Additional file 1 of Low postoperative blood platelet count may be a risk factor for 3-year mortality in patients with acute type A aortic dissection

Additional file 1: Table 1. Diagnostic criteria for each postoperative complications. Table 2. The results of univariate Logistic regression

Table_1_v1_High-Dose Tranexamic Acid in Patients Underwent Surgical Repair of Aortic Dissection Might Reduce Postoperative Blood Loss: A Cohort Analysis.docx

IntroductionWhile tranexamic acid (TXA) is widely used in patients with acute type A aortic dissection (ATAAD) who undergo surgical repair to reduce blood loss and transfusion requirement, the optimal dosage of TXA is unknown in these patients.Materials and MethodsThis was a retrospective cohort study that compared high-dose (>50 mg/kg) and low-dose TXA (≤50 mg/kg) in patients with ATAAD who underwent surgical repair. Propensity score matching (PSM) was performed between the two groups and results were analyzed in matched cases. The primary outcome was postoperative blood loss within 3 days after surgery. The secondary outcomes were total blood loss after surgery and perioperative blood transfusion, and safety outcomes were also assessed.ResultsThrough medical record screening, 529 patients were identified. After PSM, 196 patients in the high-dose group and 196 patients in the low-dose group were matched and included in the final analysis. Postoperative blood loss in 3 days after surgery was 940 mL (710–1,010 mL) in the low-dose group and 695 mL (620–860 mL) in the high-dose group. The difference was statistically significant (P ConclusionIn ATAAD patients who underwent surgical repair, high-dose TXA significantly reduced postoperative blood loss compared to low-dose TXA, while no difference in transfusion or adverse events was found.</p

DataSheet_3_The molecular mechanism underlying dermatomyositis related interstitial lung disease: evidence from bioinformatic analysis and in vivo validation.xlsx

BackgroundDermatomyositis (DM) is an autoimmune and inflammatory disease that can affect the lungs, causing interstitial lung diseases (ILD). However, the exact pathophysiological mechanisms underlying DM-ILD are unknown. Idiopathic pulmonary fibrosis (IPF) belongs to the broader spectrum of ILD and evidence shows that common pathologic pathways might lie between IPF and DM-ILD.MethodsWe retrieved gene expression profiles of DM and IPF from the Gene Expression Omnibus (GEO) and utilized weighted gene co-expression network analysis (WGCNA) to reveal their co-expression modules. We then performed a differentially expressed gene (DEG) analysis to identify common DEGs. Enrichment analyses were employed to uncover the hidden biological pathways. Additionally, we conducted protein-protein interaction (PPI) networks analysis, cluster analysis, and successfully found the hub genes, whose levels were further validated in DM-ILD patients. We also examined the relationship between hub genes and immune cell abundance in DM and IPF. Finally, we conducted a common transcription factors (TFs)-genes network by NetworkAnalyst.ResultsWGCNA revealed 258 intersecting genes, while DEG analysis identified 66 shared genes in DM and IPF. All of these genes were closely related to extracellular matrix and structure, cell-substrate adhesion, and collagen metabolism. Four hub genes (POSTN, THBS2, COL6A1, and LOXL1) were derived through intersecting the top 30 genes of the WGCNA and DEG sets. They were validated as active transcripts and showed diagnostic values for DM and IPF. However, ssGSEA revealed distinct infiltration patterns in DM and IPF. These four genes all showed a positive correlation with immune cells abundance in DM, but not in IPF. Finally, we identified one possible key transcription factor, MYC, that interact with all four hub genes.ConclusionThrough bioinformatics analysis, we identified common hub genes and shared molecular pathways underlying DM and IPF, which provides valuable insights into the intricate mechanisms of these diseases and offers potential targets for diagnostic and therapeutic interventions.</p

DataSheet_1_The molecular mechanism underlying dermatomyositis related interstitial lung disease: evidence from bioinformatic analysis and in vivo validation.docx

