Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities.

Disordered proteins are conformationally flexible proteins that are biologically important and have been implicated in devastating diseases such as Alzheimer's disease and cancer. Unlike stably folded structured proteins, disordered proteins sample a range of different conformations that needs to be accounted for. Here, we treat disordered proteins as polymer chains, and compute a dimensionless quantity called instantaneous shape ratio (Rs), as Rs = Ree2/Rg2, where Ree is end-to-end distance and Rg is radius of gyration. Extended protein conformations tend to have high Ree compared with Rg, and thus have high Rs values, whereas compact conformations have smaller Rs values. We use a scatter plot of Rs (representing shape) against Rg (representing size) as a simple map of conformational landscapes. We first examine the conformational landscape of simple polymer models such as Random Walk, Self-Avoiding Walk, and Gaussian Walk (GW), and we notice that all protein/polymer maps lie within the boundaries of the GW map. We thus use the GW map as a reference and, to assess conformational diversity, we compute the fraction of the GW conformations (fC) covered by each protein/polymer. Disordered proteins all have high fC scores, consistent with their disordered nature. Each disordered protein accesses a different region of the reference map, revealing differences in their conformational ensembles. We additionally examine the conformational maps of the nonviral gene delivery vector polyethyleneimine at various protonation states, and find that they resemble disordered proteins, with coverage of the reference map decreasing with increasing protonation state, indicating decreasing conformational diversity. We propose that our method of combining Rs and Rg in a scatter plot generates a simple, meaningful map of the conformational landscape of a disordered protein, which in turn can be used to assess conformational diversity of disordered proteins.

Effect of Nitrogen on Growth and Optical Properties of Single-Crystal Diamond Synthesized by Chemical Vapor Deposition.

Concurrently achieving high growth rate and high quality in single-crystal diamonds (SCDs) is significantly challenging. The growth rate of SCDs synthesized by microwave plasma chemical vapor deposition (MPCVD) was enhanced by introducing N2 into the typical CH4-H2 gas mixtures. The impact of nitrogen vacancy (NV) center concentration on growth rate, surface morphology, and lattice binding structure was investigated. The SCDs were characterized through Raman spectroscopy, photoluminescence (PL) spectroscopy, and X-ray photoelectron spectroscopy. It was found that the saturation growth rate was increased up to 45 µm/h by incorporating 0.8-1.2% N2 into the gas atmosphere, which is 4.5 times higher than the case without nitrogen addition. Nitrogen addition altered the growth mode from step-flow to bidimensional nucleation, leading to clustered steps and a rough surface morphology, followed by macroscopically pyramidal hillock formation. The elevation of nitrogen content results in a simultaneous escalation of internal stress and defects. XPS analysis confirmed chemical bonding between nitrogen and carbon, as well as non-diamond carbon phase formation at 0.8% of nitrogen doping. Furthermore, the emission intensity of NV-related defects from PL spectra changed synchronously with N2 concentrations (0-1.5%) during diamond growth, indicating that the formation of NV centers activated the diamond lattice and facilitated nitrogen incorporation into it, thereby accelerating chemical reaction rates for achieving high-growth-rate SCDs.

Comprehensive Comparison and Critical Assessment of RNA-Specific Force Fields.

Molecular dynamics simulations play a pivotal role in elucidating the dynamic behaviors of RNA structures, offering a valuable complement to traditional methods such as nuclear magnetic resonance or X-ray. Despite this, the current precision of RNA force fields lags behind that of protein force fields. In this work, we systematically compared the performance of four RNA force fields (ff99bsc0χOL3, AMBERDES, ff99OL3_CMAP1, AMBERMaxEnt) across diverse RNA structures. Our findings highlight significant challenges in maintaining stability, particularly with regard to cross-strand and cross-loop hydrogen bonds. Furthermore, we observed the limitations in accurately describing the conformations of nonhelical structural motif, terminal nucleotides, and also base pairing and base stacking interactions by the tested RNA force fields. The identified deficiencies in existing RNA force fields provide valuable insights for subsequent force field development. Concurrently, these findings offer recommendations for selecting appropriate force fields in RNA simulations.

A Revised Formation of Trace Ratio LDA for Small Sample Size Problem.

