Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study

ObjectivesThis study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients.MethodsIn this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure.ResultsThe prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1*age (OR = 1.476, 95% CI: 1.103–1.975, P = 0.009), lower albumin (OR = 0.863, 95% CI: 0.771–0.965, P = 0.010), and lower 10*calcium levels (OR = 0.747, 95% CI: 0.615–0.907, P = 0.003). In addition, higher 0.1*age, lower albumin levels, and anxiety were independently associated with severe sleep disturbance in MHD patients. A risk prediction model of sleep disturbance in MHD patients showed that the concordance index after calibration is 0.736, and the calibration curve is approximately distributed along the reference line.ConclusionsOlder age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration

A Comprehensive and Rapid Quality Evaluation Method of Traditional Chinese Medicine Decoction by Integrating UPLC-QTOF-MS and UFLC-QQQ-MS and Its Application

Decoction is one of the oldest forms of traditional Chinese medicine and it is widely used in clinical practice. However, the quality evaluation and control of traditional decoction is a challenge due to the characteristics of complicated constituents, water as solvent, and temporary preparation. ShenFu Prescription Decoction (SFPD) is a classical prescription for preventing and treating many types of cardiovascular disease. In this article, a comprehensive and rapid method for quality evaluation and control of SFPD was developed, via qualitative and quantitative analysis of the major components by integrating ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry and ultra-fast-performance liquid chromatography equipped with triple quadrupole mass spectrometry. Consequently, a total of 39 constituents were tentatively identified in qualitative analysis, of which 21 compounds were unambiguously confirmed by comparing with reference substances. We determined 13 important constituents within 7 min by multiple reaction monitoring. The validated method was applied for determining five different proportion SFPDs. It was found that different proportions generated great influence on the dissolution of constituents. This may be one of the mechanisms for which different proportions play different synergistic effects. Therefore, the developed method is a fast and useful approach for quality evaluation of SFPD

Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, preprocessing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics

A Bibliometric Analysis of Research on the Role of BDNF in Depression and Treatment

Brain-derived neurotrophic factor (BDNF), as the most widely distributed and widely studied neurotrophic factor in the mammalian brain, plays a key role in depression and the mechanisms of action for antidepressants. Currently, there is a large number of studies on the role of BDNF in the pathogenesis and therapeutic mechanism of depression. The quantity and quality of these studies, however, are unknown. To give beginners a quicker introduction to this research topic, we therefore performed a bibliometric analysis. A total of 5300 publications were included. We obtained the publications on this topic from the Web of Science database, and a variety of bibliographic elements were collected, including annual publications, authors, countries/regions, institutions, journals, and keywords. Moreover, we found that oxidative stress and neuroinflammation are the hotspots in the field in very recent years. Collectively, this study provides a comprehensive summary and analysis on the role of BDNF in depression and its treatment and offers meaningful values for beginners on this topic