BackgroundDermatomyositis (DM) is an autoimmune and inflammatory disease that can affect the lungs, causing interstitial lung diseases (ILD). However, the exact pathophysiological mechanisms underlying DM-ILD are unknown. Idiopathic pulmonary fibrosis (IPF) belongs to the broader spectrum of ILD and evidence shows that common pathologic pathways might lie between IPF and DM-ILD.MethodsWe retrieved gene expression profiles of DM and IPF from the Gene Expression Omnibus (GEO) and utilized weighted gene co-expression network analysis (WGCNA) to reveal their co-expression modules. We then performed a differentially expressed gene (DEG) analysis to identify common DEGs. Enrichment analyses were employed to uncover the hidden biological pathways. Additionally, we conducted protein-protein interaction (PPI) networks analysis, cluster analysis, and successfully found the hub genes, whose levels were further validated in DM-ILD patients. We also examined the relationship between hub genes and immune cell abundance in DM and IPF. Finally, we conducted a common transcription factors (TFs)-genes network by NetworkAnalyst.ResultsWGCNA revealed 258 intersecting genes, while DEG analysis identified 66 shared genes in DM and IPF. All of these genes were closely related to extracellular matrix and structure, cell-substrate adhesion, and collagen metabolism. Four hub genes (POSTN, THBS2, COL6A1, and LOXL1) were derived through intersecting the top 30 genes of the WGCNA and DEG sets. They were validated as active transcripts and showed diagnostic values for DM and IPF. However, ssGSEA revealed distinct infiltration patterns in DM and IPF. These four genes all showed a positive correlation with immune cells abundance in DM, but not in IPF. Finally, we identified one possible key transcription factor, MYC, that interact with all four hub genes.ConclusionThrough bioinformatics analysis, we identified common hub genes and shared molecular pathways underlying DM and IPF, which provides valuable insights into the intricate mechanisms of these diseases and offers potential targets for diagnostic and therapeutic interventions.</p

DataSheet_2_The molecular mechanism underlying dermatomyositis related interstitial lung disease: evidence from bioinformatic analysis and in vivo validation.pdf

BackgroundDermatomyositis (DM) is an autoimmune and inflammatory disease that can affect the lungs, causing interstitial lung diseases (ILD). However, the exact pathophysiological mechanisms underlying DM-ILD are unknown. Idiopathic pulmonary fibrosis (IPF) belongs to the broader spectrum of ILD and evidence shows that common pathologic pathways might lie between IPF and DM-ILD.MethodsWe retrieved gene expression profiles of DM and IPF from the Gene Expression Omnibus (GEO) and utilized weighted gene co-expression network analysis (WGCNA) to reveal their co-expression modules. We then performed a differentially expressed gene (DEG) analysis to identify common DEGs. Enrichment analyses were employed to uncover the hidden biological pathways. Additionally, we conducted protein-protein interaction (PPI) networks analysis, cluster analysis, and successfully found the hub genes, whose levels were further validated in DM-ILD patients. We also examined the relationship between hub genes and immune cell abundance in DM and IPF. Finally, we conducted a common transcription factors (TFs)-genes network by NetworkAnalyst.ResultsWGCNA revealed 258 intersecting genes, while DEG analysis identified 66 shared genes in DM and IPF. All of these genes were closely related to extracellular matrix and structure, cell-substrate adhesion, and collagen metabolism. Four hub genes (POSTN, THBS2, COL6A1, and LOXL1) were derived through intersecting the top 30 genes of the WGCNA and DEG sets. They were validated as active transcripts and showed diagnostic values for DM and IPF. However, ssGSEA revealed distinct infiltration patterns in DM and IPF. These four genes all showed a positive correlation with immune cells abundance in DM, but not in IPF. Finally, we identified one possible key transcription factor, MYC, that interact with all four hub genes.ConclusionThrough bioinformatics analysis, we identified common hub genes and shared molecular pathways underlying DM and IPF, which provides valuable insights into the intricate mechanisms of these diseases and offers potential targets for diagnostic and therapeutic interventions.</p

Additional file 1 of The salutary effect of peritoneal dialysis catheters on enhanced recovery among high-risk pediatric patients undergoing the left coronary transfer procedure: a cohort study

Additional file