Comparative First-Principles Study of the Y2Ti2O7/Matrix Interface in ODS Alloys.

Oxide-dispersion-strengthened (ODS) alloys generally exhibit extraordinary service performance under severe conditions through the formation of ultrafine nano oxides. Y2Ti2O7 has been characterized as the major strengthening oxide in Fe-based ODS alloys. First-principles energetic analyses were performed to investigate the structural, elastic and interface properties of Y2Ti2O7 in either Fe-based or Ni-based ODS alloys. Y2Ti2O7 has comparable elastic constants to bcc-Fe and fcc-Ni and similar elastic deformation compatibility in Y2Ti2O7-strengthened Fe-based and Ni-based ODS alloys is therefore expected. The Ni/oxide interface has generally better thermostability than Fe/oxide across the whole range of the concerned oxygen chemical potential. Further interface bonding and adhesion calculations revealed that Y2Ti2O7 can enhance the bonding strength of Ni/Y2Ti2O7 through d-d orbital interaction between the interfacial YTi layer and Ni layer, while the interface bonding between the Fe layer and YTi layer is weakened compared to the metal matrix. First-principles calculations suggest that Y2Ti2O7 can be a candidate for strengthening nano-oxides in either Fe-based or Ni-based ODS alloys with well-behaved mechanical properties for fourth-generation fission reactors and further experimental validations are encouraged.

The regulatory mechanisms of cerium oxide nanoparticles in oxidative stress and emerging applications in refractory wound care.

Cerium oxide nanoparticles (CeNPs) have emerged as a potent therapeutic agent in the realm of wound healing, attributing their efficacy predominantly to their exceptional antioxidant properties. Mimicking the activity of endogenous antioxidant enzymes, CeNPs alleviate oxidative stress and curtail the generation of inflammatory mediators, thus expediting the wound healing process. Their application spans various disease models, showcasing therapeutic potential in treating inflammatory responses and infections, particularly in oxidative stress-induced chronic wounds such as diabetic ulcers, radiation-induced skin injuries, and psoriasis. Despite the promising advancements in laboratory studies, the clinical translation of CeNPs is challenged by several factors, including biocompatibility, toxicity, effective drug delivery, and the development of multifunctional compounds. Addressing these challenges necessitates advancements in CeNP synthesis and functionalization, novel nano delivery systems, and comprehensive bio effectiveness and safety evaluations. This paper reviews the progress of CeNPs in wound healing, highlighting their mechanisms, applications, challenges, and future perspectives in clinical therapeutics.

A Magneto-Electric Device for Fluid Pipelines with Vibration Damping and Vibration Energy Harvesting.

This study introduces an innovative energy harvesting system designed for industrial applications such as fluid pipelines, air conditioning ducts, sewer systems, and subsea oil pipelines. The system integrates magneto-electric flow coupling and utilizes a dynamic vibration absorber (DVA) to mitigate the vibrations induced by fluid flow while simultaneously harvesting energy through magnetic dipole-dipole interactions in a vibration energy harvester (VEH). The theoretical models, based on Hamilton's Principle and the Biot-Savart Law, were validated through comprehensive experiments. The results indicate the superior performance of the small-magnet system over the large-magnet system in both damping and power generation. The study analyzed the frequency response and energy conversion efficiency across different parameters, including the DVA mass, spring constant, and placement location. The experimental findings demonstrated significant vibration reduction and increased voltage output, validating the theoretical model. This research offers new avenues for energy harvesting systems in pipeline infrastructures, potentially enhancing energy efficiency and structural integrity.

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study.

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization. Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm. Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97-0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model. Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients' clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

Speciation distribution and leaching behavior of heavy metals in coal gasification fine ash: Influence of particle size, carbon content and mineral composition.

