Dolantennant6850

This study revealed that the proposed network pharmacology-integrated metabolomics strategy was a powerful tool for explaining the mechanism of synergistic action in the processing of QSP, further controlling the quality and understanding the processing mechanism of TCM formulae.The problem of associating data from multiple sources and predicting an outcome simultaneously is an important one in modern biomedical research. It has potential to identify multidimensional array of variables predictive of a clinical outcome and to enhance our understanding of the pathobiology of complex diseases. Incorporating functional knowledge in association and prediction models can reveal pathways contributing to disease risk. We propose Bayesian hierarchical integrative analysis models that associate multiple omics data, predict a clinical outcome, allow for prior functional information, and can accommodate clinical covariates. The models, motivated by available data and the need for exploring other risk factors of atherosclerotic cardiovascular disease (ASCVD), are used for integrative analysis of clinical, demographic, and genomics data to identify genetic variants, genes, and gene pathways likely contributing to 10-year ASCVD risk in healthy adults. Our findings revealed several genetic variants, genes, and gene pathways that are highly associated with ASCVD risk, with some already implicated in cardiovascular disease (CVD) risk. Extensive simulations demonstrate the merit of joint association and prediction models over two-stage methods association followed by prediction.

Native speakers frequently outperform non-native speakers on classic semantic verbal fluency tasks that target concrete non-emotional word retrieval. Much less is known about performance differences in retrieval of emotional words, which are abstract and crucial to social-emotional competence. This study compared native and non-native speakers' verbal productivity on emotional and non-emotional verbal fluency tasks.

Forty-seven native and 37 non-native speakers of English participated in the study. Selleckchem Relacorilant Participants completed seven semantic verbal fluency tasks in English including classic semantic (e.g., "animals"), action (e.g., "things people do"), and emotional variants (e.g., "things that make people happy"). Subjective and objective measures of English proficiency, information about language usage, and cognitive measures (working memory) were obtained for each participant.

Verbal productivity for classic semantic, action, and emotional verbal fluency was lower for non-native speakers. Smaller language-specific vocabulary in non-native speakers did not moderate positivity biases in emotional verbal fluency. Subjective and objective language proficiency measures were less predictive of performance on the emotional than the non-emotional verbal fluency tasks.

Non-native speakers perform more poorly than native speakers on semantic verbal fluency in English for both emotional and non-emotional variants. Positivity biases are not moderated by language proficiency and are reliable features of emotional verbal fluency. Proficiency measures may be less effective in predicting generation of emotional than neutral words.

Non-native speakers perform more poorly than native speakers on semantic verbal fluency in English for both emotional and non-emotional variants. Positivity biases are not moderated by language proficiency and are reliable features of emotional verbal fluency. Proficiency measures may be less effective in predicting generation of emotional than neutral words.In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer's disease, Parkinson's disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.Ischemic heart diseases have emerged as great threats to human health. Nowadays, restoration of cardiac blood flow supply is widely regarded as a feasible treatment choice for ischemic heart diseases; however, this intervention would contradictorily elicit reperfusion injury. Recently, myocardial ischemia/reperfusion injury (MI/RI) has aroused widespread public concerns. Remifentanil, an ultra-short acting opioid analgesic, is frequently used for surgical anesthesia. Previous studies have demonstrated the cardioprotective effects of remifentanil preconditioning in clinical practice and in vitro experimental models; however, its exact mechanisms remain largely unclear. This study aimed to further evaluate the protective effects of remifentanil preconditioning against MI/RI and elucidate the potential molecular mechanisms. Rat models of MI/RI were successfully established via ligation of left anterior descending coronary artery for 30 minutes and restoration of blood flow for 2 hours. Herein, animal experiments displayed that remifentanil preconditioning could alleviate myocardial damage in rat models of MI/RI. Consistently, cell model experiments implied that remifentanil preconditioning attenuated hypoxia/reoxygenation exposure-induced injury in rat cardiomyocytes. Moreover, our findings verified the involvement of Notch signaling pathway in the protective effects of remifentanil preconditioning. In addition, mechanistic studies revealed that remifentanil preconditioning could up-regulate Jagged-1 expression and that Jagged-1 mediated the cardioprotective effects of remifentanil preconditioning through activating Notch signaling pathway. Taken together, our data indicate that remifentanil preconditioning ameliorates myocardial damage in rat MI/RI models via Jagged-1-mediated Notch signaling pathway activation. Thus, this study may offer some novel clues for understanding the cardioprotective mechanisms of remifentanil preconditioning against MI/RI.

