Additional file 1 of Comparison of corneal and lens density measurements obtained by Pentacam and CASIA2 in myopes

Supplementary Material

The Functional Property Changes of Muscular Na_v1.4 and Cardiac Na_v1.5 Induced by Scorpion Toxin BmK AGP-SYPU1 Mutants Y42F and Y5F

Scorpion toxins are invaluable therapeutic leads and pharmacological tools which influence the voltage-gated sodium channels. However, the details were still unclear about the structure–function relationship of scorpion toxins on VGSC subtypes. In the previous study, we reported one α-type scorpion toxin Bmk AGP-SYPU1 and its two mutants (Y5F and Y42F) which had been demonstrated to ease pain in mice acetic acid writhing test. However, the function of Bmk AGP-SYPU1 on VGSCs is still unknown. In this study, we examined the effects of BmK AGP-SYPU1 and its two mutants (Y5F and Y42F) on hNa_v1.4 and hNa_v1.5 heterologously expressed CHO cell lines by using Na⁺-specialized fluorescent dye and whole-cell patch clamp. The data showed that BmK AGP-SYPU1 displayed as an activator of hNa_v1.4 and hNa_v1.5, which might indeed contribute to its biotoxicity to muscular and cardiac system and exhibited the functional properties of both the α-type and β-type scorpion toxin. Notably, Y5F mutant exhibited lower activatory effects on hNa_v1.4 and hNa_v1.5 compared with BmK AGP-SYPU1. Y42F was an enhanced activator and confirmed that the conserved Tyr42 was the key amino acid involved in bioactivity or biotoxicity. These data provided a deep insight into the structure–function relationship of BmK AGP-SYPU1, which may be the guidance for engineering α-toxin with high selectivity on VGSC subtypes

Additional file 1 of Evolocumab loaded Bio-Liposomes for efficient atherosclerosis therapy

