Association of cardiometabolic multimorbidity with all-cause and cardiovascular disease mortality among Chinese hypertensive patients.

BACKGROUND: Hypertension usually clusters with multiple comorbidities. However, the association between cardiometabolic multimorbidity (CMM) and mortality in hypertensive patients is unclear. This study aimed to investigate the association between CMM and all-cause and cardiovascular disease (CVD) mortality in Chinese patients with hypertension. METHODS: The data used in this study were from the China National Survey for Determinants of Detection and Treatment Status of Hypertensive Patients with Multiple Risk Factors (CONSIDER), which comprised 5006 participants aged 19-91 years. CMM was defined as the presence of one or more of the following morbidities: diabetes mellitus, dyslipidemia, chronic kidney disease, coronary heart disease, and stroke. Cox proportional hazard models were used to calculate the hazard ratios (HR) with 95% CI to determine the association between the number of CMMs and both all-cause and CVD mortality. RESULTS: Among 5006 participants [mean age: 58.6 ± 10.4 years, 50% women (2509 participants)], 76.4% of participants had at least one comorbidity. The mortality rate was 4.57, 4.76, 8.48, and 16.04 deaths per 1000 person-years in hypertensive patients without any comorbidity and with one, two, and three or more morbidities, respectively. In the fully adjusted model, hypertensive participants with two cardiometabolic diseases (HR = 1.52, 95% CI: 1.09-2.13) and those with three or more cardiometabolic diseases (HR = 2.44, 95% CI: 1.71-3.48) had a significantly elevated risk of all-cause mortality. The findings were similar for CVD mortality but with a greater increase in risk magnitude. CONCLUSIONS: In this study, three-fourths of hypertensive patients had CMM. Clustering with two or more comorbidities was associated with a significant increase in the risk of all-cause and cardiovascular mortality among hypertensive patients, suggesting more intensive treatment and control in this high-risk patient group.

Latent factor model for multivariate functional data.

For multivariate functional data, a functional latent factor model is proposed, extending the traditional latent factor model for multivariate data. The proposed model uses unobserved stochastic processes to induce the dependence among the different functions, and thus, for a large number of functions, may provide a more parsimonious and interpretable characterization of the otherwise complex dependencies between the functions. Sufficient conditions are provided to establish the identifiability of the proposed model. The performance of the proposed model is assessed through simulation studies and an application to electroencephalography data.

BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.

Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.

CaWRKY50 Acts as a Negative Regulator in Response to Colletotrichum scovillei Infection in Pepper.

Chili anthracnose is one of the most common and destructive fungal pathogens that affects the yield and quality of pepper. Although WRKY proteins play crucial roles in pepper resistance to a variety of pathogens, the mechanism of their resistance to anthracnose is still unknown. In this study, we found that CaWRKY50 expression was obviously induced by Colletotrichum scovillei infection and salicylic acid (SA) treatments. CaWRKY50-silencing enhanced pepper resistance to C. scovillei, while transient overexpression of CaWRKY50 in pepper increased susceptibility to C. scovillei. We further found that overexpression of CaWRKY50 in tomatoes significantly decreased resistance to C. scovillei by SA and reactive oxygen species (ROS) signaling pathways. Moreover, CaWRKY50 suppressed the expression of two SA-related genes, CaEDS1 (enhanced disease susceptibility 1) and CaSAMT1 (salicylate carboxymethyltransferase 1), by directly binding to the W-box motif in their promoters. Additionally, we demonstrated that CaWRKY50 interacts with CaWRKY42 and CaMIEL1 in the nucleus. Thus, our findings revealed that CaWRKY50 plays a negative role in pepper resistance to C. scovillei through the SA-mediated signaling pathway and the antioxidant defense system. These results provide a theoretical foundation for molecular breeding of pepper varieties resistant to anthracnose.

Fast Multilevel Functional Principal Component Analysis.

We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA (Di et al., 2009). Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund.

Association between treatment failure in patients with early syphilis and penicillin resistance-related gene mutations of Treponema pallidum: Protocol for a multicentre nested case-control study.

