Impact of Pulmonary Ventilation Dysfunction on Prognosis of Patients with Coronary Artery Disease: A Single-Center, Observational Study.

Background: Patients with coronary artery disease (CAD) often experience pulmonary ventilation dysfunction following their initial event. However, there is insufficient research exploring the relationship between this dysfunction and CAD prognosis. Methods: To address this gap, a retrospective observational study was conducted involving 3800 CAD patients without prior pulmonary ventilation disease who underwent cardiopulmonary exercise testing (CPET) during hospitalization between November 2015 and September 2021. The primary endpoint was a composite of major adverse cardiovascular events (MACE), such as death, myocardial infarction (MI), repeat revascularization, and stroke. Propensity score matching (PSM) was used to minimize selection bias between the two groups, with a subgroup analysis stratified by smoking status. Results: The results showed that patients were divided into normal (n = 2159) and abnormal (n = 1641) groups based on their pulmonary ventilation function detected by CPET, with 1469 smokers and 2331 non-smokers. The median follow-up duration was 1237 (25-75% interquartile range 695-1596) days. The primary endpoint occurred in 390 patients (10.26%). 1472 patients in each of the two groups were enrolled in the current analysis after PSM, respectively. However, pulmonary function was not associated with MACE before (hazard ratio (HR) 1.20, 95% confidence interval (95% CI) 0.99-1.47; Log-rank p = 0.069) or after PSM (HR 1.07, 95% CI 0.86-1.34; Log-rank p = 0.545) among the entire population. Nonetheless, pulmonary ventilation dysfunction was significantly associated with an increased risk of MACE in smoking patients (HR 1.65, 95% CI 1.25-2.18; p < 0.001) but not in non-smoking patients (HR 0.81, 95% CI 0.60-1.09; p = 0.159). In addition, there was a significant interaction between current smoking status and pulmonary ventilation dysfunction on MACE (p for interaction < 0.001). Conclusions: Pulmonary ventilation dysfunction identified through CPET was independently associated with long-term poor prognosis in smoking patients with CAD but not in the overall population.

Hypoxia-induced Semaphorin 3A promotes the development of endometriosis through regulating macrophage polarization.

BACKGROUND: Semaphorin 3A (Sema3A) is a member of neural guidance factor family well-known for inducing the collapse of nerve cell growth cone and regulating nerve redistribution. It also has been characterized as an immunoregulatory and tumor promoting factor. Our previous study showed that Sema3A was involved in the regulation of sympathetic innervation and neuropathic pain of endometriosis. Nevertheless, the role of Sema3A in the development of endometriosis and its potential upstreaming factor are still not clear. METHODS: Histology experiments were carried to detect the expression of Sema3A, hypoxia -inducible factor 1α (HIF-1α) and the distribution of macrophages. Cell experiments were used to explore the effect of Sema3A on the proliferation and migration of endometrial stromal cells (ESCs) and to confirm the regulatory action of HIF-1α on Sema3A. In vivo experiments were carried out to explore the role of Sema3A on the development of endometriosis. RESULTS: Sema3A was highly expressed in endometriotic lesions and could enhanced the proliferation and migration abilities of ESCs. Aberrant macrophage distribution was found in endometriotic lesions. Sema3A also promoted the differentiation of monocytes into anti-inflammatory macrophages, so indirectly mediating the proliferation and migration of ESCs. Hypoxic microenvironment induced Sema3A mRNA and protein expression in ESCs via HIF-1α. Administration of Sema3A promoted the development of endometriosis in a mouse model. CONCLUSIONS: Sema3A, which is regulated by HIF-1α, is a promoting factor for the development of endometriosis. Targeting Sema3A may be a potential treatment strategy to control endometriotic lesions.

Drug-Target Interaction Prediction Based on an Interactive Inference Network.

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Impact of Combined Modulation of Two Potassium Ion Currents on Spiral Waves and Turbulent States in the Heart.

