Developing renewable energy power is a key measure for developing countries to promote energy transition and achieve large-scale carbon reduction and emissions reduction. However, the effectiveness of renewable energy power in achieving emissions reduction, especially the mechanism of its role in emissions reduction and the costs and benefits during the emissions reduction process, still needs to be clarified. Given this, based on the actual development scenario of renewable energy power in China, this paper manually collected geographical distribution data for 12,139 power companies in China from 2003 to 2021 and matched it with multiple grid data sets, including "economy-carbon emissions-natural geography". Using the unique data, this paper analyzes the impact of renewable energy power generation on regional carbon emissions and the cost-benefit of renewable energy power production.The study found that renewable energy power reduces carbon emissions through two channels: the "substitution effect" on fossil fuel power and the "Permeation effect" on the industrial and household sectors, with hydropower and wind power having the most significant emission reduction effects. Additionally, the development of renewable energy power not only promotes emission reductions in the region but also helps reduce carbon emissions in neighboring regions, demonstrating a significant spatial spillover effect. Cost-benefit estimates indicate that between 2003 and 2021, China's renewable energy power generation could achieve annual emissions reductions of nearly 20.382 million tons compared to fossil fuel power generation. This is equivalent to a reduction of approximately 6.75 % of carbon emissions from the power sector during the same period, with an estimated annual reduction value of approximately US$1027.97 million. Welfare analysis shows that with the increase of the proportion of renewable energy power, CS and SW increased rapidly from 2003 to 2021. The findings of this study provide strong empirical support for developing countries to promote large-scale carbon emissions reductions and socio-economic sustainable development through the development of renewable energy.

Quantization has emerged as a mainstream approach for deploying Large Language Models (LLMs) on resource-constrained devices, yet compressing precision below 4-bit typically causes severe performance degradation or prohibitive retraining costs. In this paper, we propose EdgeRazor, a lightweight framework for LLMs via Mixed-Precision Quantization-Aware Distillation. It contains three modules: Structural Quantization with Mixed Precision for fine-grained control of bit-widths, Layer-Adaptive Feature Distillation that dynamically selects the most informative features for alignment, and Entropy-Aware KL Divergence for forward-reverse balance on both human-annotated and distilled datasets. Evaluations conducted on MobileLLM and Qwen families show that under weight-activation quantization, the 1.88-bit Qwen3-0.6B-EdgeRazor outperforms the state-of-the-art 2-bit baselines by 11.27 and surpasses the strongest 3-bit baselines by 4.38, while the quantized MobileLLM-350M-EdgeRazor requires a training budget 4-10$\times$ lower than the leading quantization-aware training method. In terms of efficiency, EdgeRazor achieves higher compression ratios at all bit-widths, and the 1.58-bit Qwen3-0.6B-EdgeRazor reduces storage from 1.11 GB to 0.19 GB while accelerating decoding by 15.16$\times$ over the 16-bit baseline. These results empirically validate the effectiveness and efficiency of EdgeRazor. The codes can be accessed from \href{https://github.com/zhangsq-nju/EdgeRazor}{GitHub} and \href{https://huggingface.co/collections/zhangsq-nju/edgerazor-nbit}{Huggingface}.

The electric power industry occupies an important position in the economy development, however, a series of environmental problems have arisen during this time, so the research on the relationship between the reform of the electricity industry and carbon emissions is of great significance in realizing carbon reduction. Based on the quasi-natural experiment of the power plant grid separation policy implemented in 2003, this paper evaluates the impact of the policy on CO2 emissions by using carbon emission data and economic variable data at the county level, and further analyzes the impact mechanism. The results show that the implementation of the policy significantly increases regional CO2 emissions and per capita CO2 emissions, and the implementation of the policy increases regional CO2 emissions and per capita CO2 emissions by 0.5919 and 0.587 respectively, and this result is more significant in regions with larger power generation, dominated by thermal power generation, and coal dependent. The mechanism test indicate that the policy increases carbon emissions mainly by increasing the number of non-electricity enterprises and electricity consumption, and further analysis indicates that the implementation of the policy increases cross-provincial electricity transmission and creates inequities in environmental and economic growth between regions. The findings of this study may be useful for developing countries, such as China, to formulate reasonable electricity market reform policies and realize the healthy development of the electricity industry and environmental protection.