Linear discriminant analysis (LDA) is a classic tool for supervised dimensionality reduction. Because the projected samples can be classified effectively, LDA has been successfully applied in many applications. Among the variants of LDA, trace ratio LDA (TR-LDA) is a classic form due to its explicit meaning. Unfortunately, when the sample size is much smaller than the data dimension, the algorithm for solving TR-LDA does not converge. The so-called small sample size (SSS) problem severely limits the application of TR-LDA. To solve this problem, we propose a revised formation of TR-LDA, which can be applied to datasets with different sizes in a unified form. Then, we present an optimization algorithm to solve the proposed method, explain why it can avoid the SSS problem, and analyze the convergence and computational complexity of the optimization algorithm. Next, based on the introduced theorems, we quantitatively elaborate on when the SSS problem will occur in TR-LDA. Finally, the experimental results on real-world datasets demonstrate the effectiveness of the proposed method.

Sparse Trace Ratio LDA for Supervised Feature Selection.

Classification is a fundamental task in the field of data mining. Unfortunately, high-dimensional data often degrade the performance of classification. To solve this problem, dimensionality reduction is usually adopted as an essential preprocessing technique, which can be divided into feature extraction and feature selection. Due to the ability to obtain category discrimination, linear discriminant analysis (LDA) is recognized as a classic feature extraction method for classification. Compared with feature extraction, feature selection has plenty of advantages in many applications. If we can integrate the discrimination of LDA and the advantages of feature selection, it is bound to play an important role in the classification of high-dimensional data. Motivated by the idea, we propose a supervised feature selection method for classification. It combines trace ratio LDA with l2,p -norm regularization and imposes the orthogonal constraint on the projection matrix. The learned row-sparse projection matrix can be used to select discriminative features. Then, we present an optimization algorithm to solve the proposed method. Finally, the extensive experiments on both synthetic and real-world datasets indicate the effectiveness of the proposed method.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling.

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.

Synthesis and degradation of the cyclic dinucleotide messenger c-di-AMP in the hyperthermophilic archaeon Pyrococcus yayanosii.

Cyclic di-adenosine monophosphate (c-di-AMP) is a newly identified prokaryotic cyclic dinucleotide second messenger well elucidated in bacteria, while less studied in archaea. Here, we describe the enzymes involved in c-di-AMP metabolism in the hyperthermophilic archaeon Pyrococcus yayanosii. Our results demonstrate that c-di-AMP is synthesized from two molecules of ATP by diadenylate cyclase (DAC) and degraded into pApA and then to AMP by a DHH family phosphodiesterase (PDE). DAC can be activated by a wider variety of ions, using two conserved residues, D188 and E244, to coordinate divalent metal ions, which is different from bacterial CdaA and DisA. PDE possesses a broad substrate spectrum like bacterial DHH family PDEs but shows a stricter base selection between A and G in cyclic dinucleotides hydrolysis. PDE shows differences in substrate binding patches from bacterial counterparts. C-di-AMP was confirmed to exist in Thermococcus kodakarensis cells, and the deletion of the dac or pde gene supports that the synthesis and degradation of c-di-AMP are catalyzed by DAC and PDE, respectively. Our results provide a further understanding of the metabolism of c-di-AMP in archaea.

Meta-Analysis of Single-Cell RNA-Seq Data Reveals the Mechanism of Formation and Heterogeneity of Tertiary Lymphoid Organ in Vascular Disease.

BACKGROUND: Tertiary lymphoid organs (TLOs) are ectopic lymphoid organs developed in nonlymphoid tissues with chronic inflammation, but little is known about their existence in different types of vascular diseases and the mechanism that mediated their development. METHODS: To take advantage of single-cell RNA sequencing techniques, we integrated 28 single-cell RNA sequencing data sets containing 5 vascular disease models (atherosclerosis, abdominal aortic aneurysm, intimal hyperplasia, isograft, and allograft) to explore TLOs existence and environment supporting its growth systematically. We also searched Medline, Embase, PubMed, and Web of Science from inception to January 2022 for published histological images of vascular remodeling for histological evidence to support TLO genesis. RESULTS: Accumulation and infiltration of innate and adaptive immune cells have been observed in various remodeling vessels. Interestingly, the proportion of such immune cells incrementally increases from atherosclerosis to intimal hyperplasia, abdominal aortic aneurysm, isograft, and allograft. Importantly, we uncovered that TLO structure cells, such as follicular helper T cells and germinal center B cells, present in all remodeled vessels. Among myeloid cells and lymphocytes, inflammatory macrophages, and T helper 17 cells are the major lymphoid tissue inducer cells which were found to be positively associated with the numbers of TLO structural cells in remodeled vessels. Vascular stromal cells also actively participate in vascular TLO genesis by communicating with myeloid cells and lymphocytes via CCLs (C-C motif chemokine ligands), CXCL (C-X-C motif ligand), lymphotoxin, BMP (bone morphogenetic protein) chemotactic, FGF-2 (fibroblast growth factor-2), and IGF (insulin growth factor) proliferation mechanisms, particularly for lymphoid tissue inducer cell aggregation. Additionally, the interaction between stromal cells and immune cells modulates extracellular matrix remodeling. Among TLO structure cells, follicular helper T, and germinal center B cells have strong interactions via TCR (T-cell receptor), CD40 (cluster of differentiation 40), and CXCL signaling, to promote the development and maturation of the germinal center in TLO. Consistently, by reviewing the histological images from the literature, TLO genesis was found in those vascular remodeling models. CONCLUSIONS: Our analysis showed the existence of TLOs across 5 models of vascular diseases. The mechanisms that support TLOs formation in different models are heterogeneous. This study could be a valuable resource for understanding and discovering new therapeutic targets for various forms of vascular disease.