Two complementary studies were used to assess the real-life use of nalmefene in alcohol-dependent patients and its impact on alcohol use health status.

USE-PACT was a prospective cohort study designed to evaluate the real-life effectiveness of nalmefene in the management of alcohol dependence, as assessed by total alcohol consumption (TAC) and number of heavy drinking days (HDD) at 1 year. USE-AM was a cohort study using data from the French nationwide claims database and was used to evaluate the external validity of the population in the prospective study.

Overall, 256 of 700 new nalmefene users enrolled in the USE-PACT study had valid data at 1year. After 1 year, patients treated with nalmefene showed a mean±SD reduction from baseline in TAC (-41.5±57.4g/day) and number of HDD (-10.7±11.7days/4weeks). Patients took a mean±SD of 20.0±12.0 tablets/4weeks (median of 1 tablet/day) for the first 3months and then reduced the dose. The proportion of patients who no longer took nalmefene gradually increased from 5% at 1month to 52% at 1year. The USE-AM study identified 486 patients with a first reimbursement for nalmefene in 2016; baseline characteristics confirmed external validity of the USE-PACT study. Overall, 46.3% of initial USE-AM prescriptions were made by GPs; most (91.8%) patients stopped treatment during follow-up. However, 15.2% of patients resumed treatment after stopping.

In this analysis of French routine practice, patients with alcohol dependence treated with nalmefene showed reduced alcohol consumption, and nalmefene was generally well tolerated.

In this analysis of French routine practice, patients with alcohol dependence treated with nalmefene showed reduced alcohol consumption, and nalmefene was generally well tolerated.

To illustrate a machine learning-based approach for identifying and investigating moderators of alcohol use intervention effects in aggregate-data meta-analysis.

We illustrated the machine learning technique of random forest modeling using data from an ongoing meta-analysis of brief substance use interventions implemented in general healthcare settings. A subset of 40 trials testing brief alcohol interventions (BAIs) was used; these trials provided 344 estimates of post-intervention effects on participants' alcohol use as well as data on 20 potential moderators of intervention effects. These candidate moderators included characteristics of trial methodology and implementation, intervention design and participant samples.

The best-fitting random forest model identified 10 important moderators from the pool of 20 candidate moderators. Meta-regression utilizing the selected moderators found that inclusion of prescriptive advice in a BAI session significantly moderated BAI effects on alcohol use. Observed effects were also significantly moderated by several methodological characteristics of trials, including the type of comparison group used, the overall level of attrition and the strategy used to address missing data. In a meta-regression model that included all candidate moderators, fewer coefficients were found to be significant, indicating that the use of a preliminary data reduction technique to identify only important moderators for inclusion in final analyses may have yielded improved statistical power to detect moderation.

Machine learning methods can be valuable tools for clarifying the influence of trial, intervention and sample characteristics on alcohol use intervention effects, in particular when numerous candidate moderators are available.

Machine learning methods can be valuable tools for clarifying the influence of trial, intervention and sample characteristics on alcohol use intervention effects, in particular when numerous candidate moderators are available.It has been demonstrated that trimethylamine N-oxide (TMAO) serves as a driver of atherosclerosis, suggesting that reduction of TMAO level might be a potent method to prevent the progression of atherosclerosis. Herein, we explored the role of TMAO in the stability of carotid atherosclerotic plaques and disclosed the underlying mechanisms. The unstable carotid artery plaque models were established in C57/BL6 mice. L-carnitine (LCA) and methimazole (MMI) administration were applied to increase and reduce TMAO levels. Hematoxylin and eosin (H&E) staining, Sirius red, Perl's staining, Masson trichrome staining and immunohistochemical staining with CD68 staining were used for histopathology analysis of the carotid artery plaque. M1 and M2 macrophagocyte markers were assessed by RT-PCR to determine the polarization of RAW264.7 cells. MMI administration for 2 weeks significantly decreased the plaque area, increased the thickness of the fibrous cap and reduced the size of the necrotic lipid cores, whereas 5-week of administration of MMI induced intraplate hemorrhage. LCA treatment further deteriorated the carotid atherosclerotic plaque but with no significant difference. In mechanism, we found that TMAO treatment impaired the M2 polarization and efferocytosis of RAW264.7 cells with no obvious effect on the M1 polarization. In conclusion, the present study demonstrated that TMAO reduction enhanced the stability of carotid atherosclerotic plaque through promoting macrophage M2 polarization and efferocytosis.