Background: Membranous nephropathy (MN), a prevalent glomerular disorder, remains poorly understood in terms of its association with mitochondrial dynamics (MD). This study investigated the mechanistic involvement of mitochondrial dynamics-related genes (MDGs) in the pathogenesis of MN. Methods: Comprehensive bioinformatics analyses-encompassing Mendelian randomization, machine-learning algorithms, and single-cell RNA sequencing (scRNA-seq)-were employed to interrogate transcriptomic datasets (GSE200828, GSE73953, and GSE241302). Core MDGs were further validated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Results: Four key MDGs-RTTN, MYO9A, USP40, and NFKBIZ-emerged as critical determinants, predominantly enriched in olfactory transduction pathways. A nomogram model exhibited exceptional diagnostic performance (area under the curve [AUC] = 1). Seventeen immune cell subsets, including regulatory T cells and activated dendritic cells, demonstrated significant differential infiltration in MN. Regulatory network analyses revealed ATF2 co-regulation mediated by RTTN and MYO9A, along with RTTN-driven modulation of ELOA-AS1 via hsa-mir-431-5p. scRNA-seq analysis identified mesenchymal-epithelial transitioning cells as key contributors, with pseudotime trajectory mapping indicating distinct temporal expression profiles: NFKBIZ (initial upregulation followed by decline), USP40 (gradual fluctuation), and RTTN (persistently low expression). RT-qPCR results corroborated a significant downregulation of all four genes in MN samples compared to controls (p < 0.05). Conclusions: These findings elucidate the molecular underpinnings of MDG-mediated mechanisms in MN, revealing novel diagnostic biomarkers and therapeutic targets. The data underscore the interplay between mitochondrial dynamics and immune dysregulation in MN progression, providing a foundation for precision medicine strategies.

Diabetic nephropathy (DN) is a common and serious complication of diabetes, characterized by chronic fibro-inflammatory processes with an unclear pathogenesis. Renal fibrosis plays a significant role in the development and progression of DN. While recent research suggests that the neddylation pathway may influence fibrotic processes, its specific dysregulation in DN and the underlying mechanisms remain largely unexplored. This study identified the neddylation of RhoA as a novel post-translational modification that regulates its expression and promotes renal fibrosis in DN. We here demonstrated that two key components of the neddylation pathway—NEDD8-activating enzyme E1 subunit 1 (NAE1) and NEDD8—are significantly upregulated in human chronic kidney disease (CKD) specimens compared to healthy kidneys, implicating neddylation in CKD-associated fibrosis. Our findings further revealed that both pharmacological inhibition of neddylation using MLN4924 and genetic knockdown of NAE1 mitigate renal fibrosis in mouse models of streptozotocin-induced diabetes and unilateral ureteral obstruction (UUO). Immunoprecipitation-mass spectrometry (IP-MS) and subsequent function assays demonstrated a direct interaction between RhoA and NEDD8. Importantly, neddylation inhibition reduced RhoA protein expression, highlighting a potential therapeutic target. Additionally, a positive correlation was noted between elevated NEDD8 mRNA levels and RhoA mRNA expression in human CKD specimens. RhoA overexpression counteracted the antifibrotic effects of neddylation inhibition, underscoring its critical role in fibrosis progression. Mechanistically, we unveiled that neddylation enhances RhoA protein stability by inhibiting its ubiquitination-mediated degradation, which subsequently activates the ERK1/2 pathway. Collectively, this study provides novel insights into NAE1-dependent RhoA neddylation as a key contributor to renal fibrosis in DN.

