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. …”

Inc exhibits key properties of a Cul3 adaptor in neurons in vivo. by Qiuling Li (390393)

“…Neuronal <i>Cul3</i> RNAi decreases <i>Cul3</i> transcript levels but does not significantly alter those of <i>inc</i>. …”

Figure 4 from Impact of Glucocorticoids on Immune Checkpoint Inhibitor Efficacy and Circulating Biomarkers in Non–Small Cell Lung Cancer Patients by Lauren Polyakov (21672045)

“…Ab, antibody; dex, dexamethasone; i.p., intraperitoneal; NS, not significant; αPD-L1, anti–PD-L1.</p>…”

Figure 4 from OATP1B-type Transport Function Is a Determinant of Aromatase Inhibitor–Associated Arthralgia Susceptibility by Hanieh Taheri (20944284)

“…Two-sided, unpaired Mann–Whitney–Wilcoxon rank tests were used to analyze the data and no multiple comparison adjustment was applied. NS, non-significant; <i>P</i> = 0.279 and *, <i>P</i> < 0.05 compared with the wild-type group.…”

Figure 5 from Elraglusib Induces Cytotoxicity via Direct Microtubule Destabilization Independently of GSK3 Inhibition by Josh T. Coats (20321355)

“…Fold change calculated vs. control, error bars represent SEM, and statistical significance was determined using one-way ANOVA, with <i>P</i> ≤ 0.05. ns, not significant; *, <i>P</i> < 0.05; ****, <i>P</i> < 0.0001.…”

Figure 4 from Functional Profiling of p53 and RB Cell Cycle Regulatory Proficiency Suggests Mechanism-Driven Molecular Stratification in Endometrial Carcinoma by Zelei Yang (18491142)

“…For comparisons indicated by brackets over the treatment groups being compared, * = <i>P</i> < 0.05 and NS = not significant by an ordinary one-way ANOVA with Šídák’s multiple comparisons test. …”

Table 1_ddPCR Enhances early diagnosis, treatment, prognosis, and pathogen verification in elderly BSI.docx by Jiayi Peng (7312952)

“…When detecting Streptococcus, coagulase-negative Staphylococci (CoNS), Acinetobacter baumannii complex, and Candida, diagnostic thresholds of 132.55, 182.70/262.24, and 174.78 copies/mL, respectively, were established to help differentiate false-positive results.…”

PVD neurons show reduced lysosome function during aging and in the <i>hlh-30</i> mutant. by Ruiling Zhong (22339779)

“…****<i>P</i> < 0.0001, **<i>P</i> < 0.01, ns: not significant, One-way ANOVA with Tukey post-test. …”

Visual surround network activation modulates center excitability in the SCs. by Peng Cui (33358)

“…Statistical testing was done using <i>t</i> test followed by Wilcoxon rank-signed test (** <i>p</i> < 0.01, *** <i>p</i> < 0.001; ns, no significance). The data underlying this figure can be found in the Supplementary section.…”

Neurochemical identity of dFB^23E10Ո^84C10 neurons. by Joseph D. Jones (13883524)

“…Two-way ANOVA followed by Sidak’s multiple comparisons revealed that activating 84C10-AD; ChAT-DBD neurons significantly increases sleep. n.s. = not significant, ****<i>P</i> < 0.0001. …”

The impact of nano-curcumin on cell viability and proliferation in breast cancer cells. by Taraneh Givi (22793530)

“…Statistical analysis was performed using a one-way ANOVA or student t-test, with means ± SEM presented. ns = not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.…”

miR-155 directly targets the 3′UTR of TRAF3 and STAT1. by Xinyue Zhang (271120)

“…The levels of TRAF3 and STAT1 protein were detected by Western blotting. **, P < 0.01. ns, P > 0.05, no significant difference.</p>…”

ZNF503-AS2 had predictive value for chemotherapy and immunotherapy in glioma. by Yibo Wu (788885)

“…(M-O) ZNF503-AS2 was a prognostic risk factor in SKCM (M, N) and NSCLC (O), and patients with low ZNF503-AS2 expression responded better to immunotherapy. NS, not statistically significant; * <i>P</i> < 0.05; ** <i>P</i> < 0.01; *** <i>P</i> < 0.001.…”

Rescue of <i>pbp1</i>-KD phenotypes using CRISPRi trans-complementation. by Gregory A. Harrison (16639446)

“…The mean and standard deviation were calculated across replicates; statistical significance was determined by a one-way ANOVA with Tukey’s multiple comparisons test. ns, not significant; **** p < 0.0001.…”

CCDC66 localizes to the axoneme and regulates primary cilium stability but not cilia formation in IMCD3 cells. by Jovana Deretic (13113617)

“…(Welch’s <i>t</i> test, ns: not significant) <b>(D)</b> Quantification of cilia length in 3D, according to ARL13b signal (co-localizing with acetylated-tubulin) from <b>(B)</b>. …”

Glial 5-HT_2a receptors are necessary and rate limiting for experience-dependent synaptic glomeruli pruning. by Vanessa Kay Miller (19775496)

“…All the individual data points are shown with mean ± SEM. Significance is indicated as <i>p</i> < 0.0001 (****) and <i>p</i> > 0.05 (not significant, ns). …”