In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches. While previous studies have proposed some combination strategies, further reinforcement and supplementation are required, particularly in the context of controlling arrhythmias through the combined regulation of two potassium ion currents. This study employs the Luo-Rudy phase I cardiac model, modulating the maximum conductance of the time-dependent potassium current and the time-independent potassium current, to investigate the effects of this combined modulation on spiral waves and turbulent states. Numerical simulation results indicate that, compared to modulating a single current, combining reductions in the conductance of two potassium ion currents can rapidly control spiral waves and turbulent states in a short duration. This implies that employing blockers for both potassium ion currents concurrently represents a more efficient control strategy. The control outcomes of this study represent a novel and effective combination for antiarrhythmic interventions, offering potential avenues for new antiarrhythmic drug targets.

A contrastive learning approach to integrate spatial transcriptomics and histological images.

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Acupuncture and tuina knowledge graph with prompt learning.

Introduction: Acupuncture and tuina, acknowledged as ancient and highly efficacious therapeutic modalities within the domain of Traditional Chinese Medicine (TCM), have provided pragmatic treatment pathways for numerous patients. To address the problems of ambiguity in the concept of Traditional Chinese Medicine (TCM) acupuncture and tuina treatment protocols, the lack of accurate quantitative assessment of treatment protocols, and the diversity of TCM systems, we have established a map-filling technique for modern literature to achieve personalized medical recommendations. Methods: (1) Extensive acupuncture and tuina data were collected, analyzed, and processed to establish a concise TCM domain knowledge base. (2)A template-free Chinese text NER joint training method (TemplateFC) was proposed, which enhances the EntLM model with BiLSTM and CRF layers. Appropriate rules were set for ERE. (3) A comprehensive knowledge graph comprising 10,346 entities and 40,919 relationships was constructed based on modern literature. Results: A robust TCM KG with a wide range of entities and relationships was created. The template-free joint training approach significantly improved NER accuracy, especially in Chinese text, addressing issues related to entity identification and tokenization differences. The KG provided valuable insights into acupuncture and tuina, facilitating efficient information retrieval and personalized treatment recommendations. Discussion: The integration of KGs in TCM research is essential for advancing diagnostics and interventions. Challenges in NER and ERE were effectively tackled using hybrid approaches and innovative techniques. The comprehensive TCM KG our built contributes to bridging the gap in TCM knowledge and serves as a valuable resource for specialists and non-specialists alike.

Classifying breast cancer using multi-view graph neural network based on multi-omics data.

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

An individualized criterion for left bundle branch capture in patients with a narrow QRS complex.

BACKGROUND: Left bundle branch (LBB) pacing (LBBP) is a physiological pacing; however, the accuracy of current electrocardiographic criteria for LBBP remains inadequate. OBJECTIVE: The purpose of this study was to establish a novel individualized criterion to improve the accuracy of LBBP determination in patients with a narrow QRS complex. METHODS: Patients in whom both LBBP and left ventricular septal pacing (LVSP) were acquired during operation were enrolled. LBB conduction time (LBBCT) was measured from LBB potential (LBBpo) to intrinsic QRS onset. LBBpo-V6RWPT, Native-V6RWPT, and Paced-V6RWPT were respectively measured from LBBpo, intrinsic QRS onset, and stimulus to R-wave peak in V6. ΔV6RWPT was the difference value between Paced-V6RWPT and Native-V6RWPT. The accuracy of ΔV6RWPT criterion for determining LBBP was evaluated. RESULTS: In all 71 enrolled patients, ΔV6RWPT was <30 ms during LBBP (21.3 ± 4.6 ms; range 9.3-28.3 ms) but was >30 ms during LVSP (38.5 ± 4.6 ms; range 31.1-47.0 ms). The probability distribution of ΔV6RWPT was well separated between LBBP and LVSP. Sensitivity and specificity of the novel criterion of "ΔV6RWPT <30 ms" for determining LBBP both were 100%. However, the optimal cutoff value of Paced-V6RWPT for validation of LBBP was 64.2 ms, and sensitivity and specificity were 84.5% and 97.2%, respectively. Paced-V6RWPT during LBBP was equivalent to LBBpo-V6RWPT in all patients. There was a strong linear correlation between Native-V6RWPT and LBBpo-V6RWPT (r = 0.796; P <.001). CONCLUSION: ΔV6RWPT could be an accurate individualized criterion for determining LBB capture with high sensitivity and specificity and was superior over the fixed Paced-V6RWPT criterion.