Punishment and network reciprocity have profound implications for the evolution of cooperation. However, existing research on the consequences of cooperation under punishment in social networks has largely relied on agent-based models and laboratory experiments. Moreover, different from the majority of existing studies where punishment is always believed to be deterministic, the individuals' preferences for certain behaviors are always stochastic and vary with the environment. There is an urgent need to explore how cooperation evolves when punishment is stochastic and endogenous in social networks. In this paper, we propose a theoretical model of endogenous punishment in spatial public goods games. Cooperators each can stochastically choose whether to participate in the punishment for defectors. The choice to penalize defectors comes with a price. Whether and how defectors are punished is endogenously determined by the cooperators' preferences for executing the costly punishment. We analyze how cooperation evolves under endogenous punishment based on a regular network in the mean-field limit and outline the conditions under which endogenous punishment can support cooperation. When network reciprocity is unfavorable for cooperation, endogenous punishment can be effective in supporting cooperation. On the contrary, endogenous punishment no longer supports or even hinders the promoting effect of network reciprocity on cooperation. These findings illustrate that the effectiveness of endogenous punishment in fostering cooperation is dependent on the cooperators' willingness to pay for punishment as well as the topology of social networks.

Cardiovascular diseases with their related secondary complications are the main causes of morbidity and mortality worldwide. Abdominal aortic aneurysm (AAA) belongs to the cardiovascular diseases and causes approximately 1.3 % of all deaths among men between 65 and 85 years old in developed countries [1]. The pathogenesis of AAA mainly attributes to pathological dilation of the abdominal aorta, which will further lead to a high mortality rate up to 85 % due to excessive dilation and rupture [2]. A criterion was proposed in 1991 that AAA infrarenal aorta diameter should be 1.5 times the normal diameter [3], and McGregor additionally defined AAA as an aorta with a diameter greater than 30 mm in the infrarenal segment [4]. Although the diagnosis of AAA seems conclusive, there is no specific treatment to prevent AAA expansion. Elective aortic repair operation is conditional recommended when the aneurysm diameter reaches 55 mm in men, 50 mm in women or grows by 6 mm to 8 mm per year [5]. However, small aneurysms probably also grow rapidly or rupture at a high risk, and even some patients die from aneurysm rupture before they manifest surgical indications. Thus, controlling risk factors and exploring novel therapeutic approaches gradually substitute as key directions for aneurysm treatment. Smoking, hypertension, age and gender have been identified as the common risk factors during AAA progression in the past decades [6], but the mechanisms how these hazards contribute to pathological dilatation of abdominal aortas remain unclear. Interestingly, histone modifications have recently emerged as an important link between the intrinsic genetic landscape and extrinsic risk factors, and a plethora of studies have been dedicated to exploring the role of histone modifications in AAA pathogenesis. In this review, current progress on the contribution of histone modifications to the regulation of AAA will be summarized.

This study proposed an accident data-driven approach using hybrid AI techniques for the quantification of falling risks at workplaces. Six machine learning models and one ensemble learning model were deployed for automatic extraction of causal factors. These causal factors were taken as main nodes in the falling risk Bayesian network (FRBN). Data-driven and knowledge-driven methods were combined for structure learning of the FRBN, based upon algorithms of hill climbing and tree augmented naive Bayes firstly and modification of FRBN through incorporation of knowledge. Sensitive causal factors were determined using parameter-based and evidence-based sensitivity analysis approaches. The FRBN was further adopted for forward and backward causal inferences. The accident data-driven approach through hybrid AI techniques contributes to substantial learning from fall-related accidents. Measures would be tailored according to causal inferences within the FRBN, so that the probability of falling risk will be reduced and negative impacts of fall-from-height (FFH) accidents will be decreased.

