Additional file 1: Figure S1. Chromatogram of top 10 metabolites in the targeted metabolomics analysis. Figure S2. The correlation between the predictive metabolites of CAS [PE (20:2/16:0), PG (18:0/20:4) and de novo lipogenesis (16:0/18:2n-6)] and metabolic index among nonobese MAFLD. Group 1: nonobese MAFLD patients without CAS; Group 2: nonobese MAFLD patients with CAS. Figure S3. The correlation between the predictive metabolites of CAS [PE (20:2/16:0), PG (18:0/20:4) and de novo lipogenesis (16:0/18:2n-6)] and pathological...

Additional file 2: Fig S1. Fragment size analysis of DNA isolated from medium of HEK293 cells and adult mouse liver cells. a The h293-s1 was the library for h293_s1_GL and h293_s1_NV. The h293-s2 was the library for h293_s2_GL and h293_s2_NV. b The mouse-s1 was the library for mouse_s1_GL and mouse_s1_NV. The mouse-s2 was the library for mouse_s2_GL and mouse_s2_NV.

Additional file 4: Fig S2. The M bias of h293 samples in pro-cleansing data and post-cleansing data.

Additional file 9: Fig S5. The methylation pattern of GNB1 genebody 40 loci.

Additional file 4. Review history.

Additional file 3. Supplemental tables. Table S1. The univariate analysis of lipid metabolism biomarkers for OS; Table S2. Multicollinearity test of 5 variables for OS; Table S3. The baseline data of the training cohort and the validation cohort.

Additional file 1: Supplementary Table S1. The sequences of promoter PRKU6a and promoter PRKU6b. Supplementary Table S2. The sequence of codon-optimized Cas9-NLS-6*His. Table S3. The sequence of sgRNA of RKCrtYB used in the present study. Supplementary Table S4. Primers used in the present study.

Additional file 5: Supplementary table 1. The clinical characteristics of TCGA and ICGC cohorts.

Additional file 6: Supplementary table 2. The clinical characteristics of GSE14520 and 116174 cohorts.

Additional file 8: Supplementary Table 4. The primer lists.

Additional file 9: Supplementary Table 5. The specific sequences of sh-DLAT and OE-DLAT.

Additional file 1: Figure S1. The workflow of our work.

Additional file 3: Figure S4. Characteristics of chemokines, interleukins, interferons, and other cytokines among the threedistinct pyroptosis gene clusters.

Additional file 4: Figure S4. Characteristics of chemokines, interleukins, interferons, and other cytokines among the threedistinct pyroptosis gene clusters.

Additional file 5: Figure S5. Comprehensive analysis of Pyrsig score in OC.

Additional file 8: Figure S8. Relationships between immune checkpoints and GSDMD, GZMB and their prognostic values.

Additional file 8: Figure S8. Relationships between immune checkpoints and GSDMD, GZMB and their prognostic values.

Additional file 5.

Additional file 1: Methods. Supplementary figures and legends. Table S1: Basic information of enrolled patients. Table S2: Brief summary of all participants. Table S13: Performance of single mutation site in the diagnosis of HCC.

Additional file 2: Fig. S1. Proportions of concordance/discordance among individuals according to LDL-C clinical cutpoints. Fig. S2. Progression trajectories of separate baPWV and ABI. Fig. S3. The scatter plots between remnant cholesterol and LDL cholesterol stratified by age and sex.

Additional file 1: Supplemental table 1. Multivariate logistic analysis of risk factors associated with progressive hemorrhagic injury after traumatic brain injury*. Supplemental table 2. Multivariate logistic analysis of risk factors associated with progressive intraparenchymal contusion or hematoma*. Supplemental table 3. Multivariate logistic analysis of risk factors associated with progressive epidural hematoma*. Supplemental table 4. Subtypes of progressive hemorrhagic injury (PHI) and their outcomes after propensity score matching (PSM).

Additional file 1: Supplemental table 1. Multivariate logistic analysis of risk factors associated with progressive hemorrhagic injury after traumatic brain injury*. Supplemental table 2. Multivariate logistic analysis of risk factors associated with progressive intraparenchymal contusion or hematoma*. Supplemental table 3. Multivariate logistic analysis of risk factors associated with progressive epidural hematoma*. Supplemental table 4. Subtypes of progressive hemorrhagic injury (PHI) and their outcomes after propensity score matching (PSM).

Additional file 4: Fig. S4. Negligible effect of ADSC-NVs on cell senescence in vitro. HUVECs were exposed to H2O2 (50 μM, 2 h) to induce senescence and then incubated with 0 μg/mL or 20 μg/mL ADSC-NVs for 24 h. (A) Representative images of SA β-gal staining of HUVECs. Scale bar: 100 μm. (B) Quantitation of SA β-gal-positive HUVECs (n = 3 per group). Besides, HUVECs cultured in complete medium were incubated with or without 20...

Additional file 5: Fig. S5. Fat graft with ADSC-EVs. BALB/c nude mice were administered injection of a mixture of fat granules and ADSC-EVs (20 μg/mL). The grafts were harvested at 12 weeks post-transplantation for further analysis. (A) Macroscopic images of the fat grafts. Scale bar: 1 cm. Quantitative analysis of grafts shows no significant difference of volume (B) and weight (C) between ADSC-NVs (20 μg/mL) group and ADSC-EVs (20 μg/mL) group (n = 5 per...

Structurally diverse biflavonoids have attracted significant research interest for drug discovery over past decades. Biflavonoid oriented phytochemistry research on the stems of Daphne kiusiana var. atrocaulis (Rehd.) F. Maekawa was carried out, which resulted in the identification of ten major effective components (1–10), including the undescribed biflavonoids, daphnodorin Q (1), daphnodorin R (2) and flavane, daphnekiuslin A (10). The known structures were identified from this herb for the first time. Their structures were determined by...