Comprehensive mapping of lunar surface chemistry by adding Chang'e-5 samples with deep learning.

Lunar surface chemistry is essential for revealing petrological characteristics to understand the evolution of the Moon. Existing chemistry mapping from Apollo and Luna returned samples could only calibrate chemical features before 3.0 Gyr, missing the critical late period of the Moon. Here we present major oxides chemistry maps by adding distinctive 2.0 Gyr Chang'e-5 lunar soil samples in combination with a deep learning-based inversion model. The inferred chemical contents are more precise than the Lunar Prospector Gamma-Ray Spectrometer (GRS) maps and are closest to returned samples abundances compared to existing literature. The verification of in situ measurement data acquired by Chang'e 3 and Chang'e 4 lunar rover demonstrated that Chang'e-5 samples are indispensable ground truth in mapping lunar surface chemistry. From these maps, young mare basalt units are determined which can be potential sites in future sample return mission to constrain the late lunar magmatic and thermal history.

Patient-derived organoids as personalized avatars and a potential immunotherapy model in cervical cancer.

Cervical cancer remains a significant health issue in developing countries. However, finding a preclinical model that accurately reproduces tumor characteristics is challenging. Therefore, we established a patient-derived organoids (PDOs) biobank containing 67 cases of heterogeneous cervical cancer that mimic the histopathological and genomic characteristics of parental tumors. The in vitro response of the organoids indicated their ability to capture the radiological heterogeneity of the patients. To model individual responses to adoptive T cell therapy (ACT), we expanded tumor-infiltrating lymphocytes (TILs) ex vivo and co-cultured them with paired organoids. The PDOs-TILs co-culture system demonstrates clear responses that correspond to established immunotherapy efficiency markers like the proportion of CTLs. This study supports the potential of the PDOs platform to guide treatment in prospective interventional trials in cervical cancer.

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods.

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.

HMGB1 induces macrophage pyroptosis in chronic endometritis.

BACKGROUND: Chronic endometritis (CE) reflects the local imbalance in the endometrial immune microenvironment after inflammation. High mobility group box 1 (HMGB1) is highly involved in both immunity and inflammation. In this study, we aimed to explore the roles of HMGB1 in the endometrium of patients with CE. METHODS: Endometrium and uterine fluid HMGB1 were tested in a cohort of infertile patients with or without CE. Expression levels of the pyroptosis marker, gasdermin D (GSDMD)-N-terminal (NT), in the human endometrium of patients with CE and controls were determined. Next, the role of HMGB1 as a driver of macrophage pyroptosis was investigated using human THP-1 cells in vitro and a CE mouse model in vivo. RESULTS: High expression levels of HMGB1 in biopsied endometrial tissue and uterine fluid were confirmed in a cohort of patients with CE. Positive correlation between the number of CD138+ cells and HMGB1 mRNA expression level were detected (rs = 0.592, P < 0.001). Meanwhile, we found that GSDMD-NT expression was significantly increased in the CE endometrium at both the transcriptional and translational levels. Moreover, co-localization of GSDMD-NT and macrophages was confirmed via the double immunostaining of GSDMD-NT and CD68. In vitro experiments revealed that macrophage pyroptosis was induced by HMGB1 in human THP-1-derived macrophages. Treatment with glycyrrhizic acid, an inhibitor of HMGB1, significantly suppressed endometrial pyroptosis and inflammation in the CE mouse model. CONCLUSIONS: HMGB1 effectively induced macrophage pyroptosis in the human endometrium, suggesting that its inhibition may serve as a novel treatment option for CE.

