Table 3_Global, region and country burden of osteoarthritis at different sites in middle-aged and elderly populations from 1990 to 2021: a systematic analysis of the 2021 global bu... by Guoxin Huang (5775458)

“…From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.…”

Table 1_Global, region and country burden of osteoarthritis at different sites in middle-aged and elderly populations from 1990 to 2021: a systematic analysis of the 2021 global bu... by Guoxin Huang (5775458)

“…From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.…”

Table 5_Global, region and country burden of osteoarthritis at different sites in middle-aged and elderly populations from 1990 to 2021: a systematic analysis of the 2021 global bu... by Guoxin Huang (5775458)

“…From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.…”

Table 2_Global, region and country burden of osteoarthritis at different sites in middle-aged and elderly populations from 1990 to 2021: a systematic analysis of the 2021 global bu... by Guoxin Huang (5775458)

“…From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.…”

Image 4_Unraveling impact and potential mechanisms of baseline pain on efficacy of immunotherapy in lung cancer patients: a retrospective and bioinformatic analysis.jpg by Zexin Zhang (7507979)

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 2_Unraveling impact and potential mechanisms of baseline pain on efficacy of immunotherapy in lung cancer patients: a retrospective and bioinformatic analysis.jpg by Zexin Zhang (7507979)

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 1_Unraveling impact and potential mechanisms of baseline pain on efficacy of immunotherapy in lung cancer patients: a retrospective and bioinformatic analysis.jpg by Zexin Zhang (7507979)

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. …”