Additional file 1. Fig. S1 Phagocytosis of Rho in VSMCs and HUVECs in a transwell. BF indicates bright field. Fig. S2 Cell uptake mechanism of M@Lipo NPs. CLSM image (A) and quantitation (B) of the VSMCs uptake for M@Lipo NPs in different inhibitor groups. Scale bar = 60 μm. Data are means ± SD, n = 3, *P < 0.05, **P < 0.01, ***P < 0.001 vs. the Control. Fig. S3 Immune-escape properties of (Lipo+M)@E NPs in vitro. Confocal images (A) and mean fluorescence intensity (MFI) (B) of different concentrations of Lipo NPs and M@Lipo NPs phagocytosed by RAW264.7 cells. Scale bars = 60 μm. Data are means ± SD, n = 3, *P < 0.05, **P < 0.01, ***P < 0.001 vs. the Lipo. Fig. S4 Distribution of M@Lipo NPs in major organs of ApoE-/- mice. (A) Fluorescence imaging of the major organs of ApoE-/- mice with different treatments for 12 h. (B) The relative fluorescence signal of major organs (n = 3). Statistically significant differences between M@Lipo@Ce6 NPs in different organs and in the livers (###P < 0.001); statistically significant differences between Lipo@Ce6 NPs and M@Lipo@Ce6 NPs in the livers (*P < 0.05). Fig. S5 LPS can increase the expression of PCSK9 in VSMCs. LPS-induced expression of PCSK9 in VSMCs in a dose-dependent fashion (measured by western blot). Data are means ± SD, n = 3, #P < 0.05, ##P < 0.01 vs. the Control. Fig. S6 Evol can reduce the expression of PCSK9 in VSMCs. Western blot assay of the levels of PCSK9 in VSMCs treated with different concentrations of Evol (2.5 nM, 5.0 nM, and 10.0 nM). Data are means ± SD, n = 3, ###P < 0.001 vs. the Control. **P < 0.01, ***P < 0.001 vs. the Model. Fig. S7 Transcriptomic analysis of the Model and (Lipo+M)@E group. (A) Volcano plots show differential expression genes between the Model and (Lipo+M)@E NPs group. Red and blue represent genes upregulation and downregulation, respectively. Biological process (B), Molecular function (C), and Cellular component (D) in GO function of the Model and (Lipo+M)@E NPs group. (E) GSEA enrichment plots of differentially expressed genes in the Model and (Lipo+M)@E group. (a) actin binding. (b) actin monomer binding. (c) extracellular matrix structural constituent. (d) negative regulation of cell proliferation. P < 0.05. Fig. S8 Cellular uptake of oxLDL and Oil Red O staining of VSMCs. Representative fluorescence images (A) and quantification (B) of DiL-oxLDL uptake in VSMCs after incubation for 4 h. Scale bars = 60 µm. Images (C) and quantification (D) of oxLDL internalization in VSMCs. Scale bars = 20 µm. Data are means ± SD, n = 3, ###P < 0.001 vs. the Control. ***P < 0.001 vs. the Model. Fig. S9. (Lipo+M)@E NPs regulated LDLR and PCSK9 levels in livers of ApoE-/- mice. (A) Represented photograph showing the protein expressions of LDLR and PCSK9 in livers of ApoE-/- mice after treatment with Evol, Lipo@E NPs, and (Lipo+M)@E NPs. (B) The relative quantification analysis of LDLR and PCSK9. Data are means ± SD, n = 3, ###P < 0.001 vs. the Control. ***P < 0.001 vs. the Model. Fig. S10 (Lipo+M)@E NPs can alter the composition of intestinal flora in ApoE-/- atherosclerosis mice. (A&B) Shannon indexes and Simpson indexes of the Control, Model, and (Lipo+M)@E NPs group. (C) PCoA analysis of the Control, Model, and (Lipo+M)@E NPs group. The relative abundance of gut microbiota at phylum levels (D) and genus levels (H) in the Control, Model, and (Lipo+M)@E NPs groups. (E) The F to B ratio of three groups at the phylum levels. The relative abundance of Firmicutes (F) and Bacteroides (G) of three groups at the phylum levels. (I) The relative abundance of Bacteroides at genus levels. Data are means ± SD, n = 3, #P < 0.05, ###P < 0.001 vs. the Control. *P < 0.05, ***P < 0.001 vs. the Model. Fig. S11 Metabolic characteristics of ApoE-/- mice in Control, Model, and (Lipo+M)@E NPs groups. (A) Volcano plots show differential metabolites among groups in positive ion and negative ion modes. (B) PLS-DA score plot of the groups in two ion modes based on LC-MS technology. (C) Heatmaps show the differences in metabolites among the three groups. (D) Venn diagram compares overlap and unique differential metabolites among the groups. Fig. S12 (Lipo+M)@E NPs reverse metabolic disorders caused by HFD. (A) KEGG pathway between Control, Model, and (Lipo+M)@E group. (P < 0.05). The levels of metabolites in different metabolic pathways including Chenodcoxycholic Acid (B), Taurochenodeoxycholic acid (C), Deoxycholic acid (D), Pantothenic acid (E), Ursodeoxycholic acid (F), and Palmitoylethanolamide (G) in Control, Model, and (Lipo+M)@E group. Data are means ± SD, n = 4, #P < 0.05, ##P < 0.01, ###P < 0.001 vs. the Control. **P < 0.01, ***P < 0.001 vs. the Model. Fig. S13 Effect of (Lipo+M)@E NPs on serum TC and TG levels in ApoE-/- mice. Data are means ± SD, n = 5, ###P < 0.001 vs. the Control. ***P < 0.001 vs. the Model. Fig. S14 Proposed mechanism of (Lipo+M)@E NPs in attenuating atherosclerosis. (Lipo+M)@E NPs alleviate atherosclerosis in vivo, due to reduce PCSK9, decrease LDLR degradation, while regulating gut microbiota, bile acids, and cholesterol metabolism. Fig. S15 In vitro biocompatibility. (A) Hemolysis of RBCs at various concentrations of different materials. (B) The microscopy image of the hemolytic test, [Lipo]=3.6 μg/mL. Scale bars = 20 µm. (C) Platelet activation assay with different formulations. [Lipo]=3.6 μg/mL. (D) In vitro cytotoxicity evaluation of (Lipo+M)@E NPs. Fig. S16 In vivo security assessment. (A) H&E-stained images of the heart, liver, spleen, lung, and kidney of ApoE-/- mice with different treatments. Scale bars = 100 µm. (B&C) Blood routine and liver-kidney assays. n = 5

A Coumarin-Based Array for the Discrimination of Amyloids

Self-assembly of misfolded proteins can lead to the formation of amyloids, which are implicated in the onset of many pathologies including Alzheimer’s disease and Parkinson’s disease. The facile detection and discrimination of different amyloids are crucial for early diagnosis of amyloid-related pathologies. Here, we report the development of a fluorescent coumarin-based two-sensor array that is able to correctly discriminate between four different amyloids implicated in amyloid-related pathologies with 100% classification. The array was also applied to mouse models of Alzheimer’s disease and was able to discriminate between samples from mice corresponding to early (6 months) and advanced (12 months) stages of Alzheimer’s disease. Finally, the flexibility of the array was assessed by expanding the analytes to include functional amyloids. The same two-sensor array was able to correctly discriminate between eight different disease-associated and functional amyloids with 100% classification

Additional file 1 of Synergistic dual cell therapy for atherosclerosis regression: ROS-responsive Bio-liposomes co-loaded with Geniposide and Emodin

Supplementary Material