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling.

Chaotic time series are widely present in practice, but due to their characteristics-such as internal randomness, nonlinearity, and long-term unpredictability-it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.

Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain.

Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.

Efficacy and Safety of Moderate-Intensity Continuous Training on the Improvement of Cardiopulmonary Function in Patients After Transcatheter Aortic Valve Replacement (ENERGY): A Randomized Controlled Trial.

OBJECTIVES: To investigate the effect of moderate-intensity continuous training (MICT) on the improvement of cardiopulmonary function for patients undergoing transcatheter aortic valve replacement (TAVR). DESIGN: Randomized controlled study. SETTING AND PARTICIPANTS: Between August 20, 2021, and February 28, 2022, a total of 66 patients after TAVR were screened for inclusion and randomly divided into the MICT and control groups at a ratio of 1:1. MICT was scheduled 3 times per week for 3 months in the intervention group. Patients in the control group received one-time advice on physical activity according to the current guideline. METHODS: The primary endpoint was the 3-month change in peak oxygen consumption (peak VO2) assessed by cardiopulmonary exercise testing. The secondary endpoints included the 3-month change in 6-minute walk test (6MWT), the 12-Item Short Form Health Survey (SF-12), New York Heart Association (NYHA) class, echocardiographic parameters, and laboratory parameters. RESULTS: After 3 months, the change in peak VO2 was higher in the MICT group than that in the control group (1.63 mL/kg/min, 95% CI 0.58-2.67, P = .003). Change in 6MWT (21.55 m, 95% CI 0.38-42.71, P = .046) was higher in the MICT group compared with the control group. A significant change in favor of MICT was also observed for low-density lipoprotein cholesterol (-0.62 mmol/L, 95% CI -1.00 to -0.23, P = .002). However, there were no significant changes in other echocardiographic indices, laboratory parameters, and SF-12 between the 2 groups (all P > .05). CONCLUSIONS AND IMPLICATIONS: MICT had a positive effect on the cardiopulmonary function and physical capacity of patients after TAVR.

Gut dysbiosis-derived ß-glucuronidase promotes the development of endometriosis.

OBJECTIVE: To explore the role of gut dysbiosis-derived ß-glucuronidase (GUSB) in the development of endometriosis (EMs). DESIGN: 16S rRNA sequencing of stool samples from women with (n = 35) or without (n = 30) endometriosis and from a mouse model was conducted to assess gut microbiome changes and identify molecular factors influencing the development of endometriosis. Experiments in vivo in an endometriosis C57BL6 mouse model and in vitro verified the level of GUSB and its role in the development of EMs. SETTING: Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University; Guangdong Provincial Clinical Research Center for Obstetrical and Gynecological Diseases. PATIENT(S): Women of reproductive age with a histological diagnosis of endometriosis were enrolled in the endometriosis group (n = 35) and infertile or healthy age-matched women who had undergone a gynecological or radiological examination in the control group (n = 30). Fecal and blood samples were taken the day before surgery. Paraffin-embedded sections from 50 bowel endometriotic lesions, 50 uterosacral lesions, 50 samples without lesions, and 50 normal endometria were collected. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Changes in the gut microbiome of patients with EMs and mice and the effect of ß-glucuronidase on the proliferation and invasion of endometrial stromal cells and the development of endometriotic lesions were assessed. RESULT(S): No difference in α and ß diversity was found between patients with EMs and controls. Immunohistochemistry analysis showed higher ß-glucuronidase expression in bowel lesions and uterosacral ligament lesions than in the normal endometrium (p<0.01). ß-Glucuronidase promoted the proliferation and migration of endometrial stromal cells during cell counting kit-8, Transwell, and wound-healing assays. Macrophage levels, especially M2, were higher in bowel lesions and uterosacral ligament lesions than in controls, and ß-glucuronidase promoted the M0 to M2 transition. Medium conditioned by ß-glucuronidase-treated macrophages promoted endometrial stromal cell proliferation and migration. ß-Glucuronidase increased the number and volume of endometriotic lesions and number of macrophages present in lesions in the mouse EMs model. CONCLUSION(S): This ß-Glucuronidase promoted EMs development directly or indirectly by causing macrophage dysfunction. The characterization of the pathogenic role of ß-glucuronidase in EMs has potential therapeutic implications.

LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBM.

Hybrid recommendation algorithms perform well in improving the accuracy of recommendation systems. However, in specific applications, they still cannot reach the requirements of the recommendation target due to the gap between the design of the algorithms and data characteristics. In this paper, in order to learn higher-order feature interactions more efficiently and to distinguish the importance of different feature interactions better on the prediction results of recommendation algorithms, we propose a light and FM deep neural network (LFDNN), a hybrid recommendation model including four modules. The LightGBM module applies gradient boosting decision trees for feature processing, which improves LFDNN's ability to handle dense numerical features; the shallow model introduces the FM model for explicitly modeling the finite-order feature crosses, which strengthens the expressive ability of the model; the deep neural network module uses a fully connected feedforward neural network to allow the model to obtain more high-order feature crosses information and mine more data patterns in the features; finally, the Fusion module allows the shallow model and the deep model to obtain a better fusion effect. The results of comparison, parameter influence and ablation experiments on two real advertisement datasets shows that the LFDNN reaches better performance than the representative recommendation models.

Daily exercise improves the long-term prognosis of patients with acute coronary syndrome.

Objective: To demonstrate the effect of daily exercise on the incidence of major adverse cardiovascular events (MACE) for patients with acute coronary syndrome (ACS). Methods: A cohort of 9,636 patients with ACS were consecutively enrolled in our retrospective study between November 2015 and September 2017, which were used for model development. 6,745 patients were assigned as the derivation cohort and 2,891 patients were assigned as the validation cohort. The least absolute shrinkage and selection operator (LASSO) regression and COX regression were used to screen out significant variables for the construction of the nomogram. Multivariable COX regression analysis was employed for the development of a model represented by a nomogram. The nomogram was then evaluated for performance traits such as discrimination, calibration, and clinical efficacy. Results: Among 9,636 patients with ACS (mean [SD] age, 60.3 [10.4] years; 7,235 men [75.1%]), the 5-year incidence for MACE was 0.19 at a median follow-up of 1,747 (1,160-1,825) days. Derived from the LASSO regression and COX regression, the nomogram has included 15 factors in total including age, previous myocardial infarction (MI), previous percutaneous coronary intervention (PCI), systolic pressure, N-terminal Pro-B-type natriuretic peptide (NT-proBNP), high-density lipoprotein cholesterol (HDL), serum creatinine, left ventricular end-diastolic diameter (LVEDD), Killip class, the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score, left anterior descending (LAD) stenosis (≥50%), circumflex (LCX) stenosis (≥50%), right coronary artery (RCA) stenosis (≥50%), exercise intensity, cumulative time. The 5-year area under the ROC curve (AUC) of derivation and validation cohorts were 0.659 (0.643-0.676) and 0.653 (0.629-0.677), respectively. The calibration plots showed the strong concordance performance of the nomogram model in both two cohorts. Moreover, decision curve analysis (DCA) also showed the usefulness of nomogram in clinical practice. Conclusion: The present work provided a prediction nomogram predicting MACE for patients with ACS after incorporating the already known factors and the daily exercise, which demonstrated the effectiveness of daily exercise on the improvement of prognosis for patients with ACS.