Both clonal hematopoiesis of indeterminate potential (CHIP) and type 2 diabetes mellitus (T2DM) are conditions closely associated with advancing age. This study delves into the possible implications and prognostic significance of CHIP and T2DM in patients diagnosed with ST-segment elevation myocardial infarction (STEMI). Deep-targeted sequencing employing a unique molecular identifier (UMI) for the analysis of 42 CHIP mutations—achieving an impressive mean depth of coverage at 1000 × —was conducted on a cohort of 1430 patients diagnosed with acute myocardial infarction (473 patients with T2DM and 930 non-DM subjects). Variant allele fraction ≥ 2.0 Central Illustration: The association between clonal hematopoiesis of indeterminate potential (CHIP) and type 2 diabetes mellitus (T2DM): The prevalence of CHIP is notably higher in individuals with T2DM, as demonstrated in a prospective study within an Asian cohort of acute myocardial infarction (AMI). Furthermore, the predictive value of CHIP as a marker for poor clinical prognosis in T2DM has been assessed in this study. Mendelian randomization studies suggest that the development of T2DM may increase the propensity for CHIP, as indicated by findings from the UK Biobank and FinnGen consortium. T2DM, type 2 diabetes mellitus; CHIP, clonal haematopoiesis of indeterminate potential.

The carbon emissions trading (CET) policy is a crucial market-based environmental regulatory policy for managing corporate carbon emissions, thereby assisting China in achieving its carbon peak and carbon neutrality goals. This study examines whether such a policy can boost corporate environmental performance. Based on China’s CET pilot as a quasi-natural experiment, this paper employs the difference-in-differences method and difference-in-difference-in-differences method to analyze the data of listed companies in the pilot regions from 2010 to 2020. Findings show that the policy of CET has a significant positive influence on firms’ environmental performance. Notably, while high-pollution companies benefit substantially, the effect is relatively weaker compared to others. Mechanism analysis shows that the policy drives improvements through enhanced environmental management and green innovation, and government environmental subsidies promote the effect of CET on environmental performance. In addition, the impact is more pronounced in state-owned, large-scale, and power industry companies; companies in regions with strong environmental regulations; and with high executive green awareness. These findings provide some insights for refining China’s CET framework and enhancing environmental outcomes.

Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, we propose the TernaryCLIP, a lightweight computational framework that converts connection weights of both vision and text encoders of CLIP into the ternary format, instead of full-precision or floating ones. TernaryCLIP incorporates quantization-aware training and distillation modules, preventing precision degradation and enabling low-cost and high-efficiency computations. Comprehensive experiments demonstrate that TernaryCLIP can achieve up to 99% ternarized weights with 1.58-bit representation, 16.98 × compression ratio, 2.3 × inference acceleration, 16 × storage reduction, 10 × memory optimization, and 60% sparsity while maintaining promising performance on zero-shot image classification and image-text retrieval tasks across 41 commonly used datasets. Our work highlights the feasibility of extreme quantization for large multimodal models, supporting effective and efficient deployment on resource-constrained devices. The model and code can be accessed from Hugging Face and GitHub.

Abdominal aortic aneurysm (AAA) is a major vascular pathology with high morbidity and mortality. Phenotypic changes of vascular smooth muscle cells (VSMC) play a key role in AAA pathogenesis. In the present study, we investigated the effect of VSMC-specific ablation of BRG1, a chromatin remodeling protein, on AAA pathogenesis focusing on transcriptional mechanism and translational potential. BRG1 expression was elevated in VSMCs by pro-AAA stimuli in vitro and in vivo. Compared to the BRG1f/f mice, the VSMC-specific BRG1 knockout mice (BRG1ΔSMC) displayed a less severe AAA phenotype induced by chronic angiotensin II (Ang II) infusion, by treatment with calcium phosphate, or by treatment with porcine pancreatic elastase as evidenced by smaller vascular dilation, decreased rupture of elastic fiber, decreased VSMC apoptosis, and reduced MMP activities. BRG1 deletion significantly attenuated the induction of apoptosis and MMP activities by Ang II treatment in cultured VSMCs. RNA-seq uncovered Cathepsin K (Ctsk) as a novel transcriptional target for BRG1 in VSMCs. Ang II stimulated Ctsk expression in VSMCs whereas Brg1 deletion repressed Ctsk induction. BRG1 was detected to directly bind to the Ctsk promoter to activate transcription. Consistently, Ctsk knockdown in VSMCs dampened apoptosis and inhibited MMP activities. Finally, inhibition of BRG1 activity with a small-molecule compound (PFI-3) ameliorated phenotypic modulation in VSMCs and mitigated AAA in mice. Our data suggest that BRG1 may contribute to AAA pathogenesis by activating Ctsk transcription in VSMCs. Therefore, targeting BRG1 can be considered as a reasonable approach for AAA intervention.