In this study, the occurrence and distribution of heavy metals in coal gasification fine ash (CGFA) with different particle sizes were investigated to ensure safer disposal and utilization strategies for CGFA. These measures are critical to sustainable industrial practices. This study investigates the distribution and leachability of heavy metals in CGFA, analyzing how these factors vary with particle size, carbon content, and mineral composition. The results demonstrated that larger CGFA particles (>1 mm) encapsulated up to 70 % more heavy metals than smaller particles (<0.1 mm). Cr and Zn were present in higher concentrations in larger CGFA particles, whereas volatile elements such as Zn, Hg, Se, and Pb were found in relatively higher contents in finer CGFA particles. At least 70 % of Hg in CGFA was present in an acid-soluble form of speciation, whereas Cd, Zn, and Pb were mostly present in a reducible form of speciation, which could be attributed to the presence of franklinite. More than 40 % of Cd and Zn in fine CGFA particles exist in an acid-soluble form. With the exception of CGFA_1.18, Se in CGFA mainly existed in an oxidizable form at a ratio of 60 %-80 %. This could be attributed to the presence of bassanite particles as well as the higher affinity of Se for S. In contrast, Cr, Cu, and As were mostly present in residual speciation forms owing to their parasitism in quartz, sillimanite, and amorphous Fe solid solution in CGFA. Additionally, the study revealed that there was no significant relationship between heavy metal content, leaching behavior, and carbon content in CGFA. Based on combined analyses using toxicity characteristic leaching procedure (TCLP) leaching concentrations and risk assessment code (RAC) results, it is recommended to focus on the environmental risks posed by Cd, Cr, Pb, Zn, and Hg in CGFA during their modification and utilization processes.

Artificial intelligence in ischemic stroke images: current applications and future directions.

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture.

Background: Brain metastases present significant challenges in radiotherapy due to the need for precise tumor delineation. Traditional methods often lack the efficiency and accuracy required for optimal treatment planning. This paper proposes an improved U-Net model that uses a position attention module (PAM) for automated segmentation of gross tumor volumes (GTVs) in computed tomography (CT) simulation images of patients with brain metastases to improve the efficiency and accuracy of radiotherapy planning and segmentation. Methods: We retrospectively collected CT simulation imaging datasets of patients with brain metastases from two centers, which were designated as the training and external validation datasets. The U-Net architecture was enhanced by incorporating a PAM into the transition layer, which improved the automated segmentation capability of the U-Net model. With cross-entropy loss employed as the loss function, the samples from the training dataset underwent training. The model's segmentation performance on the external validation dataset was assessed using metrics including the Dice similarity coefficient (DSC), intersection over union (IoU), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and Hausdorff distance (HD). Results: The proposed automated segmentation model demonstrated promising performance on the external validation dataset, achieving a DSC of 0.753±0.172. In terms of evaluation metrics (including the DSC, IoU, accuracy, sensitivity, MCC, and HD), the model outperformed the standard U-Net, which had a DSC of 0.691±0.142. The proposed model produced segmentation results that were closer to the ground truth and could reveal more detailed features of brain metastases. Conclusions: The PAM-improved U-Net model offers considerable advantages in the automated segmentation of the GTV in CT simulation images for patients with brain metastases. Its superior performance in comparison with the standard U-Net model supports its potential for streamlining and improving the accuracy of radiotherapy. With its ability to produce segmentation results consistent with the ground truth, the proposed model holds promise for clinical adoption and provides a reference for radiation oncologists to make more informed GTV segmentation decisions.

Cerium oxide nanoparticles in wound care: a review of mechanisms and therapeutic applications.

Skin wound healing is a complex and tightly regulated process. The frequent occurrence and reoccurrence of acute and chronic wounds cause significant skin damage to patients and impose socioeconomic burdens. Therefore, there is an urgent requirement to promote interdisciplinary development in the fields of material science and medicine to investigate novel mechanisms for wound healing. Cerium oxide nanoparticles (CeO2 NPs) are a type of nanomaterials that possess distinct properties and have broad application prospects. They are recognized for their capabilities in enhancing wound closure, minimizing scarring, mitigating inflammation, and exerting antibacterial effects, which has led to their prominence in wound care research. In this paper, the distinctive physicochemical properties of CeO2 NPs and their most recent synthesis approaches are discussed. It further investigates the therapeutic mechanisms of CeO2 NPs in the process of wound healing. Following that, this review critically examines previous studies focusing on the effects of CeO2 NPs on wound healing. Finally, it suggests the potential application of cerium oxide as an innovative nanomaterial in diverse fields and discusses its prospects for future advancements.

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN.

