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BackgroundSpontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.MethodsA retrospective analysis was conducted on clinical data from 380 patients with SICH who were hospitalized at three different centers between June 2020 and June 2023. Seventy percent of the samples were randomly selected as the training set, while the remaining 30% were used as the validation set. Univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Pearson correlation analysis were used to screen clinical variables. The selected variables were then incorporated into five machine learning models: complementary naive bayes (CNB), support vector machine (SVM), gaussian naive bayes (GNB), multilayer perceptron (MLP), and extreme gradient boosting (XGB), to assess their performance. Additionally, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model, and global and individual interpretive analyses were conducted using importance ranking and Shapley additive explanations (SHAP).ResultsAmong the 380 patients, 95 ultimately had poor prognostic outcomes. In the validation set, the AUC values for CNB, SVM, GNB, MLP, and XGB models were 0.899 (0.816–0.979), 0.916 (0.847–0.982), 0.730 (0.602–0.857), 0.913 (0.834–0.986), and 0.969 (0.937–0.998), respectively. Therefore, the XGB model performed the best among the five algorithms. SHAP analysis revealed that the GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels were the most important variables for poor prognosis.ConclusionThe XGB model developed in this study can effectively predict the risk of poor prognosis in patients with SICH, helping clinicians make personalized and rational clinical decisions. Prognostic risk in patients with SICH is closely associated with GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels.

Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed single-cell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML.

Abstract Colorful circularly polarized luminescence materials are desired for 3D displays, information security and asymmetric synthesis, in which single-emitted materials are ideal owing to self-absorption avoidance, evenly entire-visible-spectrum-covered photon emission and facile device fabrication. However, restricted by the synthesis of chiral broad-luminescent emitters, the realization and application of high-performing single-emitted full-color circularly polarized luminescence is in its infancy. Here, we disclose a single-emitted full-color circularly polarized luminescence system (spiral full-color emission generator), composed of whole-vis-spectrum emissive quantum dots and chiral liquid crystals. The system achieves a maximum luminescence dissymmetry factor of 0.8 and remains an order of 10−1 in visible region by tuning its photonic bandgap. We then expand it to a series of desired customized-color circularly polarized luminescence, build chiral devices and further demonstrate the working scenario in the photoinduced enantioselective polymerization. This work contributes to the design and synthesis of efficient chiroptical materials, device fabrication and photoinduced asymmetric synthesis.

Abstract Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from sputum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges. To explore more levels of global pattern features to promote the distinguishing ability of bacterial categories and maintain sufficient local fine-grained features to ensure accurate localization of ambiguous bacteria simultaneously, we propose a novel dual-branch deformable cross-attention fusion network (DB-DCAFN) for accurate bacterial segmentation. Specifically, we first designed a dual-branch encoder consisting of multiple convolution and transformer blocks in parallel to simultaneously extract multilevel local and global features. We then designed a sparse and deformable cross-attention module to capture the semantic dependencies between local and global features, which can bridge the semantic gap and fuse features effectively. Furthermore, we designed a feature assignment fusion module to enhance meaningful features using an adaptive feature weighting strategy to obtain more accurate segmentation. We conducted extensive experiments to evaluate the effectiveness of DB-DCAFN on a clinical dataset comprising three bacterial categories: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The experimental results demonstrate that the proposed DB-DCAFN outperforms other state-of-the-art methods and is effective at segmenting bacteria from sputum smear images.

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model.

ObjectiveThis study aimed to investigate the microscopic structure and characteristics of nevi on the conjunctiva of the lacrimal caruncle by in vivo confocal microscopy.MethodsIn total, four patients with nevi growing on the lacrimal caruncle conjunctiva were recruited. The morphological characteristics of the nevi were evaluated by in vivo confocal microscopy before excision surgery; the results were compared with histopathological analyses of the surgical specimens.ResultsThe nevi of the four patients were all located at the conjunctiva of the lacrimal caruncle, with a slightly nodular surface, mixed black and brown color, and clear boundary. The nevi were round and highly protruded on the surface of the lacrimal caruncle, with an average diameter of 4.5 ± 1.29 mm. Under in vivo confocal microscopy, the pigmented nevus cells on the conjunctiva of the lacrimal caruncle were observed to be clustered in nests with irregular boundaries. The cells were round or irregular, with clear cell boundaries, hyper-reflective at the periphery, with low reflectivity in the center. Vascular crawling was observed in some regions. Histopathological analysis showed that nevus cells were roughly equal in size and distributed in a nodular pattern. Melanin granules were observed in the cytoplasm. No atypia or mitotic figures of the cells were found.ConclusionThis study revealed that the microstructure of nevi growing on the conjunctiva of the lacrimal caruncle can be identified by in vivo confocal microscopy.

BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.ResultsA total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.ConclusionThe XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.