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions.

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.

Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as potential drugs with high selectivity and safety. However, current experimental methods have limitations to identify allosteric sites. Therefore, molecular dynamics simulation based on empirical force field becomes an important complement of experimental methods. Moreover, the precision and efficiency of current force fields need improvement. Deep learning and reweighting methods were used to train allosteric protein-specific precise force field (named APSF). Multiple allosteric proteins were used to evaluate the performance of APSF. The results indicate that APSF can capture different types of allosteric pockets and sample multiple energy-minimum reference conformations of allosteric proteins. At the same time, the efficiency of conformation sampling for APSF is higher than that for ff14SB. These findings confirm that the newly developed force field APSF can be effectively used to identify the allosteric pocket that can be further used to screen potential allosteric drugs based on these pockets.

Clinical characteristics and prognosis differences between isolated right and left ventricular myocardial infarction in the Chinese population: a retrospective study.

Background and aims: Acute myocardial infarction (AMI) is divided into left ventricular myocardial infarction (LVMI) and right ventricular myocardial infarction (RVMI) according to the regions of myocardial ischemic necrosis. Clinical characteristics, treatment strategies, and prognosis differences between isolated RVMI and LVMI have not been well characterized. This study aimed to explore this difference of patients with isolated RVMI and LVMI. Methods: This retrospective cohort study included 3,506 patients hospitalized with coronary angiography diagnosed type 1 myocardial infarction (MI). Characteristics of admission and treatment strategies were compared in patients with isolated RVMI and LVMI. COX proportional hazards models with and without inverse probability of treatment weighting (IPTW) adjustment were performed to estimate the difference in all-cause and cardiovascular mortality between the two groups. Results: In this retrospective study, we found the frequency of isolated RVMI was significantly lower in the population than that of isolated LVMI (406 (11.6%) vs 3,100 (88.4%)). Patients with isolated RVMI have similar age, sex, and comorbidities to the patients with isolated LVMI. However, patients with isolated RVMI have lower heart rate and blood pressure, but higher rates of cardiogenic shock and atrioventricular block. It is noteworthy that patients with isolated RVMI are more likely to be complicated with the multivessel lesion. Patients with isolated RVMI have lower risk of all-cause mortality (HR 0.36; 95% CI [0.24-0.54], p < 0.001) and cardiovascular mortality (HR 0.37; 95% CI [0.22-0.62], p < 0.001) than patients with isolated LVMI. Conclusions: This study showed that patients with isolated RVMI and LVMI have similar baseline characteristics. However, the clinical manifestations were different in the isolated RVMI and LVMI patients. This study revealed a better prognosis of isolated RVMI patients compared to isolated LVMI, which indicates the ischemic region could be considered in AMI risk stratification models for better assessment of risk for adverse clinical events.

New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model.

In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained after daily, weekly and monthly weighting. Based on the 5-min high-frequency data of the SSE index, the fractional integration Realized GARCH model, Realized HAR GARCH model and Realized GARCH model are investigated for their fitting effect and predictive ability on market volatility, and Monte Carlo simulations are conducted from the error terms obeying normal distribution, t-distribution and chi-square distribution so as to compare the RMSE and MAE of the three types of models with respect to conditional variance. The empirical results show that the fractionally integrated Realized GARCH model is found to better capture the long-run correlation in volatility in certain intervals by comparing the theoretical and sample auto-correlation functions, while the overall predictive power of the model is better than the other two models. Finally, it provides technical support and suggestions for investors' risk control.