BACKGROUND: In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in the availability of tools tailored for the automatic segmentation of the GTV based on CT simulation localization images. PURPOSE: This study aims to fill this gap by devising an effective tool specifically for the automatic segmentation of the GTV using CT simulation localization images. METHODS: A dual-network generative adversarial network (GAN) architecture was developed, wherein the generator focused on refining CT images for more precise delineation, and the discriminator differentiated between real and augmented images. This architecture was coupled with the Mask R-CNN model to achieve meticulous GTV segmentation. An end-to-end training process facilitated the integration between the GAN and Mask R-CNN functionalities. Furthermore, a conditional random field (CRF) was incorporated to refine the initial masks generated by the Mask R-CNN model to ensure optimal segmentation accuracy. The performance was assessed using key metrics, namely, the Dice coefficient (DSC), intersection over union (IoU), accuracy, specificity, and sensitivity. RESULTS: The GAN+Mask R-CNN+CRF integration method in this study performs well in GTV segmentation. In particular, the model has an overall average DSC of 0.819 ± 0.102 and an IoU of 0.712 ± 0.111 in the internal validation. The overall average DSC in the external validation data is 0.726 ± 0.128 and the IoU is 0.640 ± 0.136. It demonstrates favorable generalization ability. CONCLUSION: The integration of the GAN, Mask R-CNN, and CRF optimization provides a pioneering tool for the sophisticated segmentation of the GTV in brain metastases using CT simulation localization images. The method proposed in this study can provide a robust automatic segmentation approach for brain metastases in the absence of MRI.

Identifying SARS-CoV-2 Variants Using Single-Molecule Conductance Measurements.

The global COVID-19 pandemic has highlighted the need for rapid, reliable, and efficient detection of biological agents and the necessity of tracking changes in genetic material as new SARS-CoV-2 variants emerge. Here, we demonstrate that RNA-based, single-molecule conductance experiments can be used to identify specific variants of SARS-CoV-2. To this end, we (i) select target sequences of interest for specific variants, (ii) utilize single-molecule break junction measurements to obtain conductance histograms for each sequence and its potential mutations, and (iii) employ the XGBoost machine learning classifier to rapidly identify the presence of target molecules in solution with a limited number of conductance traces. This approach allows high-specificity and high-sensitivity detection of RNA target sequences less than 20 base pairs in length by utilizing a complementary DNA probe capable of binding to the specific target. We use this approach to directly detect SARS-CoV-2 variants of concerns B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron) and further demonstrate that the specific sequence conductance is sensitive to nucleotide mismatches, thus broadening the identification capabilities of the system. Thus, our experimental methodology detects specific SARS-CoV-2 variants, as well as recognizes the emergence of new variants as they arise.

Effects of Different Calcium Sources on Mechanical Properties of Metakaolin Geopolymers.

Metakaolin-based geopolymers have substantial potential as replacements for cement, but their relatively inferior mechanical properties restrict their application. This paper aims to enhance the mechanical properties of metakaolin-based geopolymers by incorporating appropriate amounts of calcium sources. CaCO3, Ca(OH)2, and CaSO4 are three types of calcium sources commonly found in nature and are widely present in various industrial wastes. Thus, the effects of these three calcium sources on the performance of metakaolin-based geopolymers were studied. Through the analysis of the mechanical properties, heat-release behavior during hydration, hydration products, and microstructure of geopolymers, the effectiveness of the aforementioned calcium sources in improving the performance of metakaolin-based geopolymer was evaluated, and the mechanisms of action were elucidated. The results indicate that the pozzolanic reaction between CH and MK could promote MK hydration and increase the proportion of CASH gel in the hydration products, thereby facilitating the setting of the geopolymer and enhancing its strength. CS could react with the active aluminates in MK to form ettringite, thus forming a higher early strength. CC had a lower reactivity with MK and does not improve the performance of MK-based geopolymers.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

Evolution mechanism of scratch removal based on the implementation of magnetorheological finishing.

Scratches on optical components induce laser damage and limit the increase in laser power. Magnetorheological finishing (MRF) is a highly deterministic optical manufacturing technology that can improve the surface roughness of optical components. Although MRF has exhibited significant potential for reducing subsurface damage and removing scratches, the principle and mechanism behind the scratch removal are not sufficiently understood. In this study, the theory of fluid mechanics is used to analyze the pressure, velocity, and particle trajectory distribution near a scratch. A physical model was developed for the differential removal of scratches at the bottom and surface of the optical components. The morphological evolution of the scratch was predicted during removal, and detailed experiments were performed to verify the effectiveness of the proposed model. The results indicate that scratches expand laterally rather than being completely removed. Furthermore, scratch removal efficiency is greater when the removal direction is perpendicular to the scratch rather than being parallel. This study offers an intrinsic perspective for a comprehensive understanding of the MRF technique used for scratch removal, which can be beneficial for removing scratches from aspherical optical systems.

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-ß) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life.

Sustainable building materials-recycled aggregate and concrete: a systematic review of properties, modification techniques, and environmental impacts.