Background: The widespread occurrence of syphilis remains a global public health problem. Although penicillin has been recommended as the first-line therapy for syphilis for more than 70 years, treatment failure occurs in 10-20% of patients with early syphilis. Recent studies have reported varied single-nucleotide polymorphisms (SNPs) of Treponema pallidum related to penicillin resistance. The clinical relevance of these SNPs to treatment failure in patients with early syphilis is unresolved. In this work, a protocol is developed to evaluate the association between treatment failure in patients with early syphilis and penicillin resistance-related gene mutations of T. pallidum. Methods: A multicentre nested case-control study is designed, and patients who are diagnosed with early syphilis and treated with penicillin will be recruited for the study cohort. Before the first treatment, baseline information and biological specimens will be collected from the subjects, and serological tests for syphilis will be performed. Each participant will be followed up at 1, 3, 6, 9, and 12 months after the first treatment, and the clinical manifestations and serum non-treponemal test titres will be evaluated at each follow-up. Patients who will fail treatment are defined as cases, and those who will respond to treatment are defined as controls. Tests for SNPs related to penicillin-binding proteins and Tp47 will be performed in these cases and controls. Survival analysis is used performed to identify gene mutations of T. pallidum related to penicillin resistance and their combinations associated with treatment failure. Discussion: This protocol provides a practical clinical study design that illustrates the role of gene mutations of T. pallidum related to penicillin resistance in the treatment outcome of patients with early syphilis.

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.

Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.

Functional data analysis for longitudinal data with informative observation times.

In functional data analysis for longitudinal data, the observation process is typically assumed to be noninformative, which is often violated in real applications. Thus, methods that fail to account for the dependence between observation times and longitudinal outcomes may result in biased estimation. For longitudinal data with informative observation times, we find that under a general class of shared random effect models, a commonly used functional data method may lead to inconsistent model estimation while another functional data method results in consistent and even rate-optimal estimation. Indeed, we show that the mean function can be estimated appropriately via penalized splines and that the covariance function can be estimated appropriately via penalized tensor-product splines, both with specific choices of parameters. For the proposed method, theoretical results are provided, and simulation studies and a real data analysis are conducted to demonstrate its performance.

CRISPR-assisted transcription activation by phase-separation proteins

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system has been widely used for genome engineering and transcriptional regulation in many different organisms. Current CRISPR-activation (CRISPRa) platforms often require multiple components because of inefficient transcriptional activation. Here, we fused different phase-separation proteins to dCas9-VPR (dCas9-VP64-P65-RTA) and observed robust increases in transcriptional activation efficiency. Notably, human NUP98 (nucleoporin 98) and FUS (fused in sarcoma) IDR domains were best at enhancing dCas9-VPR activity, with dCas9-VPR-FUS IDR (VPRF) outperforming the other CRISPRa systems tested in this study in both activation efficiency and system simplicity. dCas9-VPRF overcomes the target strand bias and widens gRNA designing windows without affecting the off-target effect of dCas9-VPR. These findings demonstrate the feasibility of using phase-separation proteins to assist in the regulation of gene expression and support the broad appeal of the dCas9-VPRF system in basic and clinical applications.

Three-dimensional reconstruction reveals the correlation between the extent of alveolar clefts and secondary nasal deformity in adults / 华西口腔医学杂志

OBJECTIVES@#This study aimed to explore the relationship between alveolar cleft and secondary nasal deformity post unilateral cleft lip repair in adults.@*METHODS@#A total of 27 patients aged 16-30 years old with unilateral secondary nasal deformity and alveolar cleft were included, 13 of whom underwent bone grafting. Spiral CT data of all preoperative and postoperative patients who had alveolar bone grafting were collected. Then, Mimics software was used for three-dimensional reconstruction to evaluate the correlation between the width, height, and volume of the alveolar cleft and those of the nasal deformity. The difference in nasal deformity before and after alveolar bone grafting was also explored.@*RESULTS@#The width of the alveolar cleft was positively correlated with the difference in bilateral nostril floor width (P<0.05). As the effective depth of the alveolar cleft increased, the sub-alare inclination angle largened (P<0.05). However, no significant difference was found in the nasal deformity between before and after alveolar bone grafting.@*CONCLUSIONS@#Alveolar cleft is closely related to secondary nasal deformities post unilateral cleft lip repair, especially nasal floor deformities. Alveolar bone grafting benefits adult patients for the improvement of secondary nasal deformities post unilateral cleft lip repair.

