Small molecule QF84139 ameliorates cardiac hypertrophy via activating the AMPK signaling pathway.

Cardiac hypertrophy is a common adaptive response to a variety of stimuli, but prolonged hypertrophy leads to heart failure. Hence, discovery of agents treating cardiac hypertrophy is urgently needed. In the present study, we investigated the effects of QF84139, a newly synthesized pyrazine derivative, on cardiac hypertrophy and the underlying mechanisms. In neonatal rat cardiomyocytes (NRCMs), pretreatment with QF84139 (1-10 µM) concentration-dependently inhibited phenylephrine-induced hypertrophic responses characterized by fetal genes reactivation, increased ANP protein level and enlarged cardiomyocytes. In adult male mice, administration of QF84139 (5-90 mg·kg-1·d-1, i.p., for 2 weeks) dose-dependently reversed transverse aortic constriction (TAC)-induced cardiac hypertrophy displayed by cardiomyocyte size, left ventricular mass, heart weights, and reactivation of fetal genes. We further revealed that QF84139 selectively activated the AMPK signaling pathway without affecting the phosphorylation of CaMKIIδ, ERK1/2, AKT, PKCε, and P38 kinases in phenylephrine-treated NRCMs and in the hearts of TAC-treated mice. In NRCMs, QF84139 did not show additive effects with metformin on the AMPK activation, whereas the anti-hypertrophic effect of QF84139 was abolished by an AMPK inhibitor Compound C or knockdown of AMPKα2. In AMPKα2-deficient mice, the anti-hypertrophic effect of QF84139 was also vanished. These results demonstrate that QF84139 attenuates the PE- and TAC-induced cardiac hypertrophy via activating the AMPK signaling. This structurally novel compound would be a promising lead compound for developing effective agents for the treatment of cardiac hypertrophy.

Exploring the diagnostic value of CLR and CPR in differentiating Kawasaki disease from other infectious diseases based on clinical predictive modeling.

Background: Kawasaki disease (KD) is an important cause of acquired heart disease in children and adolescents worldwide. KD and infectious diseases can be easily confused when the clinical presentation is inadequate or atypical, leading to misdiagnosis or underdiagnosis of KD. In turn, misdiagnosis or underdiagnosis of KD can lead to delayed use of intravenous immunoglobulin (IVIG), increasing the risk of drug resistance and coronary artery lesions (CAL). Objectives: The purpose of this study was to develop a predictive model for identifying KD and infectious diseases in children in the hope of helping pediatricians develop timely and accurate treatment plans. Methods: The data Patients diagnosed with KD from January 2018 to July 2022 in Shenzhen Longgang District Maternity & Child Healthcare Hospital, and children diagnosed with infectious diseases in the same period will be included in this study as controls. We collected demographic information, clinical presentation, and laboratory data on KD before receiving IVIG treatment. All statistical analyses were performed using R-4.2.1 (https://www.rproject.org/). Logistic regression and Least Absolute Shrinkage with Selection Operator (LASSO) regression analyses were used to build predictive models. Calibration curves and C-index were used to validate the accuracy of the prediction models. Results: A total of 1,377 children were enrolled in this study, 187 patients with KD were included in the KD group and 1,190 children with infectious diseases were included in the infected group. We identified 15 variables as independent risk factors for KD by LASSO analysis. Then by logistic regression we identified 7 variables for the construction of nomogram including white blood cell (WBC), Monocyte (MO), erythrocyte sedimentation rate (ESR), alanine transaminase (ALT), albumin (ALB), C-reactive protein to procalcitonin ratio (CPR) and C-reactive protein to lymphocyte ratio (CLR). The calibration curve and C-index of 0.969 (95% confidence interval: 0.960-0.978) validated the model accuracy. Conclusion: Our predictive model can be used to discriminate KD from infectious diseases. Using this predictive model, it may be possible to provide an early determination of the use of IVIG and the application of antibiotics as soon as possible.