MicroRNA-155 Attenuates Late Sepsis-Induced Cardiac Dysfunction Through JNK and β-Arrestin 2

Cardiac dysfunction is correlated with detrimental prognosis of sepsis and contributes to a high risk of mortality. After an initial hyperinflammatory reaction, most patients enter a protracted state of immunosuppression (late sepsis) that alters both innate and adaptive immunity. The changes of cardiac function in late sepsis are not yet known. MicroRNA-155 (miR-155) is previously found to play important roles in both regulations of immune activation and cardiac function. In this study, C57BL/6 mice were operated to develop into early and late sepsis phases, and miR-155 mimic was injected through the tail vein 48 h after cecal ligation and puncture (CLP). The effect of miR-155 on CLP-induced cardiac dysfunction was explored in late sepsis. We found that increased expression of miR-155 in the myocardium protected against cardiac dysfunction in late sepsis evidenced by attenuating sepsis-reduced cardiac output and enhancing left ventricular systolic function. We also observed that miR-155 markedly reduced the infiltration of macrophages and neutrophils into the myocardium and attenuated the inflammatory response via suppression of JNK signaling pathway. Moreover, overexpression of β-arrestin 2 (Arrb2) exacerbated the mice mortality and immunosuppression in late sepsis. Furthermore, transfection of miR-155 mimic reduced Arrb2 expression, and then restored immunocompetence and improved survival in late septic mice. We conclude that increased miR-155 expression through systemic administration of miR-155 mimic attenuates cardiac dysfunction and improves late sepsis survival by targeting JNK associated inflammatory signaling and Arrb2 mediated immunosuppression

Clinical Features and Prognosis of Severe Secondary Hyperparathyroidism: A Retrospective Study from a Single Center

Abstract Purpose Secondary hyperparathyroidism (SHPT) is one of the most common complications of chronic kidney disease and has a high rate of morbidity and mortality. Current studies on prognostic factors in SHPT are inadequate. We aimed to identify a single-center cohort of severe SHPT to elucidate relevant clinical and laboratory features and explore laboratory indicators that related to its prognosis. Methods The clinical data of 46 patients with SHPT, admitted to the intensive care unit (ICU) of the First Affiliated Hospital of Zhengzhou University in the time period ranging from January 2019 to March 2022 were analyzed retrospectively. Clinical data collected were screened univariately for influences that were associated with poor prognosis. A binary logistic regression model was constructed to analyze the independent risk factors for poor clinical prognosis, using correlated influences. The value of each indicator in predicting patient prognosis was analyzed using receiver operating characteristic curves (ROC) curves. Results The causes of death among the 46 patients with severe SHPT were cardiogenic death (malignant arrhythmia, cardiac arrest) in 11 cases (47.8%), sepsis in 9 cases (39.2%), and neurogenic death (intracranial hemorrhage) in 3 cases (13.0%). Patients were divided into a good prognosis group and a poor prognosis group according to their status at the time of leaving the ICU. There was no statistically significant difference in sex, BUN, NT-pro BNP, ALP, Scr, Mg, Ca, Pi, K, CRP, Hb, and PLT between the poor prognosis group and the good prognosis groups. The age, PTH, PCT, WBC, APACHE II, and neutrophil ratio of the poor prognosis group were higher than those of the good prognosis group, and the ALB level was lower than that of the good prognosis group, with a statistically significant difference of P < 0.05. The 19 clinical indicators mentioned above were screened univariately. Among them, age, PTH, WBC, ALB, APACHE II and neutrophil ratio were significantly associated with prognosis, P < 0.05. Binary logistic regression analysis showed that age (OR = 1.076, 95% CI (1.011, 1.145)), PTH (OR = 1.004, 95% CI (1.000, 1.007)), WBC (OR = 1.295, 95% CI (1.026, 1.634)) were indicators for poor prognosis in patients with severe SHPT, and ALB (OR = 0.803, 95% CI (0.645, 0.998)) was a protective factor for poor prognosis. The ROC curve showed that the optimal cut-off point for patient age was 51 years, with a sensitivity of 86.9% and specificity of 52.2%; the optimal cut-off point for PTH was 346 pg/ml, with a sensitivity of 59.1% and specificity of 82.6%; the optimal cut-off point for WBC was 11.95 × 10^9/L, with a sensitivity of 56.52% and specificity of 91.3%; the optimal cut-off point for neutrophil ratio was 82.4%, sensitivity 82.6%, specificity 73.9%. Conclusion Age, PTH, and WBC are independent risk factors for poor prognosis of severe SHPT, and ALB is an independent protective factor for poor prognosis. Patients with severe SHPT should be assessed for risk of the poor prognosis based on age, admission PTH, WBC, ALB, and neutrophil ratio as early as possible to adjust the treatment strategy

Development and Validation of Novel Diagnostic Models for Biliary Atresia in a Large Cohort of Chinese PatientsResearch in Context

Background & aims: The overlapping features of biliary atresia (BA) and the other forms of neonatal cholestasis (NC) with different causes (non-BA) has posed challenges for the diagnosis of BA. This study aimed at developing new and better diagnostic models for BA. Methods: We retrospectively analyzed data from 1728 newborn infants with neonatal obstructive jaundice (NOJ). New prediction models, including decision tree (DT), random forest (RF), and multivariate logistic regression-based nomogram for BA were created and externally validated in an independent set of 508 infant patients. Results: Fiver predictors, including gender, weight, direct bilirubin (DB), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT) were significantly different between the BA and non-BA groups (P < .05), from which DT, RF, and nomogram models were developed. The area under the receiver operating characteristic (ROC) curve (AUC) value for the nomogram was 0.898, which was greater than that of a single biomarker in the prediction of BA. Performance comparison of the three diagnostic models showed that the nomogram displayed better discriminative ability (sensitivity, 85.7%; specificity, 80.3%; PPV, 0.969) at the optimal cut-off value compared with DT and RF, which had relatively similar high sensitivity and PPV (0.941 and 0.947, respectively), but low specificity in the modeling group. In sub-analysis of the discriminative capacity between the nomogram and GGT (<300 or ≥ 300), we found that the nomogram was superior to the GGT alone in the preoperative diagnosis of BA. Conclusions: The nomogram has demonstrated better performance for the prediction of BA, holding promise for future clinical application. Keywords: Biliary atresia, Neonatal cholestasis, Gamma-glutamyl transpeptidase, Nomogra

DataSheet_1_Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.docx

IntroductionAnti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. MethodsUltra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. ResultsA partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p DiscussionThis study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.</p