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Background and hypothesisThe recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner.MethodsFrom the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model.ResultsThe 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis.ConclusionsThis study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.

Gender and biological sex have distinct impacts on the pathogenesis of type 2 diabetes (T2D). Estrogen deficiency is known to predispose female mice to T2D. In our previous study, we found that a high-fat, high-sucrose diet (HFHSD) induces T2D in male mice through the miR-10b-5p/KLF11/KIT pathway, but not in females, highlighting hormonal disparities in T2D susceptibility. However, the underlying molecular mechanisms of this hormonal protection in females remain elusive. To address this knowledge gap, we utilized ovariectomized, estrogen-deficient female mice, fed them a HFHSD to induce T2D, and investigated the molecular mechanisms involved in estrogen-deficient diabetic female mice, relevant cell lines, and female T2D patients. Initially, female mice fed a HFHSD exhibited a delayed onset of T2D, but ovariectomy-induced estrogen deficiency promptly precipitated T2D without delay. Intriguingly, insulin (INS) was upregulated, while insulin receptor (INSR) and protein kinase B (AKT) were downregulated in these estrogen-deficient diabetic female mice, indicating insulin-resistant T2D. These dysregulations of INS, INSR, and AKT were mediated by a miR-10a/b-5p-NCOR2 axis. Treatment with miR-10a/b-5p effectively alleviated hyperglycemia in estrogen-deficient T2D female mice, while β-estradiol temporarily reduced hyperglycemia. Consistent with the murine findings, plasma samples from female T2D patients exhibited significant reductions in miR-10a/b-5p, estrogen, and INSR, but increased insulin levels. Our findings suggest that estrogen protects against insulin-resistant T2D in females through miR-10a/b-5p/NCOR2 pathway, indicating the potential therapeutic benefits of miR-10a/b-5p restoration in female T2D management.

Gender and biological sex have distinct impacts on the pathogenesis of type 2 diabetes (T2D). Estrogen deficiency is known to predispose female mice to T2D. In our previous study, we found that a high-fat, high-sucrose diet (HFHSD) induces T2D in male mice through the miR-10b-5p/KLF11/KIT pathway, but not in females, highlighting hormonal disparities in T2D susceptibility. However, the underlying molecular mechanisms of this hormonal protection in females remain elusive. To address this knowledge gap, we utilized ovariectomized, estrogen-deficient female mice, fed them a HFHSD to induce T2D, and investigated the molecular mechanisms involved in estrogen-deficient diabetic female mice, relevant cell lines, and female T2D patients. Initially, female mice fed a HFHSD exhibited a delayed onset of T2D, but ovariectomy-induced estrogen deficiency promptly precipitated T2D without delay. Intriguingly, insulin (INS) was upregulated, while insulin receptor (INSR) and protein kinase B (AKT) were downregulated in these estrogen-deficient diabetic female mice, indicating insulin-resistant T2D. These dysregulations of INS, INSR, and AKT were mediated by a miR-10a/b-5p-NCOR2 axis. Treatment with miR-10a/b-5p effectively alleviated hyperglycemia in estrogen-deficient T2D female mice, while β-estradiol temporarily reduced hyperglycemia. Consistent with the murine findings, plasma samples from female T2D patients exhibited significant reductions in miR-10a/b-5p, estrogen, and INSR, but increased insulin levels. Our findings suggest that estrogen protects against insulin-resistant T2D in females through miR-10a/b-5p/NCOR2 pathway, indicating the potential therapeutic benefits of miR-10a/b-5p restoration in female T2D management. [ABSTRACT FROM AUTHOR]

Abstract Practical methods to mitigate level and variation of passive intermodulation with respect to time and temperature of coaxial cavity type duplexer are proposed through a variety of experimental results. From the findings derived in this study, a stable passive intermodulation response with less than 1 dB variation was shown in the environment temperature range of −10–85 °C. The duplexer with time‐stable PIM response at room temperature had a stable PIM value even in the environment temperature range.

Subepithelial platelet-derived growth factor receptor alpha (PDGFRα)+ cells found in the colonic mucosal tissue come in close contact with epithelial cells, immune cells, neurons, capillaries, and lymphatic networks. Mucosal subepithelial PDGFRα+ cells (MuPαC) are important regulators in various intestinal diseases including fibrosis and inflammation. However, the transcriptome of MuPαC has not yet been elucidated. Using Pdgfra-eGFP mice and flow cytometry, we isolated colonic MuPαC and obtained their transcriptome data. In analyzing the transcriptome, we identified three novel, and selectively expressed, markers (Adamdec1, Fin1, and Col6a4) found in MuPαC. In addition, we identified a unique set of MuPαC-enriched genetic signatures including groups of growth factors, transcription factors, gap junction proteins, extracellular proteins, receptors, cytokines, protein kinases, phosphatases, and peptidases. These selective groups of genetic signatures are linked to the unique cellular identity and function of MuPαC. Furthermore, we have added this MuPαC transcriptome data to our Smooth Muscle Genome Browser that contains the transcriptome data of jejunal and colonic smooth muscle cells (SMC), interstitial cells of Cajal (ICC), and smooth muscle resident PDGFRα+ cells: (https://med.unr.edu/physio/transcriptome). This online resource provides a comprehensive reference of all currently known genetic transcripts expressed in primary MuPαC in the colon along with smooth muscle resident PDGFRα cells, SMC, and ICC in the murine colon and jejunum.

Metalloendopeptidase ADAM-Like Decysin 1 (ADAMDEC1) is an anti-inflammatory peptidase that is almost exclusively expressed in the gastrointestinal (GI) tract. We have recently found abundant and selective expression of Adamdec1 in colonic mucosal PDGFRα+ cells. However, the cellular origin for this gene expression is controversial as it is also known to be expressed in intestinal macrophages. We found that Adamdec1 mRNAs were selectively expressed in colonic mucosal subepithelial PDGFRα+ cells. ADAMDEC1 protein was mainly released from PDGFRα+ cells and accumulated in the mucosal layer lamina propria space near the epithelial basement membrane. PDGFRα+ cells significantly overexpressed Adamdec1 mRNAs and protein in DSS-induced colitis mice. Adamdec1 was predominantly expressed in CD45− PDGFRα+ cells in DSS-induced colitis mice, with only minimal expression in CD45+ CD64+ macrophages. Additionally, overexpression of both ADAMDEC1 mRNA and protein was consistently observed in PDGFRα+ cells, but not in CD64+ macrophages found in human colonic mucosal tissue affected by Crohn’s disease. In summary, PDGFRα+ cells selectively express ADAMDEC1, which is localized to the colon mucosa layer. ADAMDEC1 expression significantly increases in DSS-induced colitis affected mice and Crohn’s disease affected human tissue, suggesting that this gene can serve as a diagnostic and/or therapeutic target for intestinal inflammation and Crohn’s disease.

Background and Hypothesis: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. Methods: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. Results: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. Conclusions: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression. [ABSTRACT FROM AUTHOR]