Chronic low-grade inflammation and oxidative stress play important roles in the development of obesity-induced cardiac hypertrophy. Here, we investigated the role of Fibronectin type III domain containing 5 (FNDC5) in cardiac inflammation and oxidative stress in obesity-induced cardiac hypertrophy. Male wild-type and FNDC5−/− mice were fed normal chow or high fat diet (HFD) for 20 weeks to induce obesity, and primary cardiomyocytes and H9c2 cells treated with palmitate (PA) were used as in vitro model. The therapeutic effects of lentiviral vector-mediated FNDC5 overexpression were also examined in HFD-induced cardiac hypertrophy. High fat diet manifested significant increases in body weight and cardiac hypertrophy marker genes expression, while FNDC5 deficiency aggravated cardiac hypertrophy evidenced by increased Nppa, Nppb and Myh7 mRNA level and cardiomyocytes area, in association with enhanced cardiac inflammatory cytokines expression, oxidative stress level and JAK2/STAT3 activation in HFD-fed mice. FNDC5 deficiency in primary cardiomyocytes or FNDC5 knockdown in H9c2 cells enhanced PA-induced inflammatory responses and NOX4 expression. Exogenous FNDC5 pretreatment attenuated PA-induced cardiomyocytes hypertrophy, inflammatory cytokines up-regulation and oxidative stress in primary cardiomyocytes and H9c2 cells. FNDC5 overexpression attenuated cardiac hypertrophy as well as cardiac inflammation and oxidative stress in HFD-fed mice. FNDC5 attenuates obesity-induced cardiac hypertrophy by inactivating JAK2/STAT3 associated-cardiac inflammation and oxidative stress. The cardio-protective role of FNDC5 shed light on future therapeutic interventions in obesity and related cardiovascular complications.
Alzheimer's disease (AD) is a severe neurodegenerative disease, which mainly manifests as memory and progressive cognitive impairment. At present, there is no method to prevent the progression of AD or cure it, and effective intervention methods are urgently needed. Network-targeted intermittent theta burst stimulation (iTBS) may be effective in alleviating the cognitive symptoms of patients with mild AD. The abnormal function of the dorsolateral prefrontal cortex (DLPFC) within executive control network (ECN) may be the pathogenesis of AD. Here, we verify the abnormality of the ECN in the native AD data set, and build the relevant brain network. In addition, we also recruited AD patients to verify the clinical effects of DLPFC-targeted intervention, and explor the neuro-mechanism. Sixty clinically diagnosed AD patients and 62 normal controls were recruited to explore the ECN abnormalities. In addition, the researchers recruited 20 AD patients to explore the efficacy of 14-session iTBS treatments for targeted DLPFC interventions. Functional magnetic resonance imaging and neuropsychological assessment of resting state were performed before and after the intervention. Calculate the changes in the functional connectivity of related brain regions in the ECN, as well as the correlation between the baseline functional connectivity and the clinical scoring scale, to clarify the mechanism of the response of iTBS treatment to treatment. Our results showed that compared with normal control samples, the brain function connection between the left DLPFC and the left IPL within the ECN of AD patients was significantly enhanced (t = 2.687, p = 0.008, FDR-corrected p = 0.045). And we found that iTBS stimulation significantly reduced the functional magnetic resonance imaging signal between the left DLPFC and the left IPL in the ECN (t = 4.271, p < 0.001, FDR-corrected p = 0.006), and it was related to the improvement of the patient's clinical symptoms (r = -0.470, p = 0.042). This work provides new insights for targeted brain area interventions. By targeted adjusting the functional connection of ECN to improve the clinical symptoms and cognitive function of AD patients.
Deficits in associative memory (AM) are the earliest and most prominent feature of Alzheimer's disease (AD) and demonstrate a clear cause of distress for patients and their families.The present study aimed to determine AM enhancements following accelerated intermittent theta-burst stimulation (iTBS) in patients with AD.In a randomized, double-blind, sham-controlled design, iTBS was administered to the left dorsolateral prefrontal cortex (DLPFC) of patients with AD for 14 days. Measurements included AM (primary outcome) and a comprehensive neuropsychological battery. Patients were evaluated at baseline, following the intervention (week 2), and 8 weeks after treatment cessation (week 10).Sixty patients with AD were initially enrolled; 47 completed the trial. The active group displayed greater AM improvements compared with the sham group at week 2 (P = 0.003), which was sustained at week 10. Furthermore, higher Mini-Mental State Examination (MMSE) scores at baseline were associated with greater AM improvements at weeks 2 and 10. For the independent iTBS group, this correlation predicted improvements in AM (P < 0.001) and identified treatment responders with 92% accuracy. Most of the neuropsychological tests were markedly improved in the active group. In particular, the Montreal Cognitive Assessment and MMSE in the active group increased by 2.8 and 2.3 points, respectively, at week 2, while there was no marked change in the sham group.In the present study, accelerated iTBS of the DLPFC demonstrated an effective and well-tolerated complementary treatment for patients with AD, especially for individuals with relatively high MMSE scores.
We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i.e., the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in bi-directional MR.
A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients.As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA).Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838-0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems.We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.
Abstract Background A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. Methods As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). Results Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838–0.968). The AUC for the development cohort was 0.850 and that for the validation cohort was 0.893. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The DCA curve results for both cohorts suggested a high clinical application value for the model. Conclusions We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.
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