Objective:To investigate the effects of Roxadustat and recombinant human erythropoietin (rHuEPO) on novel inflammatory immune indices in anemic patients with non-dialysis type 2 diabetes mellitus combined with chronic kidney disease (CKD).Methods:A retrospective case-control study was performed in this study. Patients with non-dialysis type 2 diabetes mellitus combined with CKD admitted to Shanghai First People's Hospital from December 2015 to December 2023 were selected. Among those patients, 252 cases were treated with rHuEPO (rHuEPO group) and 103 cases were treated with Roxadustat (Roxadustat group). Both group had a course of treatment over three months. The baseline data and novel inflammatory immunity indices, systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) were compared between the two groups of patients before and after 3 months of treatment.Results:The differences in gender, age, body mass index, systolic blood pressure, diastolic blood pressure, history of cardiovascular and cerebrovascular disease, history of hypertension, estimated glomerular filtration rate, and use of hypoglycemic, antihypertensive, and lipid-lowering medications were not statistically significant when compared between the two groups of patients (all P>0.05). Before treatment, the differences in NLR, PLR, SII, and LMR between the two groups were not statistically significant (all P>0.05); after 3 months of treatment, NLR, PLR, SII, and LMR were lower in both groups than before treatment [rHuEPO group: (2.3±0.8)% vs. (2.8±0.8)%, (83±33)% vs. (160±49)%, (2.3±0.8)% vs. (3.1±0.7)%, (476±227)% vs. (594±243)%, with t values of 9.25, 23. 20, 26.67, and 9.62, respectively, all P<0.001; Roxadustat group: (1.7±0.6)% versus (2.9±1.0)%, (72±30)% versus (162±47)%, (2.0±0.8)% versus (3.1±0.9)%, (408±151)% versus (605±267)%, with t values of 8. 38, 14.27, 8.62, and 5.97, respectively, all P<0.001], and NLR, PLR, and SII were lower in the Roxadustat group than in the rHuEPO group ( t=5.00, P<0.001, t=2.44, P=0.015, t=2.09, P=0.040). Conclusion:In patients with anemia in non-dialysis type 2 diabetes mellitus associated with CKD,Roxadustat had similar ability of reducing the level of novel inflammatory markers compared to rHuEPO.

Objective:To investigate the effects of Roxadustat and recombinant human erythropoietin (rHuEPO) on novel inflammatory immune indices in anemic patients with non-dialysis type 2 diabetes mellitus combined with chronic kidney disease (CKD).Methods:A retrospective case-control study was performed in this study. Patients with non-dialysis type 2 diabetes mellitus combined with CKD admitted to Shanghai First People's Hospital from December 2015 to December 2023 were selected. Among those patients, 252 cases were treated with rHuEPO (rHuEPO group) and 103 cases were treated with Roxadustat (Roxadustat group). Both group had a course of treatment over three months. The baseline data and novel inflammatory immunity indices, systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) were compared between the two groups of patients before and after 3 months of treatment.Results:The differences in gender, age, body mass index, systolic blood pressure, diastolic blood pressure, history of cardiovascular and cerebrovascular disease, history of hypertension, estimated glomerular filtration rate, and use of hypoglycemic, antihypertensive, and lipid-lowering medications were not statistically significant when compared between the two groups of patients (all P>0.05). Before treatment, the differences in NLR, PLR, SII, and LMR between the two groups were not statistically significant (all P>0.05); after 3 months of treatment, NLR, PLR, SII, and LMR were lower in both groups than before treatment [rHuEPO group: (2.3±0.8)% vs. (2.8±0.8)%, (83±33)% vs. (160±49)%, (2.3±0.8)% vs. (3.1±0.7)%, (476±227)% vs. (594±243)%, with t values of 9.25, 23. 20, 26.67, and 9.62, respectively, all P<0.001; Roxadustat group: (1.7±0.6)% versus (2.9±1.0)%, (72±30)% versus (162±47)%, (2.0±0.8)% versus (3.1±0.9)%, (408±151)% versus (605±267)%, with t values of 8. 38, 14.27, 8.62, and 5.97, respectively, all P<0.001], and NLR, PLR, and SII were lower in the Roxadustat group than in the rHuEPO group ( t=5.00, P<0.001, t=2.44, P=0.015, t=2.09, P=0.040). Conclusion:In patients with anemia in non-dialysis type 2 diabetes mellitus associated with CKD,Roxadustat had similar ability of reducing the level of novel inflammatory markers compared to rHuEPO.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease bio-markers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package:TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages:1) a user-friendly interface and real-time co-expression network mining through a web server;2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene ex-pression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options;4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits;5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools;and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL:https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.