Table 1_DNase I alleviates renal inflammatory injury in MRL/lpr mice by inhibiting NETs formation.docx by Manling Zhang (566460)

“…Furthermore, the critical molecules PADI4, HMGB1, TLR4, and MYD88 were significantly upregulated in peripheral blood neutrophils from LN patients, and their expression levels in the kidneys of MRL/lpr mice increased in a time-dependent manner.…”

Other differentially-accumulated metabolites in <i>P</i>. <i>fijiensis</i> exposed to thiabendazole. by María Gabriela Maridueña-Zavala (3377081)

“…Values represent the means and standard errors of 5 replicates. Different letters represent significant differences (p < 0.05).…”

Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis by weiwei zheng (11880230)

“…Obvious histopathological differences were detected in resistant and susceptible individuals in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing was performed to characterize the changes in intestinal microbial community, in which Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. RNA-seq analysis indicated that 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than 310 in the resistant group, when compared to the control group. …”

Data Sheet 1_Water quality and fluorescent dissolved organic matter dynamics of Dhaleshwari River.pdf by Mohammad Mohinuzzaman (18275214)

“…Relatively higher FDOM levels were found in the upstream area of the river compared to the downstream part. Furthermore, we found that with the increase in the river depth, the FDOM substances decreased due to the absorption of sunlight, microbial degradation, and photosynthesis. …”

Supplementary file 1_Comprehensive real-time metabolic profiling of peripheral blood mononuclear cells reveals important methodological considerations for immunometabolism research... by Michael Macleod (22516823)

“…Extended blood processing time significantly lowered T cell activation capacity in PBMCs, evidenced by decreased responses of mitochondrial and glycolytic ATP supply to CD3/CD28 activation.…”

Table1_Cover crops lower the dispersal of grapevine foliar pathogens from the ground and contribute to early-season disease management.docx by Gultekin Hasanaliyeva (17752296)

“…In two organic vineyards, for 2 years, fall- (mixture of Lolium perenne, Onobrychis viciifolia, and Trifolium repens) or spring-sown (a mixture of Vicia sativa and Sinapis sp.) cover crops could significantly delay (by 14–30 days) and reduce (till >90%) the development of downy and powdery mildew epidemics. …”

Data Sheet 1_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Table 4_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Table 1_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Table 5_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Table 2_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Table 3_Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel... by Zachary D. Wallen (19986522)

“…Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.…”

Image 3_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.tif by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Image 2_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.tif by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Image 1_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.tif by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Table 2_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.xlsx by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Image 4_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.tif by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Table 1_Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.xlsx by Rong Su (2210740)

“…Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. …”

Table 1_Physical and chemical characteristics of pyrogenic carbon from peatland vegetation fires differ across burn severities.xlsx by Oscar J. Kennedy-Blundell (20506349)

“…</p>Results<p>While there was a good degree of similarity between samples within each severity group, there were significant differences between severity groups. …”

Behavioral characterization of prenatal nicotine exposure (PNE) and control mice. by Rodrigo Vidal (4177060)

“…Bottom, PNE mice showed a decreased recognition index percentage compared to control mice (PNE: 24.00 ± 1.03%, n = 6; Control: 53.17 ± 4.09%, n = 6), t = 6,921 df = 10, p < 0,0001. …”