With the increasing global concern about climate change and environmental protection, reducing carbon emissions has become an important issue of sustainable development. Digital inclusive finance (DIF), as a powerful instrument to promote economic reform and carbon emission reduction, provides substantial support for achieving the goal of carbon neutrality. Against the backdrop of China's goals of carbon peaking and carbon neutrality (referred to as "dual carbon"), the panel data of nine provinces and regions in the Yellow River basin (YRB) from 2011-2020 were analyzed in this paper to reveal the role of DIF in carbon emissions and the underlying reasons behind it. A Two-way Fixed Effect (TWFE) model was used, and relevant heterogeneity analysis and robustness tests were carried out. The results show that DIF significantly promotes carbon emission reduction. After several rounds of robustness tests, this conclusion is still valid, which fully proves the result's reliability. The analysis of DIF sub-dimension indices shows that coverage breadth, usage depth, and digitization degree have different degrees of impact on curbing carbon emissions. In addition, the differences in geographical location and economic development level among different regions significantly affect the carbon reduction effects of DIF. In middle-lower reaches of YRB and regions with higher economic development level, the carbon reduction effects of DIF are particularly significant. Mechanism analysis revealed different pathways of DIF supporting carbon reduction. DIF significantly reduces carbon emissions by promoting technical innovation level and optimizing energy structure. The existence of Environmental Kuznets Curve was also confirmed. Furthermore, government intervention degree and environmental regulation intensity have varying degrees of inhibitory effects on carbon emissions, while industrial structure level has a promotional effect on carbon emissions. This study has important reference value for comprehensively assessing the environmental impact of DIF and formulating relevant policies. It also provides practical policy insights for designing and implementing strategies to promote DIF development and drive corporate carbon reduction.

Glycolysis is recognized as a central metabolic pathway in the neoplastic evolution of gastric cancer, exerting profound effects on the tumor microenvironment and the neoplastic growth trajectory. However, the identification of key glycolytic genes that significantly affect gastric cancer prognosis remains underexplored. In this work, five machine-learning algorithms were used to elucidate the intimate association between the glycolysis-associated gene phosphofructokinase fructose-bisphosphate 3 (PFKFB3) and the prognosis of gastric cancer patients. Validation across multiple independent datasets confirmed the prognostic significance of PFKFB3. Further, we delved into the functional implications of PFKFB3 in modulating immune responses and biological processes within gastric cancer patients, as well as its broader relevance across multiple cancer types. Results underscore the potential of PFKFB3 as a prognostic biomarker and therapeutic target in gastric cancer. Our project can be found at https://github.com/PiPiNam/ML-GCP .