Effects of alloying elements on diamond/Cu interface properties based on first-principles calculations.

Diamond/copper composites with high thermal conductivity and a variable thermal expansion coefficient are promising materials for thermal management applications. However, achieving the desired thermal conductivity of the composite material is difficult due to detachment or weak bonding between diamond and Cu. The interfacial properties of diamond/Cu composites can be improved using metal matrix alloying methods. In this study, we investigate the effects of alloying elements (B, Cr, Hf, Mo, Nb, Si, Ti, V, Zr) on the interfacial properties of diamond/Cu using first-principles calculations. Results showed that all alloying components could increase the interfacial bonding of diamond/Cu. Analysis of the electronic structure revealed that increased interfacial bonding strength after doping was the result of the stronger bonding of the alloying element atoms to the C atoms. The C atoms in the first layer of diamond at the interface formed wave peaks near the Fermi energy level after doping with B or Si atoms, facilitating electron-phonon interaction at the interface. The phonon properties of B4C and SiC were similar to those of diamond, which facilitated phonon-phonon coupling. B and Si were shown to be better alloying elements when interfacial bond strength and heat transfer were considered.

Sparse PCA via l2,p-Norm Regularization for Unsupervised Feature Selection.

In the field of data mining, how to deal with high-dimensional data is an inevitable topic. Since it does not rely on labels, unsupervised feature selection has attracted a lot of attention. The performance of spectral-based unsupervised methods depends on the quality of the constructed similarity matrix, which is used to depict the intrinsic structure of data. However, real-world data often contain plenty of noise features, making the similarity matrix constructed by original data cannot be completely reliable. Worse still, the size of a similarity matrix expands rapidly as the number of samples rises, making the computational cost increase significantly. To solve this problem, a simple and efficient unsupervised model is proposed to perform feature selection. We formulate PCA as a reconstruction error minimization problem, and incorporate a l2,p-norm regularization term to make the projection matrix sparse. The learned row-sparse and orthogonal projection matrix is used to select discriminative features. Then, we present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically. Finally, experiments on both synthetic and real-world data sets demonstrate the effectiveness of our proposed method.

Unsupervised Feature Selection With Weighted and Projected Adaptive Neighbors.

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels. Many unsupervised methods perform feature selection by exploring spectral analysis and manifold learning, such that the intrinsic structure of data can be preserved. However, most of these methods ignore a fact: due to the existence of noise features, the intrinsic structure directly built from original data may be unreliable. To solve this problem, a new unsupervised feature selection model is proposed. The graph structure, feature weights, and projection matrix are learned simultaneously, such that the intrinsic structure is constructed by the data that have been feature weighted and projected. For each data point, its nearest neighbors are acquired in the process of graph construction. Therefore, we call them adaptive neighbors. Besides, an additional constraint is added to the proposed model. It requires that a graph, corresponding to a similarity matrix, should contain exactly c connected components. Then, we present an optimization algorithm to solve the proposed model. Next, we discuss the method of determining the regularization parameter γ in our proposed method and analyze the computational complexity of the optimization algorithm. Finally, experiments are implemented on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed method.

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration.

The consensus that the digital economy drives urban-rural integration has been gradually reached both in practice and theory. Besides, the way by which the digital economy drives urban-rural integration remains updated iteratively. The coming period is an important opportunity to break down the dualistic urban-rural structure and improve the urban-rural integration development. It is also a critical stage for China to promote the deep integration of the digital economy and the real economy. In this study, the intrinsic mechanism of the digital economy in driving the four dimensions of urban-rural integration was elaborated. An analysis was made of the spatial effects in 30 provinces (municipalities and autonomous regions) of China during 2011-2019 using Bivariate Global Moran's I and geographically and temporally weighted regression (GTWR) models. As revealed by the results: (1) the digital economy and the four dimensions of urban-rural integration advance steadily, in which the convergence degree of urban and rural resident consumption is comparatively higher; (2) there is a significant spatial auto-correlation between the digital economy and the four dimensions of urban-rural integration, with the influence gradually strengthened with time; (3) the digital economy exerts mainly positive impacts on the equivalent allocation of urban and rural factors, integration of three industries in urban and rural areas, and convergence degree of urban and rural resident consumption, but inhibits the equalization of urban and rural public services in nearly half research areas; (4) both digital equipment basis and user basis play a vital role in promoting the four dimensions of urban-rural integration.

Surface Nanodroplet-Based Extraction Combined with Offline Analytic Techniques for Chemical Detection and Quantification.

Liquid-liquid extraction based on surface nanodroplets can be a green and sustainable technique to extract and concentrate analytes from a sample flow. However, because of the extremely small volume of each droplet (<10 fL, tens of micrometers in base radius and a few or less than 1 µm in height), only a few in situ analytical techniques, such as surface-enhanced Raman spectroscopy, were applicable for the online detection and analysis based on nanodroplet extraction. To demonstrate the versatility of surface nanodroplet-based extraction, in this work, the formation of octanol surface nanodroplets and extraction were performed inside a 3 m Teflon capillary tube. After extraction, surface nanodroplets were collected by injecting air into the tube, by which the contact line of surface droplets was collected by the capillary force. As the capillary allows for the formation of ∼1012 surface nanodroplets on the capillary wall, ≥2 mL of octanol can be collected after extraction. The volume of the collected octanol was enough for the analysis of offline analytical techniques such as UV-vis, GC-MS, and others. Coupled with UV-vis, reliable extraction and detection of two common water pollutants, triclosan and chlorpyrifos, was shown by a linear relationship between the analyte concentration in the sample solution and UV-vis absorbance. Moreover, the limit of detection (LOD) as low as 2 × 10-9 M for triclosan (∼0.58 µg/L) and 3 × 10-9 M for chlorpyrifos (∼1.05 µg/L) could be achieved. The collected surface droplets were also analyzed via gas chromatography (GC) and fluorescence microscopy. Our work shows that surface nanodroplet extraction may potentially streamline the process in sample pretreatment for sensitive chemical detection and quantification by using common analytic tools.

Base-specific RNA force field improving the dynamics conformation of nucleotide.

RNA plays a key role in numerous biological processes. Traditional experimental methods have difficulties capturing the structure and dynamic conformation of RNA. Thus, Molecular dynamic simulations (MDs) has become an essential complementary for RNA experiment. However, state-of-the-art RNA force fields have two major limitations of overestimation base stacking propensity and generation of a high ratio of intercalated conformations. Therefore, a two-step strategy was used to optimize the parameters of ff99bsc0χOL3 (named BSFF1) to improve these limitations, which as well adjusted the unbonded parameters of nucleobase heavy atoms and added ζ/α grid-based energy correction map energy term with reweighting. MD simulations of tetranucleotides indicate that BSFF1 can significantly decrease the ratio of intercalated conformations. Tests of single-strand RNA and kink-turn show that BSFF1 force field can reproduce more accurate conformers than ff99bsc0χOL3 force field. BSFF1 can also stabilize the conformers of duplex and riboswitch. The successful ab initio folding of tetraloop further supports the performance of BSFF1. These findings confirm that the newly developed force field BSFF1 can improve the conformer sampling of RNA.

Multimorbidity and combined interventions for patients with coronary heart disease in Chinese population: Latent class analysis of a multi-center study.

BACKGROUND AND AIMS: This study aimed to explore the profile of multimorbidity phenotype clusters and their discrepancy in mortality and the efficiency of combined interventions on blood pressure, glucose and lipid in each cluster. METHODS: Fine and Gray competing risk regression models and Kaplan-Meier curves were used to assess the association between multimorbidity and mortality and rehospitalization. Fine and Gray competing risk regression models and subgroup analyses were used to estimate the relations between combined interventions and mortality. RESULTS: Three distinct multimorbidity clusters were observed: Class 1 named severe class, Class 2 termed moderate class, and Class 3 named mild class. Competing risk regression models revealed that patients in Class 1 have the greatest burden of mortality and rehospitalization compared to Class 3 after confounder adjustment, with HRs 1.43 (95% CI 1.30-1.56, P < 0.001) and 2.97 (95% CI 2.74-3.21, P < 0.001), respectively. The patients in Class 2 have moderate risk of mortality and rehospitalization compared to Class 3 after confounder adjustment, with HRs 1.41 (95% CI 1.30-1.52, P < 0.001) and 2.39 (95% CI 2.23-2.56, P < 0.001), respectively. Furthermore, we found that combined interventions on blood pressure, glucose and lipid simultaneously could further benefit on survival compared to each individual intervention or two in combine. CONCLUSIONS: This study found that multimorbidity among patients with CHD was common and increased the risks of death and rehospitalization. Three multimorbidity clusters that were significantly associated with death and rehospitalization were identified. Simultaneous intervention on blood pressure, glucose and lipid level may further benefit CHD patient in survival.