Abstract Background To study the relationship between objective daily physical activity (PA), as measured by implantable cardioverter defibrillators (ICDs)/cardiac resynchronization therapy defibrillators (CRTDs), and long-term prognoses in patients with age > 75 years at high risk of sudden cardiac death (SCD). Methods In total, 133 patients with age > 75 years old (age 79.52 ± 3.68 years) in the SUMMIT study were retrospectively analysed. The major endpoint was all-cause mortality, and the minor endpoint was cardiac death. Results The mean follow-up time was 57.1 ± 24.2 months (range: from 4 to 96 months). In total, 46 all-cause mortality and 23 cardiac death events occurred. The receiver operating characteristic curve indicated a baseline PA cut-off value of 6.47% (93 min/day) can predict all-cause mortality in patients with age > 75 years, with an area under the curve of 0.670 (95% confidence interval (CI): 0.573–0.767, P = 0.001). The sensitivity was 67.4%, and the specificity was 66.7%. Patients with baseline PA ≤ 6.47% had higher rates of all-cause mortality (51.7% vs 20.5%, P 75 years and high risk of SCD with ICDs/CRTDs. PA monitoring may aid in long-term management of older patients at high risk of SCD.

Abstract Background To evaluate a modified technique for involutional entropion correction in a retrospective cohort study. Methods The patients with involutional entropion eyelid were corrected by tightening the pretarsal orbicularis oculi muscle and excising the excess skin of the lower eyelid. The patients received correction surgery from April 2013 to March 2019 were followed up for more than 6 months postoperatively. The outcome measures included the complications and the recurrence rates. Results Total 152 patients (169 eyes) were included. The mean follow-up period was 29.6 months (range: 6–36 months). Postoperative ectropion (over-correction) was observed in 1 patient with 1 eyelid (0.59%); yet, no further surgery was needed for this patient. Recurrence of entropion was found in 1 patient (0.59%). The patient with recurrent entropion received repeated surgery with the same method and achieved a good eyelid position. Conclusions This study demonstrated that tightening the pretarsal orbicularis oculi muscle and excising the excess skin of the lower eyelid could be an effective surgical method to correct lower eyelid involutional entropion. This method is technically easy with a low recurrence rate and not associated with significant complications in Asians.

AimsTo determine the interaction of electrical storm (ES) and impaired left ventircular ejection fraction (LVEF) on the mortality risk of patients with implantable cardioverter defibrillator (ICD).Methods and resultsA total of 554 Chinese ICD recipients from 2010 to 2014 were retrospectively included and the mean follow-up was 58 months. The proportions of dilated cardiomyopathy and the hypertrophic cardiomyopathy were 26.0% (144/554) and 5.6% (31/554), respectively. There were 8 cases with long QT syndrome, 6 with arrhythmogenic right ventricular cardiomyopathy and 2 with Brugada syndrome. Patients with prior MI accounted for 15.5% (86/554) and pre-implantation syncope accounted for 23.3% (129/554). A total of 199 (35.9%) patients had primary prevention indications for ICD therapy. Both ES and impaired LVEF (

Aims: To evaluate whether low level left vagus nerve stimulation (LLVNS) in early stage of myocardial infarction (MI) could effectively prevent ventricular arrhythmias (VAs) and protect cardiac function, and explore the underlying mechanisms.Methods and Results: After undergoing implantable cardioverter defibrillators (ICD) and left cervical vagal stimulators implantation and MI creation, 16 dogs were randomly divided into three groups: the MI (n = 6), MI+LLVNS (n = 5), and sham operation (n = 5) groups. LLVNS was performed for 3 weeks. VAs, the left ventricular function, the density of the nerve fibers in the infarction area and gene expression profiles were analyzed. Compared with the MI group, dogs in the MI+LLVNS group had a lower VAs incidence (p < 0.05) and better left ventricular function. LLVNS significantly inhibited excessive sympathetic nerve sprouting with the evidences of decreased density of TH, GAP43 and NF positive nerves (p < 0.05). The gene expression profiling found a total of 206 genes differentially expressed between MI+LLVNS and MI dogs, mainly involved in cardiac tissue remodeling, cardiac neural remodeling, immune response and apoptosis. These genes, including 55 up-regulated genes and 151 down-regulated genes, showed more protective expressions under LLVNS.Conclusions: This study suggests that LLVNS was delivered without altering heart rate, contributing to reduced incidences of VAs and improved left ventricular function. The potential mechanisms included suppressing cardiac neuronal sprouting, inhibiting excessive sympathetic nerve sprouting and subduing pro-inflammatory responses by regulating gene expressions from a canine experimental study.

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

Objective: To clarify the impact of sex on physical activity (PA) levels among patients with implantable cardioverter-defibrillators/cardiac resynchronization therapy defibrillators (ICD/CRT-D) and its association with cardiac death and all-cause mortality.Methods: Overall, data of 820 patients with ICD/CRT-D from the SUMMIT registry were retrospectively analyzed. Baseline PA from 30 to 60 days after device implantation was measured using Biotronik accelerometer sensors. The primary and secondary endpoints were cardiac death and all-cause mortality, respectively.Results: Baseline PA levels were significantly higher in male patients than in female patients (11.40 ± 5.83% vs. 9.93 ± 5.49%, P = 0.001). Males had higher predictive PA cut-off values for cardiac death (11.16 vs. 7.15%) and all-cause mortality (11.33 vs. 7.17%). During the median follow-up time of 75.7 ± 29.1 months, patients with baseline PA

Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.

Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed singlecell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML. [ABSTRACT FROM AUTHOR]

Abstract In coal mining, coal rock fracturing damage and leakage pose significant challenges. To study the relationship between piezoelectric signals and seepage characteristics during uniaxial compression, and to achieve leakage monitoring based on piezoelectric signals, a coal rock fracturing damage and leakage monitoring experiment was carried out using coal from the Zhaogu Mine in Henan Province. The wavelet packet energy was introduced to investigate the piezoelectric characteristics. The results indicate the relative variation in wavelet packet energy server as a damage index during the uniaxial compression of coal, correlating closely with permeability changes. The wavelet packet energy at the end of the elastic deformation stage was 0.26 V2, similar to the pre-compression value of 0.29 V2. Therefore, the overall damage in the compression and elastic deformation stages was approximately 0. Permeability values remained stable, fluctuating from 0.26 × 10−16 m2 to 0.11 × 10−16 m2. In the plastic deformation stage, wavelet packet energy decreased while the damage index and permeability rose. Prior to peak stress, wavelet packet energy and damage index plateaued, signaling a precursor to a sudden increase in permeability. Post-peak stress, a marked decline in wavelet packet energy and an increase in the damage index and permeability were observed. This study offers a method for monitoring coal rock leakage, contributing valuable insights for efficient coalbed methane extraction and timely safety alerts.

Abstract Carbapenem‐resistant Acinetobacter baumannii (CRAB) has become a major threat in the treatment of bacterial infection, and immunotherapy in a non‐antibiotic‐dependent manner is an effective way to overcome CRAB infection. However, the role of the innate immune response in CRAB infection is poorly understood. Here, it is reported that CRAB infection induced a cytosolic DNA‐sensing signaling pathway and significant IFN‐β production in mice post‐CRAB infection. The knockout of STING reduced bacterial burden, the production of inflammatory cytokines, and lung injury in mice post CRAB infection. The cytosolic DNA sensor cyclic GMP‐AMP synthase (cGAS) and the adaptor protein stimulator of interferon genes (STING) are required for CRAB‐induced IFN‐β expression in macrophages. Intriguingly, CRAB utilized outer membrane vesicles (OMVs) to transport outer membrane protein 38 (OMP38) into mitochondria, triggering mitochondrial DNA (mtDNA) release into the cytosol through the mitochondrial permeability transition pore (mPTP) and activating the cGAS‐STING signaling. Finally, epigallocatechin gallate (EGCG) is demonstrated to block the activation of the cGAS‐STING pathway and ameliorate CRAB‐induced excessive inflammatory response. These results demonstrated that the early innate immune response to CRAB infection is activated in a cGAS‐STING‐dependent manner, which could be a potential therapeutic target for CRAB infection.

Abstract Background The RNA N 6-methyladenosine (m6A) modification has become an essential hotspot in epigenetic modulation. Serine–arginine protein kinase 1 (SRPK1) is associated with the pathogenesis of various cancers. However, the m6A modification of SRPK1 and its association with the mechanism of in lung adenocarcinoma (LUAD) remains unclear. Methods Western blotting and polymerase chain reaction (PCR) analyses were carried out to identify gene and protein expression. m6A epitranscriptomic microarray was utilized to the assess m6A profile. Loss and gain-of-function assays were carried out elucidate the impact of METTL3 and SRPK1 on LUAD glycolysis and tumorigenesis. RNA immunoprecipitation (RIP), m6A RNA immunoprecipitation (MeRIP), and RNA stability tests were employed to elucidate the SRPK1’s METTL3-mediated m6A modification mechanism in LUAD. Metabolic quantification and co-immunoprecipitation assays were applied to investigate the molecular mechanism by which SRPK1 mediates LUAD metabolism. Results The epitranscriptomic microarray assay revealed that SRPK1 could be hypermethylated and upregulated in LUAD. The main transmethylase METTL3 was upregulated and induced the aberrant high m6A levels of SRPK1. Mechanistically, SRPK1’s m6A sites were directly methylated by METTL3, which also stabilized SRPK1 in an IGF2BP2-dependent manner. Methylated SRPK1 subsequently promoted LUAD progression through enhancing glycolysis. Further metabolic quantification, co-immunoprecipitation and western blot assays revealed that SRPK1 interacts with hnRNPA1, an important modulator of PKM splicing, and thus facilitates glycolysis by upregulating PKM2 in LUAD. Nevertheless, METTL3 inhibitor STM2457 can reverse the above effects in vitro and in vivo by suppressing SRPK1 and glycolysis in LUAD. Conclusion It was revealed that in LUAD, aberrantly expressed METTL3 upregulated SRPK1 levels via an m6A-IGF2BP2-dependent mechanism. METTL3-induced SRPK1 fostered LUAD cell proliferation by enhancing glycolysis, and the small-molecule inhibitor STM2457 of METTL3 could be an alternative novel therapeutic strategy for individuals with LUAD.

Abstract The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC.

Abstract A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.