Aims Aberrant cardiac fibrosis, defined as excessive production and deposition of extracellular matrix (ECM), is mediated by myofibroblasts. ECM-producing myofibroblasts are primarily derived from resident fibroblasts during cardiac fibrosis. The mechanism underlying fibroblast-myofibroblast transition is not fully understood. Methods Cardiac fibrosis was induced by transverse aortic constriction (TAC) or by angiotensin II (Ang II) infusion in C57B6/j mice. Cellular transcriptome was evaluated by RNA-seq and CUT&Tag-seq. Results Integrated transcriptomic screening revealed that a disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) was a novel transcriptional target for Kruppel-like factor 6 (KLF6) in cardiac fibroblasts. Treatment with either TGF-β or Ang II up-regulated ADAMTS1 expression. KLF6 knockdown attenuated whereas KLF6 over-expression enhanced ADAMTS1 induction. ChIP assay and reporter assay showed that KLF6 was recruited to the ADAMTS1 promoter to activate its transcription. Consistently, ADAMTS1 knockdown suppressed fibroblast-myofibroblast transition in vitro. Importantly, myofibroblast-specific ADAMTS1 depletion attenuated cardiac fibrosis and normalized heart function in mice. Significance In conclusion, our data demonstrate that ADAMTS1, as a downstream target of KLF6, contributes to cardiac fibrosis by regulating fibroblast-myofibroblast transition.

This study focuses on gas injection mass fluctuation in high-pressure hydrogen coupling direct injection technology for jet ignition, conducting research to optimize injection process stability. Investigating the fluctuation mechanism reveals that diesel pressure fluctuation induced by pilot injection is the fundamental cause for circulating gas injection mass fluctuation. It is found that the free diesel pressure oscillation within the fuel system and gas injection mass fluctuation at varying injection interval time both exhibit an under-damped oscillation pattern. In view of this, the research addresses stability optimization for gas injection mass from two aspects. On the one hand, the mechanism of the Helmholtz filter to reduce pressure fluctuation is explored in system structural aspect, and designed the structural parameters based on its resonant angular frequency. Then, the influence from different volumes on pressure fluctuation within the low-impedance region is analyzed. Through experimental verification, it is known that this method can reduce gas injection mass fluctuation, but it will decrease the total gas injection mass. On the other hand, an energizing time correction algorithm based on predictive model is proposed. By fitting and optimizing the each parameter sub-algorithms, the final corrected injection pulse width is obtained by combining the each parameter sub-algorithms. The results show that the Helmholtz filter can achieve a maximum reduction in gas injection mass fluctuation reaching 69.82 %. But compared with the original system, the total gas injection mass is reduced by about 7.4 %. The correction model can reduce the gas injection mass fluctuation reach up to 83.92 % and do not affect the total gas injection mass. The results can provide a theoretical basis for the advanced engine fuel injection process closed-loop control, and the experimental data can provide a reference for a dual-fuel injection system.

To enhance the performance of learning from past fall-related accidents, this study developed an innovative framework for automatically extracting every individual causal factor from accident investigation reports based upon the modified framework of the human factors analysis and classification system. Multiple techniques including the synthetic minority oversampling technique (SMOTE) algorithm for handling imbalanced data, soft voting with unequal weights for ensemble learning, and hyperparameter optimization were adopted to improve automatic identification of causal factors from unstructured text data. Experimental results denoted there were no classifiers with the best accuracy and F1 score unanimously for any of the 19 subcategories of causal factors. Therefore, one or more specific classifiers were preferred for predicting one specific causal factor with the best performance. Further comparative analyses between seven classifiers demonstrated that the ensemble learning model by the algorithm of soft voting (ELSV) could provide more stable predictions with low variance across different causal factors compared with individual machine learning models. It was suggested that the ELSV ought to be prioritized for collectively identifying all 19 causal factors. These findings are beneficial for substantial learning from past fall-related accidents with high efficiency and reliability, and valuable insights can be discerned and utilized for controlling the risk of fall-from-height at construction sites. This study aims to propose an innovative framework based on multiple machine learning models (i.e., support vector machine, naive Bayes, decision tree, k-nearest neighbors, random forest, and multilayer perceptron) and one ensemble learning approach. Several techniques (i.e., SMOTE for handling imbalanced data, soft voting with unequal weights for ensemble learning, and hyperparameter optimization) were used for improving automatic identification of causal factors. It was found that there were no best classifiers unanimously for all 19 subcategories of causal factors. Comparative analysis results between seven classifiers demonstrated that the ensemble learning approach was able to provide more stable predictions with low variance across various causal factors compared with individual machine learning models. This innovative framework provides a feasible method of automatic identification of causal factors from fall-from-height postaccident investigation reports at construction workplaces. It decreases the time and subjectivity through a manual process, enhancing the efficiency and reliability in extracting causal factors. It also satisfies the requirement that an investigation process should be implemented as fast as possible after an accident. Safety managers on site will adopt corrective and preventive measures to deal with causal factors immediately, in order to effectively reduce falling risks in the construction industry.

