Image 9_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.jpeg by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Data Sheet 1_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.pdf by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Image 2_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.jpeg by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Image 13_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.jpeg by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Image 1_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.tif by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Image 14_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.jpeg by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Image 12_RBM15-mediated metabolic reprogramming boosts immune response in colorectal cancer.jpeg by Chen Wang (88408)

“…</p>Methods<p>To identify potential regulators of immunotherapy response, we conducted a comprehensive analysis of colorectal cancer datasets from The Cancer Genome Atlas (TCGA). …”

Tetraspanins variably affect the virulence of <i>E</i>. <i>histolytica</i>. by Han Jiang (195350)

Table 1_The impact of IL-17A inhibitors on scalp and gut microbiota in psoriasis.docx by Wenxia Huang (12290795)

“…Moreover, the abundance of Roseburia, Megamonas, and the phylum Bacteroidota increased, although the Firmicutes/Bacteroidota (F/B) ratio showed no significant change. Conclusion: IL-17A inhibitor therapy has the potential to improve the structure and diversity of the scalp microbiome, gradually restoring it toward a healthier state while also enhancing gut microbiota diversity. …”

Data Sheet 1_Unveiling frailty: comprehensive and sex-specific characterization in prematurely aging PolgA mice.pdf by Dilara Yılmaz (19710658)

“…We demonstrated that frailty became evident in PolgA mice around 40 weeks, validated through body weight loss, reduced walking speed, decreased physical activity, and weaker grip strength. …”

Macular regions analyzed by spectral-domain optical coherence tomography scan in the retinal thickness map analysis. by Jung-Tae Kim (8997767)

Schematic presentation <i>M</i>. <i>bovis</i> PG45 5′ nucleotidase and phylogenetic tree of its homologs. by Aga E. Gelgie (17661720)

Oligonucleotides used in this study. by Aga E. Gelgie (17661720)

“…In comparison to the parent strain, the 0690::Tn mutant displayed markedly reduced fitness, as evidenced by a significant decrease or even absence in post-challenge mycoplasma counts in murine and cow mammary tissues, respectively. …”

Data supporting the findings of this study. by Aga E. Gelgie (17661720)

“…In comparison to the parent strain, the 0690::Tn mutant displayed markedly reduced fitness, as evidenced by a significant decrease or even absence in post-challenge mycoplasma counts in murine and cow mammary tissues, respectively. …”

<i>M</i>. <i>bovis</i> 0690::Tn mutant failed to elicit the recruitment of neutrophils into milk spaces. by Aga E. Gelgie (17661720)

Baseline demographic characteristics. by Jung-Tae Kim (8997767)

“…Group 2 exhibited a significantly greater decrease in GCC thickness compared with Group 1(P = 0.026).…”

I Optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

ANOVA for I optimal design of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

The kinetic release data of the optimized formula compared to drug suspension. by Randa Mohammed Zaki (21637175)

Composition of different formulations with their responses in I optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)