for ST3GAL4, with 54 pathways enriched by ST3GAL4-associated genes. C GSEA results for ACTA2, identifying 55 enriched pathways related to ACTA2. D GSVA enrichment analysis of CXCL16, primarily enriched in 90 KEGG pathways including KEGG_MISMATCH_REPAIR. E GSVA enrichment analysis of ST3GAL4, primarily enriched in 90 KEGG pathways including KEGG_PRIMARY_IMMUNODEFICIENCY. F GSVA enrichment analysis of ACTA2, mainly enriched in 64 KEGG pathways including KEGG_BUTANOATE_METABOLISM

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques:

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Drug prediction and molecular docking analysis. A Biomarker–drug interaction network. Red nodes represent biomarkers, and pink nodes represent predicted drug candidates. B–D Molecular docking models of key genes with their corresponding predicted drugs: B CXCL16 with benzene; C ST3GAL4 with Idose; D ACTA2 with probucol

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: Biomarker Discovery

Journal: Cellular and Molecular Neurobiology

Article Title: Identification of Biomarkers Associated With Copper Metabolism in Ischemic Stroke Through Bulk RNA Sequencing and Mendelian Randomization Analysis

doi: 10.1007/s10571-026-01697-8

Figure Lengend Snippet: Molecular docking results between the proteins encoded by biomarkers and their respective inhibitors. A CXCL16 with Vx-702. B ST3GAL4 with Brigatinib. C ACTA2 with Y-27632

Article Snippet: To further validate the reliability of the molecular docking approach, we selected known target protein inhibitors reported in the literature as positive controls for docking analysis, including: the ACTA2 inhibitor Y-27632 (Aguado et al. ), the CXCL16 inhibitor Vx-702 ( https://www.scbt.com/browse/cxcl16-inhibitors ), and the ST3GAL4 inhibitor Brigatinib (Han et al. ).

Techniques: