Combinatorial Single-Cell Analyses of Granulocyte-Monocyte Progenitor Heterogeneity Reveals an Early Uni-potent Neutrophil Progenitor.

Granulocyte-monocyte progenitors (GMPs) have been previously defined for their potential to generate various myeloid progenies such as neutrophils and monocytes. Although studies have proposed lineage heterogeneity within GMPs, it is unclear if committed progenitors already exist among these progenitors and how they may behave differently during inflammation. By combining single-cell transcriptomic and proteomic analyses, we identified the early committed progenitor within the GMPs responsible for the strict production of neutrophils, which we designate as proNeu1. Our dissection of the GMP hierarchy led us to further identify a previously unknown intermediate proNeu2 population. Similar populations could be detected in human samples. proNeu1s, but not proNeu2s, selectively expanded during the early phase of sepsis at the expense of monocytes. Collectively, our findings help shape the neutrophil maturation trajectory roadmap and challenge the current definition of GMPs.

Alkenyl oxindole is a novel PROTAC moiety that recruits the CRL4DCAF11 E3 ubiquitin ligase complex for targeted protein degradation.

Alkenyl oxindoles have been characterized as autophagosome-tethering compounds (ATTECs), which can target mutant huntingtin protein (mHTT) for lysosomal degradation. In order to expand the application of alkenyl oxindoles for targeted protein degradation, we designed and synthesized a series of heterobifunctional compounds by conjugating different alkenyl oxindoles with bromodomain-containing protein 4 (BRD4) inhibitor JQ1. Through structure-activity relationship study, we successfully developed JQ1-alkenyl oxindole conjugates that potently degrade BRD4. Unexpectedly, we found that these molecules degrade BRD4 through the ubiquitin-proteasome system, rather than the autophagy-lysosomal pathway. Using pooled CRISPR interference (CRISPRi) screening, we revealed that JQ1-alkenyl oxindole conjugates recruit the E3 ubiquitin ligase complex CRL4DCAF11 for substrate degradation. Furthermore, we validated the most potent heterobifunctional molecule HL435 as a promising drug-like lead compound to exert antitumor activity both in vitro and in a mouse xenograft tumor model. Our research provides new employable proteolysis targeting chimera (PROTAC) moieties for targeted protein degradation, providing new possibilities for drug discovery.

High-entropy polymer produces a giant electrocaloric effect at low fields.

More than a decade of research on the electrocaloric (EC) effect has resulted in EC materials and EC multilayer chips that satisfy a minimum EC temperature change of 5 K required for caloric heat pumps1-3. However, these EC temperature changes are generated through the application of high electric fields4-8 (close to their dielectric breakdown strengths), which result in rapid degradation and fatigue of EC performance. Here we report a class of EC polymer that exhibits an EC entropy change of 37.5 J kg-1 K-1 and a temperature change of 7.5 K under 50 MV m-1, a 275% enhancement over the state-of-the-art EC polymers under the same field strength. We show that converting a small number of the chlorofluoroethylene groups in poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) terpolymer into covalent double bonds markedly increases the number of the polar entities and enhances the polar-nonpolar interfacial areas of the polymer. The polar phases in the polymer adopt a loosely correlated, high-entropy state with a low energy barrier for electric-field-induced switching. The polymer maintains performance for more than one million cycles at the low fields necessary for practical EC cooling applications, suggesting that this strategy may yield materials suitable for use in caloric heat pumps.

Skinny deeping: Uncovering immune cell behavior and function through imaging techniques.

As the largest organ of the body, the skin is a key barrier tissue with specialized structures where ongoing immune surveillance is critical for protecting the body from external insults. The innate immune system acts as first-responders in a coordinated manner to react to injury or infections, and recent developments in intravital imaging techniques have made it possible to delineate dynamic immune cell responses in a spatiotemporal manner. We review here key studies involved in understanding neutrophil, dendritic cell and macrophage behavior in skin and further discuss how this knowledge collectively highlights the importance of interactions and cellular functions in a systems biology manner. Furthermore, we will review emerging imaging technologies such as high-content proteomic screening, spatial transcriptomics and three-dimensional volumetric imaging and how these techniques can be integrated to provide a systems overview of the immune system that will further our current knowledge and lead to potential exciting discoveries in the upcoming decades.

Trp207 regulation of voltage-dependent activation of human Hv1 proton channel.

In voltage-gated Na+ and K+ channels, the hydrophobicity of noncharged residues in the S4 helix has been shown to regulate the S4 movement underlying the process of voltage-sensing domain (VSD) activation. In voltage-gated proton channel Hv1, there is a bulky noncharged tryptophan residue located at the S4 transmembrane segment. This tryptophan remains entirely conserved across all Hv1 members but is not seen in other voltage-gated ion channels, indicating that the tryptophan contributes different roles in VSD activation. The conserved tryptophan of human voltage-gated proton channel Hv1 is Trp207 (W207). Here, we showed that W207 modifies human Hv1 voltage-dependent activation, and small residues replacement at position 207 strongly perturbs Hv1 channel opening and closing, and the size of the side chain instead of the hydrophobic group of W207 regulates the transition between closed and open states of the channel. We conclude that the large side chain of tryptophan controls the energy barrier during the Hv1 VSD transition.

SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction.

Drug-target binding affinity prediction is a fundamental task for drug discovery and has been studied for decades. Most methods follow the canonical paradigm that processes the inputs of the protein (target) and the ligand (drug) separately and then combines them together. In this study we demonstrate, surprisingly, that a model is able to achieve even superior performance without access to any protein-sequence-related information. Instead, a protein is characterized completely by the ligands that it interacts. Specifically, we treat different proteins separately, which are jointly trained in a multi-head manner, so as to learn a robust and universal representation of ligands that is generalizable across proteins. Empirical evidences show that the novel paradigm outperforms its competitive sequence-based counterpart, with the Mean Squared Error (MSE) of 0.4261 versus 0.7612 and the R-Square of 0.7984 versus 0.6570 compared with DeepAffinity. We also investigate the transfer learning scenario where unseen proteins are encountered after the initial training, and the cross-dataset evaluation for prospective studies. The results reveals the robustness of the proposed model in generalizing to unseen proteins as well as in predicting future data. Source codes and data are available at https://github.com/huzqatpku/SAM-DTA.

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis. AVAILABILITY AND IMPLEMENTATION: The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.

MOTIVATION: RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation. RESULTS: In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ml4bio/RiboDiffusion.

S100A8/A9-activated IFNγ+ NK cells trigger ß-cell necroptosis in hepatitis B virus-associated liver cirrhosis.

BACKGROUND AND AIMS: Hepatitis B virus (HBV)-associated liver cirrhosis (LC), a common condition with high incidence and mortality rates, is often associated with diabetes mellitus (DM). However, the molecular mechanisms underlying impaired glucose regulation during HBV-associated LC remain unclear. METHODS: Data from 63 patients with LC and 62 patients with LC-associated DM were analysed. Co-culture of NK cells and islet ß cell lines were used to study the glucose regulation mechanism. A mouse model of LC was used to verify the effect of S100A8/A9 on the glucose regulation. RESULTS: Higher levels of interferon (IFN)-γ derived from natural killer (NK) cells and lower levels of insulin emerged in the peripheral blood of patients with both LC and DM compared with those from patients with LC only. IFN-γ derived from NK cells facilitated ß cell necroptosis and impaired insulin production. Furthermore, S100A8/A9 elevation in patients with both LC and DM was found to upregulate IFN-γ production in NK cells. Consistently, in the mouse model for LC, mice treated with carbon tetrachloride (CCL4) and S100A8/A9 exhibited increased blood glucose, impaired insulin production, increased IFN-γ, and increased ß cells necroptosis compared with those treated with CCL4. Mechanistically, S100A8/A9 activated the p38 MAPK pathway to increase IFN-γ production in NK cells. These effects were diminished after blocking RAGE. CONCLUSION: Together, the data indicate that IFN-γ produced by NK cells induces ß cell necroptosis via the S100A8/A9-RAGE-p38 MAPK axis in patients with LC and DM. Reduced levels of S100A8/A9, NK cells, and IFN-γ could be valuable for the treatment of LC with DM. Accumulation of S100A8/A9 in patients with LC may indicate the emergence of DM.

Ultrafast atomic view of laser-induced melting and breathing motion of metallic liquid clusters with MeV ultrafast electron diffraction.

Under the irradiation of an ultrafast intense laser, solid materials can be driven into nonequilibrium states undergoing an ultrafast solid-liquid phase transition. Understanding such nonequilibrium states is essential for scientific research and industrial applications because they exist in various processes including laser fusion and laser machining yet challenging in the sense that high resolution and single-shot capability are required for the measurements. Herein, an ultrafast diffraction technique with megaelectron-volt (MeV) electrons is used to resolve the atomic pathway over the entire laser-induced ultrafast melting process, from the initial loss of long-range order and the formation of high-density liquid to the progressive evolution of short-range order and relaxation into the metastable low-density liquid state. High-resolution measurements using electron pulse compression and a time-stamping technique reveal a coherent breathing motion of polyhedral clusters in transient liquid aluminum during the ultrafast melting process, as indicated by the oscillation of the interatomic distance between the center atom and atoms in the nearest-neighbor shell. Furthermore, contraction of interatomic distance was observed in a superheated liquid state with temperatures up to 6,000 K. The results provide an atomic view of melting accompanied with internal pressure relaxation and are critical for understanding the structures and properties of matter under extreme conditions.

A 3D-printed transepidermal microprojection array for human skin microbiome sampling.

Skin microbiome sampling is currently performed with tools such as swabs and tape strips to collect microbes from the skin surface. However, these conventional approaches may be unable to detect microbes deeper in the epidermis or in epidermal invaginations. We describe a sampling tool with a depth component, a transepidermal microprojection array (MPA), which captures microbial biomass from both the epidermal surface and deeper skin layers. We leveraged the rapid customizability of 3D printing to enable systematic optimization of MPA for human skin sampling. Evaluation of sampling efficacy on human scalp revealed the optimized MPA was comparable in sensitivity to swab and superior to tape strip, especially for nonstandard skin surfaces. We observed differences in species diversity, with the MPA detecting clinically relevant fungi more often than other approaches. This work delivers a tool in the complex field of skin microbiome sampling to potentially address gaps in our understanding of its role in health and disease.

Domain Switching Characteristics in Ga-Doped HfO2 Ferroelectric Thin Films with Low Coercive Field.

The gallium-doped hafnium oxide (Ga-HfO2) films with different Ga doping concentrations were prepared by adjusting the HfO2/Ga2O3 atomic layer deposition cycle ratio for high-speed and low-voltage operation in HfO2-based ferroelectric memory. The Ga-HfO2 ferroelectric films reveal a finely modulated coercive field (Ec) from 1.1 (HfO2/Ga2O3 = 32:1) to an exceptionally low 0.6 MV/cm (HfO2/Ga2O3 = 11:1). This modulation arises from the competition between domain nucleation and propagation speed during polarization switching, influenced by the intrinsic domain density and phase dispersion in the film with specific Ga doping concentrations. Higher Ec samples exhibit a nucleation-dominant switching mechanism, while lower Ec samples undergo a transition from a nucleation-dominant to a propagation-dominant reversal mechanism as the electric field increases. This work introduces Ga as a viable dopant for low Ec and offers insights into material design strategies for HfO2-based ferroelectric memory applications.

New recognition of the heart-brain axis and its implication in the pathogenesis and treatment of PTSD.

Post-traumatic stress disorder (PTSD) is a complex psychological disorder provoked by distressing experiences, and it remains without highly effective intervention strategies. The exploration of PTSD's underlying mechanisms is crucial for advancing diagnostic and therapeutic approaches. Current studies primarily explore PTSD through the lens of the central nervous system, investigating concrete molecular alterations in the cerebral area and neural circuit irregularities. However, the body's response to external stressors, particularly the changes in cardiovascular function, is often pronounced, evidenced by notable cardiac dysfunction. Consequently, examining PTSD with a focus on cardiac function is vital for the early prevention and targeted management of the disorder. This review undertakes a comprehensive literature analysis to detail the alterations in brain and heart structures and functions associated with PTSD. It also synthesizes potential mechanisms of heart-brain axis interactions relevant to the development of PTSD. Ultimately, by considering cardiac function, this review proposes novel perspectives for PTSD's prophylaxis and therapy.

Comparison of Short-term and Three-year Oncological Outcomes Between Robotic and Laparoscopic Gastrectomy for Gastric Cancer: A Large Multicenter Cohort Study.

OBJECTIVE: To compare the short-term and long-term outcomes between robotic gastrectomy (RG) and laparoscopic gastrectomy (LG) for gastric cancer. BACKGROUND: The clinical outcomes of RG over LG have not yet been effectively demonstrated. METHODS: This retrospective cohort study included 3599 patients with gastric cancer who underwent radical gastrectomy at eight high-volume hospitals in China from January 2015 to June 2019. Propensity score matching was performed between patients who received RG and LG. The primary end point was 3-year disease-free survival (DFS). RESULTS: After 1:1 propensity score matching, 1034 pairs of patients were enrolled in a balanced cohort for further analysis. The 3-year DFS in the RG and LG was 83.7% and 83.1% ( P =0.745), respectively, and the 3-year overall survival was 85.2% and 84.4%, respectively ( P =0.647). During 3 years of follow-up, 154 patients in the RG and LG groups relapsed (cumulative incidence of recurrence: 15.0% vs 15.0%, P =0.988). There was no significant difference in the recurrence sites between the 2 groups (all P >0.05). Sensitivity analysis showed that RG had comparable 3-year DFS (77.4% vs 76.7%, P =0.745) and overall survival (79.7% vs 78.4%, P =0.577) to LG in patients with advanced (pathologic T2-4a) disease, and the recurrence pattern within 3 years was also similar between the 2 groups (all P >0.05). RG had less intraoperative blood loss, lower conversion rate, and shorter hospital stays than LG (all P >0.05). CONCLUSIONS: For resectable gastric cancer, including advanced cases, RG is a safe approach with comparable 3-year oncological outcomes to LG when performed by experienced surgeons.

PCSK9 inhibitor added to high-intensity statin therapy to prevent cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention: a randomized, double- blind, placebo-controlled, multicenter SHAWN study.

BACKGROUND: It is currently uncertain whether the combination of a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor and high-intensity statin treatment can effectively reduce cardiovascular events in patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) for culprit lesions. METHODS: This study protocol describes a double-blind, randomized, placebo-controlled, multicenter study aiming to investigate the efficacy and safety of combining a PCSK9 inhibitor with high-intensity statin therapy in patients with ACS following PCI. A total of 1,212 patients with ACS and multiple lesions will be enrolled and randomly assigned to receive either PCSK9 inhibitor plus high-intensity statin therapy or high-intensity statin monotherapy. The randomization process will be stratified by sites, diabetes, initial presentation and use of stable (≥4 weeks) statin treatment at presentation. PCSK 9 inhibitor or its placebo is injected within 4 hours after PCI for the culprit lesion. The primary endpoint is the composite of cardiovascular death, myocardial infarction, stroke, re-hospitalization due to ACS or heart failure, or any ischemia-driven coronary revascularization at 1-year follow-up between 2 groups. Safety endpoints mean PCSK 9 inhibitor and statin intolerance. CONCLUSION: The SHAWN study has been specifically designed to evaluate the effectiveness and safety of adding a PCSK9 inhibitor to high-intensity statin therapy in patients who have experienced ACS following PCI. The primary objective of this study is to generate new evidence regarding the potential benefits of combining a PCSK9 inhibitor with high-intensity statin treatment in reducing cardiovascular events among these patients.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.

We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.

AcrNET: predicting anti-CRISPR with deep learning.

MOTIVATION: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. RESULTS: On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. AVAILABILITY AND IMPLEMENTATION: Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.

LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography.

In recent years, digital dentistry has increasingly utilized advanced image analysis techniques, such as image classification and disease diagnosis, to improve clinical outcomes. Despite these advances, the lack of comprehensive benchmark datasets is a significant barrier. To address this gap, our research team develop LMCD-OR, a substantial collection of oral radiograph images designed to support extensive artificial intelligence (AI)-driven diagnostics. LMCD-OR comprises 3,818 digital imaging and communications in medicine (DICOM) oral X-ray images from local medical institutions that are meticulously annotated to provide broad category information for both primary dental outpatient services and detailed secondary disease diagnoses. This dataset is engineered to train and validate multiclassification models to improve the precision and scope of oral disease diagnostics. To ensure robust dataset validation, we employ four cutting-edge visual neural network classification models as benchmarks. These models are tested against rigorous performance metrics, demonstrating the ability of the dataset to support advanced image classification and disease diagnosis tasks. LMCD-OR is publicly available at http://dentaldataset.zeroacademy.net .

The MdNAC72-MdABI5 module acts as an interface integrating jasmonic acid and gibberellin signals and undergoes ubiquitination-dependent degradation regulated by MdSINA2 in apple.

Jasmonic acid (JA) and gibberellin (GA) coordinately regulate plant developmental programs and environmental cue responses. However, the fine regulatory network of the cross-interaction between JA and GA remains largely elusive. In this study, we demonstrate that MdNAC72 together with MdABI5 positively regulates anthocyanin biosynthesis through an exquisite MdNAC72-MdABI5-MdbHLH3 transcriptional cascade in apple. MdNAC72 interacts with MdABI5 to promote the transcriptional activation of MdABI5 on its target gene MdbHLH3 and directly activates the transcription of MdABI5. The MdNAC72-MdABI5 module regulates the integration of JA and GA signals in anthocyanin biosynthesis by combining with JA repressor MdJAZ2 and GA repressor MdRGL2a. MdJAZ2 disrupts the MdNAC72-MdABI5 interaction and attenuates the transcriptional activation of MdABI5 by MdNAC72. MdRGL2a sequesters MdJAZ2 from the MdJAZ2-MdNAC72 protein complex, leading to the release of MdNAC72. The E3 ubiquitin ligase MdSINA2 is responsive to JA and GA signals and promotes ubiquitination-dependent degradation of MdNAC72. The MdNAC72-MdABI5 interface fine-regulates the integration of JA and GA signals at the transcriptional and posttranslational levels by combining MdJAZ2, MdRGL2a, and MdSINA2. In summary, our findings elucidate the fine regulatory network connecting JA and GA signals with MdNAC72-MdABI5 as the core in apple.

The relative and combined ability of stress hyperglycemia ratio and N-terminal pro-B-type natriuretic peptide to predict all-cause mortality in diabetic patients with multivessel coronary artery disease.

BACKGROUND: Stress hyperglycemia ratio (SHR) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are independently associated with increased mortality risk in diabetic patients with coronary artery disease (CAD). However, the role of these biomarkers in patients with diabetes and multivessel disease (MVD) remains unknown. The present study aimed to assess the relative and combined abilities of these biomarkers to predict all-cause mortality in patients with diabetes and MVD. METHODS: This study included 1148 diabetic patients with MVD who underwent coronary angiography at Tianjin Chest Hospital between January 2016 and December 2016. The patients were divided into four groups according to their SHR (SHR-L and SHR-H) and NT-proBNP (NT-proBNP-L and NT-proBNP-H) levels. The primary outcome was all-cause mortality. Multivariate Cox regression analyses were performed to evaluate the association of SHR and NT-proBNP levels with all-cause mortality. RESULTS: During a mean 4.2 year follow-up, 138 patients died. Multivariate analysis showed that SHR and NT-proBNP were strong independent predictors of all-cause mortality in diabetic patients with MVD (SHR: HR hazard ratio [2.171; 95%CI 1.566-3.008; P < 0.001; NT-proBNP: HR: 1.005; 95%CI 1.001-1.009; P = 0.009). Compared to patients in the first (SHR-L and NT-proBNP-L) group, patients in the fourth (SHR-H and NT-proBNP-H) group had the highest mortality risk (HR: 12.244; 95%CI 5.828-25.721; P < 0.001). The areas under the curve were 0.615(SHR) and 0.699(NT-proBNP) for all-cause mortality. Adding either marker to the original models significantly improved the C-statistic and integrated discrimination improvement values (all P < 0.05). Moreover, combining SHR and NT-proBNP levels into the original model provided maximal prognostic information. CONCLUSIONS: SHR and NT-proBNP independently and jointly predicted all-cause mortality in diabetic patients with MVD, suggesting that strategies to improve risk stratification in these patients should incorporate SHR and NT-porBNP into risk algorithms.