Accurately identifying the historical causes of carbon emissions in the process of national economic development is an important basis for developing countries to achieve carbon emission reduction. This paper explores the intrinsic institutional causes of China's high CO2 emission growth based on the characteristic economic growth target system of China, and attempts to empirically test the environmental effects behind this system. The results of the study show that the setting of absolute economic growth targets significantly increases the carbon dioxide emissions of cities under horizontal competition, and the setting of relative economic growth targets exacerbates the above carbon emission effect under vertical competition. In addition, the heterogeneity analysis shows that the carbon emission effect of setting economic growth targets is stronger in resource-dependent cities and cities with a lower level of economic development. Mechanism tests show that economic growth targets not only significantly increases total fossil energy consumption and reduces energy efficiency at the firm level, but also leads to the increase of energy consumption and the reduction of energy efficiency at the industry level. The findings of this study provide an intrinsic institutional explanation for China's high carbon emissions and provide useful guidance for the design of mechanisms to achieve large-scale carbon emission reductions in developing countries.

Background Abnormal glucose and lipid metabolism are very commonplace in individuals with myocardial infarction, while insulin resistance plays a vital role in this biological process. Nevertheless, cardiovascular risk estimation by novel lipid biomarkers based on classic lipid parameters needs assessment in myocardial infarction cohorts with diabetes mellitus (DM) according to triglyceride–glucose index (TyG) level, followed up for incident ischemic stroke events, to estimate any modification in risk estimation warrants a change in treatment. We report results from prospective cohort in such a continuing study. Methods Three novel lipid biomarkers (including proprotein convertase subtilisin/kexin type 9, Fatty acid-binding protein 4 and Resolvin D1) from different pathophysiological pathways with six traditional lipid parameters were evaluated in 1580 DM and non-DM of the myocardial infarction population cohort with 449 incident cardiovascular events (fatal or nonfatal coronary or ischemic stroke events) at median 2.02 years with follow up. Resluts 1）In the group with lower TyG levels, the risk of MACEs decreased significantly during first group (0-1 lipid parameters increased) in the DM population (HR, 0.31; 95% CI, 0.12–0.81; P=0.017) but not in patients with higher TyG levels. Similarly, among male patients, increasing number of lipid parameters index levels were associated with a stepwise higher incidence of MACEs over time (group 2, HR, 0.27; 95% CI, 0.12–0.59; P =0.001; group 3, HR, 0.42; 95% CI, 0.19–0.90; P =0.0026) in the fully adjusted Cox regression models. 2) Furthermore, among DM patients, group 1 (0-1 lipid parameters increased) and group 2 (2-4 lipid parameters increased) had significantly better ischemic stroke-free survival than other groups (p=0.025) when TyG index ≥median. 3) Adding novel lipid associated parameters and TyG index to the conventional lipid risk factors model in the cohort validated it by improved net reclassification index (p＜0.05) and integrated discrimination improvement and led to significant reclassification of individuals into risk categories. Conclusion The addition of a biomarker score including novel lipid associated biomarkers and TyG index to a conventional risk model improved risk estimation for ischemic stroke events in myocardial infarction populations with DM. Further validation is needed in other populations and age group.