Resource utilization of construction and demolition (C&D) waste has great potential to significantly reduce the consumption of natural resources and improve the environment. Meanwhile, establishing a sound policy system and reducing production are the key ways to solve the problem of C&D waste. Numerous studies on C&D waste, recycled concrete aggregate (RA), and recycled aggregate concrete (RAC) have been reported in the literature, with few systematic summaries. From a global perspective, this paper assessed the current situation of C&D waste and the countermeasure of several major economies. Then, this paper systematically introduces the composition structure and characteristics of RA. Modification techniques from macro and micro perspectives of RA and its effect on RAC were also presented. Paper also reviews the environmental impacts of RA and RAC. The results showed that bonded mortar was the most significant defect of RA than natural aggregate (NA). Thus, RA weakened RAC's microstructure, workability, mechanical properties, and durability. The research on the modification of RA mainly focused on removing bonded mortar and enhancing bonded mortar containing physical or chemical methods. Enhancing bonded mortar was a more effective method than removing bonded mortar. Carbonation and microbially induced calcium carbonate precipitation were highly efficient and environmentally friendly for RA modification. Research progress in quantifying the environmental impacts associated with concrete from waste materials through the LCA methodology is presented. Suggestions and an outlook were given on the critical issues facing RA and RAC. We expect that this work can provide more technical support for C&D waste utilization.

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

Objective: The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods: This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results: 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion: This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma.

BACKGROUND & AIMS: The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. METHODS: We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. RESULTS: Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively). CONCLUSION: CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Initial Upper Palaeolithic material culture by 45,000 years ago at Shiyu in northern China.

The geographic expansion of Homo sapiens populations into southeastern Europe occurred by ∼47,000 years ago (∼47 ka), marked by Initial Upper Palaeolithic (IUP) technology. H. sapiens was present in western Siberia by ∼45 ka, and IUP industries indicate early entries by ∼50 ka in the Russian Altai and 46-45 ka in northern Mongolia. H. sapiens was in northeastern Asia by ∼40 ka, with a single IUP site in China dating to 43-41 ka. Here we describe an IUP assemblage from Shiyu in northern China, dating to ∼45 ka. Shiyu contains a stone tool assemblage produced by Levallois and Volumetric Blade Reduction methods, the long-distance transfer of obsidian from sources in China and the Russian Far East (800-1,000 km away), increased hunting skills denoted by the selective culling of adult equids and the recovery of tanged and hafted projectile points with evidence of impact fractures, and the presence of a worked bone tool and a shaped graphite disc. Shiyu exhibits a set of advanced cultural behaviours, and together with the recovery of a now-lost human cranial bone, the record supports an expansion of H. sapiens into eastern Asia by about 45 ka.

Ultrafine Iridium Nanoparticles Anchored on Co-Based Metal-Organic Framework Nanosheets for Robust Hydrogen Evolution in Alkaline Media.

A highly promising electrocatalyst has been designed and prepared for the hydrogen evolution reaction (HER). This involves incorporating well-dispersed Ir nanoparticles into a cobalt-based metal-organic framework known as Co-BPDC [Co(bpdc)(H2O)2, BPDC: 4,4'-biphenyldicarboxylic acid]. Ir@Co-BPDC demonstrates exceptional HER activity in alkaline media, surpassing both commercial Pt/C and recent noble-metal catalysts. Theoretical results indicate that electron redistribution, induced by interfacial bonds, optimizes the adsorption energy of water and hydrogen, thereby enhancing our understanding of the superior properties of Ir@Co-BPDC for HER.

Classification of regulatory T cells and their role in myocardial ischemia-reperfusion injury.

Myocardial ischemia-reperfusion injury (MIRI) is closely related to the final infarct size in acute myocardial infarction (AMI). Therefore, reducing MIRI can effectively improve the prognosis of AMI patients. At the same time, the healing process after AMI is closely related to the local inflammatory microenvironment. Regulatory T cells (Tregs) can regulate various physiological and pathological immune inflammatory responses and play an important role in regulating the immune inflammatory response after AMI. However, different subtypes of Tregs have different effects on MIRI, and the same subtype of Tregs may also have different effects at different stages of MIRI. This article systematically reviews the classification and function of Tregs, as well as the role of various subtypes of Tregs in MIRI. A comprehensive understanding of the role of each subtype of Tregs can help design effective methods to control immune reactions, reduce MIRI, and provide new potential therapeutic options for AMI.