Table 1_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.xls by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 13_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 1_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 12_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 11_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 2_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 10_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 9_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 3_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 7_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 6_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 8_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 5_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Image 4_Integrative multiomics analysis reveals association of gut microbiota and its metabolites with susceptibility to keloids.jpeg by Dang Li (16400478)

“…An area under the curve analysis using a random forest model based on fecal metagenomics, plasma metabolomics, and tissue metabolomics revealed that gut bacteria, plasma, and tissue metabolites were effective in distinguishing between MK and NS groups. Decreased Bacteroides plebeius could lower uracil levels, altering systemic lipid metabolism, which may change the metabolic phenotype of secretory reticular fibroblasts in wounds, potentially leading to MK. …”

Table_2_Receptor Tyrosine Kinase Inhibitor Sunitinib as Novel Immunotherapy to Inhibit Myeloid-Derived Suppressor Cells for Treatment of Endometriosis.xls by Ying He (125930)

“…We found that Sunitinib significantly decreased the endometriotic lesion size and weight after 1 and 3 weeks, and decreased p-STAT3 activation in MDSCs after 1 week of treatment. …”

Table_3_Receptor Tyrosine Kinase Inhibitor Sunitinib as Novel Immunotherapy to Inhibit Myeloid-Derived Suppressor Cells for Treatment of Endometriosis.xls by Ying He (125930)

“…We found that Sunitinib significantly decreased the endometriotic lesion size and weight after 1 and 3 weeks, and decreased p-STAT3 activation in MDSCs after 1 week of treatment. …”

Table 1_Anemoside B4 targets RAGE to attenuate ferroptosis in sepsis-induced acute lung injury.xlsx by Yue Bu (11357379)

Table 2_Anemoside B4 targets RAGE to attenuate ferroptosis in sepsis-induced acute lung injury.xlsx by Yue Bu (11357379)

Data Sheet 1_Global, regional, and national trends and burden of multiple sclerosis in adolescents and young adults: a data analysis from 1990 to 2021 and projections to 2040.zip by Peixi Zhao (620068)

Data Sheet 2_Global, regional, and national trends and burden of multiple sclerosis in adolescents and young adults: a data analysis from 1990 to 2021 and projections to 2040.docx by Peixi Zhao (620068)