An interpretable machine learning-based prediction model for risk of death for patients with ischemic stroke in intensive care unit / 南方医科大学学报

OBJECTIVE@#To construct an inherent interpretability machine learning model as an explainable boosting machine model (EBM) for predicting one-year risk of death in patients with severe ischemic stroke.@*METHODS@#We randomly divided the data of 2369 eligible patients with severe ischemic stroke in the MIMIC-Ⅳ(2.0) database, who were admitted in ICU in 2008 to 2019, into a training dataset (80%) and a test dataset (20%), and assessed the prognosis of the patients using the EBM model. The prediction performance of the model was evaluated by calculating the area under the receiver operating characteristic (AUC) curve. The calibration curve and Brier score were used to evaluate the degree of calibration of the model, and a decision curve was generated to assess the net clinical benefit.@*RESULTS@#The EBM model constructed in this study had good discrimination power, calibration and net benefit, with an AUC of 0.857 (95% CI: 0.831-0.887) for predicting prognosis of severe ischemic stroke. Calibration curve analysis showed that the standard curve of the EBM model was the closest to the ideal curve. Decision curve analysis showed that the model had the greatest net benefit rate at the prediction probability threshold of 0.10 to 0.80. The top 5 independent predictive variables based on the EBM model were age, SOFA score, mean heart rate, mechanical ventilation, and mean respiratory rate, whose significance scores ranged from 0.179 to 0.370.@*CONCLUSION@#This EBM model has a good performance for predicting the risk of death within one year in patients with severe ischemic stroke and allows clinicians to better understand the contributing factors of the patients' outcomes through the model interpretability.

Bioactive pterocarpans from the root of Astragalus membranaceus var. mongholicus.

Eleven undescribed and three known pterocarpans were isolated and identified from the traditional Chinese medicine "Huang-qi", Astragali Radix (the root of Astragalus membranaceus var. mongholicus (Bunge) P.K.Hsiao). The structures of these pterocarpans were determined using spectroscopic, X-ray crystallographic, quantum chemical calculation, and chemical methods. Pterocarpans, almost exclusively distributed in the family of Leguminosae, are the second largest subgroup of isoflavanoids. However, pterocarpan glycoside number is limited, most of which are glucosides, and only one pterocarpan apioside was isolated from nature. Notably, nine rare apiosyl-containing pterocarpan glycosides were isolated and identified. The hypoglycemic activities of all these compounds were evaluated using α-glucosidase and DPP-IV inhibitory assays respectively, and some isolates displayed the α-glucosidase inhibitory function. The antioxidant activities of all compounds were evaluated using the ORAC and DPPH radical scavenging assays, respectively. All compounds exhibited varying degrees of oxygen radical absorbance capacity, and some compounds displayed DPPH radical scavenging ability.

Fixed-effects inference and tests of correlation for longitudinal functional data.

We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal sampling procedure. The framework provides a natural extension of standard longitudinal correlation models for scalar observations to functional observations. Using simulation studies, we compare fixed effects estimation under correctly and incorrectly specified correlation structures and also test the longitudinal correlation structure. Finally, we apply the proposed methods to a longitudinal functional dataset on physical activity. The computer code for the proposed method is available at https://github.com/rli20ST758/FILF.

Two-stage linked component analysis for joint decomposition of multiple biologically related data sets.

Integrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets.

Single-index models with functional connectivity network predictors.

Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease.

Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. Data used in preparation of this article were obtained from the ADNI database.

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

OBJECTIVES: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy. METHODS: Females who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimized to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns. RESULTS: In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2% and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5% and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1). CONCLUSIONS: DL had high detection sensitivity with a low FP but was affected by lesion size. ADVANCES IN KNOWLEDGE: DL is technically feasible for the automatic detection of lesions in ABUS.

Correlation between intestinal glucagon-like peptides-1 and major depressive disorder / 中华行为医学与脑科学杂志

