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Yiying Liu,1,&ast; Xin Wang,2,&ast; Linying Wang,1 Wenwen Chen,3 Wenyue Liu,1 Tingting Ye,1 Jing Hong,1 Hong Zhu,1 Feixia Shen1 1Department of Endocrinology and Metabolism, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 2Department of Urology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, People’s Republic of China; 3Department of Senile Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Feixia ShenDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of ChinaEmail shenfeixia@wmu.edu.cnPurpose: Diabetic neurogenic bladder (DNB) has been widely recognized in recent years. It is common in patients with long-term diabetes and may also lead to many severe complications. Although there has been widespread evidence that inflammation is involved in the development of some diabetic complications, there is little evidence that this can also occur in the bladder. In recent years, platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) have been viewed as potential novel markers of inflammatory responses. This study was designed to evaluate the relationship between the presence of DNB and the PLR and NLR.Patients and Methods: A total of 371 cases of T2DM patients were included in this retrospective study. Patients were divided into two groups, with 115 diabetic subjects diagnosed with diabetic neurogenic bladder and 256 control subjects without DNB. The independent predictors of DNB were analyzed using logistic regression.Results: Compared with patients without DNB, the mean PLR and NLR were significantly higher in those with DNB (p < 0.001). Based on the logistic regression, PLR was found to be an independent risk factor for DNB (odds ratio [OR]: 1.408, 95% confidence interval [CI]: 1.248– 1.617). From the receiver operating characteristic (ROC) curve, using PLR as indicative of DNB was expected to be 101.1949, and it generated a sensitivity and specificity value of 89.6% and 23.4%, respectively. The area under the curve (AUC) was also found to be 0.899 (95% CI: 0.865– 0.932).Conclusion: In our study, PLR and NLR were significantly higher for patients with DNB. The PLR was found to be a risk factor in the presence of DNB after correcting for possible confounding factors. Considering the severe complications associated with DNB, patients with elevated PLR should be seriously cared for in clinics.Keywords: diabetic autonomic neuropathy, neurogenic bladder, oxidative stress, inflammatory response

BackgroundSynovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.MethodsThe OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.ResultsOA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.ConclusionsWe developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.

Megakaryocytes (MKs) and their progeny platelets function in a variety of biological processes including coagulation, hemostasis, inflammation, angiogenesis, and innate immunity. However, the divergent developmental and cellular landscape of adult MKs remains mysterious. Here, by deriving the single‐cell transcriptomic profiling of MKs from human adult bone marrow (BM), cellular heterogeneity within MKs is unveiled and an MK subpopulation with high enrichment of immune‐associated genes is identified. By performing the dynamic single‐cell transcriptomic landscape of human megakaryopoiesis in vitro, it is found that the immune signatures of MKs can be traced back to the progenitor stage. Furthermore, two surface markers, CD148 and CD48, are identified for mature MKs with immune characteristics. At the functional level, these CD148+CD48+ MKs can respond rapidly to immune stimuli both in vitro and in vivo, exhibit high‐level expression of immune receptors and mediators, and may function as immune‐surveillance cells. The findings uncover the cellular heterogeneity and a novel immune subset of human adult MKs and should greatly facilitate the understanding of the divergent functions of MKs under physiological and pathological conditions., By performing single‐cell transcriptomic analysis of human megakaryocytes from native bone marrow, the megakaryocyte heterogeneity is revealed, an immune subpopulation marked by CD148 and CD48 is discovered, and the potential function of immune megakaryocytes as immune‐surveillance cells is elucidated.

Additional file 1. Detailed methods of genetic tests for the patient.

Additional file 1. Supplementary Table. Driver gene mutation and mutation-allele frequency in tumor core, tumor edge, and para-tumor tissues. Supplementary Figure. DNA variation of the tissue from TC, TE, P5, P10, P15, P20 and PN samples.