Objective:To explore the pathogenesis of major depressive disorder(MDD) by comparing the serum glucose and lipid metabolism indicators, levels of glucagon-like peptide-1(GLP-1) in plasma and feces, and the content of specific intestinal flora ( Lactobacillus, Bifidobacterium) between patients with MDD who were diagnosed for the first time and healthy controls. Methods:Totally 80 MDD patients hospitalized from January 1, 2020 to March 30, 2021 and 80 healthy volunteers with normal physical examination in the same period were selected. Blood and fecal samples of patients with MDD and healthy controls were collected respectively. The indicators of serum glucose and lipid metabolism were detected by automatic biochemical analyzer, the concentrations of GLP-1 in plasma and feces were detected by ELISA, and the relative contents of Lactobacillus and Bifidobacterium in feces were detected by real-time PCR. The differences between two groups of glucose and lipid metabolism indicators, GLP-1 levels and the relative contents of Lactobacillus and Bifidobacterium in feces were analyzed. SPSS 22.0 software was used for statistical analysis. Independent sample t-test and analysis of variance were used for inter group comparison, and Pearson correlation analysis was used for correlation analysis. Results:Compared with the control group, the levels of serum TC, HDL, and LDL in the MDD group decreased ((3.99±0.85)mmol/L , (4.78±0.86)mmol/L; (1.18±0.29)mmol/L, (1.30±0.28)mmol/L; (2.64±0.70)mmol/L, (3.19±0.69)mmol/L; t=5.559, 2.371, 4.695, all P<0.05). The plasma and fecal GLP-1 levels of the MDD group were lower than those of the control group (plasma: (0.81±0.22)pmol/mL, (1.05±0.26)pmol/mL , t=4.509, P<0.01; feces: (2.23±0.46)pmol/mL , (2.47±0.37)pmol/mL, t=2.533, P<0.05). Compared with the control group, the relative contents of Lactobacillus(2.56±1.59, 3.51±2.21) and Bifidobacterium(2.24±1.89 , 3.17±2.08) in the MDD group decreased ( t=2.218, 2.082, both P<0.05). The level of plasma GLP-1 in the MDD group was negatively correlated with FPG, TG, and disease severity ( r=-0.281, -0.221, -0.437, P<0.05). The level of plasma GLP-1 in the control group was negatively correlated with FPG ( r=-0.580, P<0.01). The fecal GLP-1 level of the MDD group was negatively correlated with the severity of the disease ( r=-0.298, P<0.01), and the fecal GLP-1 level of the control group was positively correlated with fecal Lactobacillus and Bifidobacterium ( r=0.685, 0.428, P<0.01). Conclusion:MDD patients have abnormal glucose and lipid metabolism, decreased GLP-1 level and decreased relative content of intestinal Lactobacillus and Bifidobacterium. Changes in intestinal flora affect GLP-1 levels. GLP-1 can affect glucose and lipid metabolism and depressive symptoms in patients with MDD by binding to specific receptors in intestinal tract and central nervous system.

Development and preliminary application of the information monitoring system for radiological protection / 中华放射医学与防护杂志

Objective:To develop an information system for testing radiological protection that can interface with National Radiation Health Information Platform/Medical Radiation Monitoring Subsystem and to improve the testing efficiency.Methods:Complying with the relevant national regulations and standards, the analysis was carried out of demand investigation and system modeling. An information system for testing radiological protection was established using B/S architecture, comprising three modules such as testing, audit and system management. The users at four levels were set of administrator, inspector, auditor and report issuer.Results:Based on test result, the developed information system has been shown to realize the informatization of the whole process from filling, auditing, issuing, issuing of the testing report to data uploading, with improved testing efficiency.Conclusions:The developed information system for testing radiological protection can improve the testing efficiency, and can be successfully interfaced with the National Radiation Health Information Platform/Medical Radiation Protection Monitoring Subsystem.

Correlation between platelet distribution width and anticoagulant stability of warfarin in patients with persistent atrial fibrillation / 中国医师杂志

Objective:To explore the correlation between platelet distribution width (PDW) and the stability of warfarin anticoagulant therapy in patients with persistent atrial fibrillation.Methods:138 patients with persistent atrial fibrillation treated with warfarin in Jiujiang First People′s Hospital from January 2018 to December 2019 were selected. They were divided into groups according to whether PDW increased (PDW decreased group, normal group, PDW increased group) and subgroups stratification was performed. After stratification, the relationship between PDW and the stability of warfarin anticoagulation treatment [expressed as the percentage of time of International normalized ratio(INR) within the treatment target range (TTR)] was analyzed. At the same time, the predictive value of PDW for the stability of warfarin anticoagulation treatment was analyzed.Results:There were significant difference in PDW and TTR among the PDW decreased group, normal group, PDW increased group ( F=30.322, 10.745, all P<0.01). The PDW distribution of patients with different anticoagulation quality was significantly different (χ 2=9.532, P<0.05). Receiver operating characteristic (ROC) curve showed that the area under curve (AUC) of PDW in predicting warfarin anticoagulant stability was 0.621(95% CI: 0.524-0.737). There was significant difference in PDW and TTR among the PDW<14%, 14%-<16%, 16-<18% and ≥18% groups( F=18.075, 11.638, all P<0.01). There was no significant difference in PDW and TTR among the three subgroups of PDW<14%, 14%-<16% and 16-<18% ( P=0.843, P=0.401). There were significant difference in PDW and TTR between the two subgroup of PDW 16-<18%、≥18% ( t=4.154, 6.712, all P<0.01). Conclusions:PDW is correlated with the standard rate of warfarin anticoagulant stability, and can be used to predict the standard rate of warfarin anticoagulant stability.