Material Removal Mechanisms of Polycrystalline Silicon Carbide Ceramic Cut by a Diamond Wire Saw.

Polycrystalline silicon carbide (SiC) is a highly valuable material with crucial applications across various industries. Despite its benefits, processing this brittle material efficiently and with high quality presents significant challenges. A thorough understanding of the mechanisms involved in processing and removing SiC is essential for optimizing its production. In this study, we investigated the sawing characteristics and material removal mechanisms of polycrystalline silicon carbide (SiC) ceramic using a diamond wire saw. Experiments were conducted with high wire speeds of 30 m/s and a maximum feed rate of 2.0 mm/min. The coarseness value (Ra) increased slightly with the feed rate. Changes in the diamond wire during the grinding process and their effects on the grinding surface were analyzed using scanning electron microscopy (SEM), laser confocal microscopy, and focused ion beam (FIB)-transmission electron microscopy (TEM). The findings provide insights into the grinding mechanisms. The presence of ductile grinding zones and brittle fracture areas on the ground surface reveals that external forces induce dislocation and amorphization within the grain structure, which are key factors in material removal during grinding.

Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study.

OBJECTIVE: The early differentiation of adrenal lipid-poor adenomas from non-adenomas is a crucial step in reducing excessive examinations and treatments. This study seeks to construct an eXtreme Gradient Boosting (XGBoost) predictive model utilizing the minimum attenuation values (minAVs) from non-contrast CT (NCCT) scans to identify lipid-poor adenomas. MATERIALS AND METHODS: Retrospective analysis encompassed clinical data, minAVs, CT histogram (CTh), mean attenuation values (meanAVs), and lesion diameter from patients with pathologically or clinically confirmed adrenal lipid-poor adenomas across two medical institutions, juxtaposed with non-adenomas. Variable selection transpired in Institution A (training set), with XGBoost models established based on minAVs and CTh separately. Institution B (validation set) corroborated the diagnostic efficacy of the two models. Receiver operator characteristic (ROC) curve analysis, calibration curves, and Brier scores assessed the diagnostic performance and calibration of the models, with the Delong test gauging differences in the area under the curve (AUC) between models. SHapley Additive exPlanations (SHAP) values elucidated and visualized the models. RESULTS: The training set comprised 136 adrenal lipid-poor adenomas and 126 non-adenomas, while the validation set included 46 and 40 instances, respectively. In the training set, there were substantial inter-group differences in minAVs, CTh, meanAVs, diameter, and body mass index (BMI) (p < 0.05 for all). The AUC for the minAV and CTh models were 0.912 (95% confidence interval [CI]: 0.866-0.957) and 0.916 (95% CI: 0.873-0.958), respectively. Both models exhibited good calibration, with Brier scores of 0.141 and 0.136. In the validation set, the AUCs were 0.871 (95% CI: 0.792-0.951) and 0.878 (95% CI: 0.794-0.962), with Brier scores of 0.156 and 0.165, respectively. The Delong test revealed no statistically significant differences in AUC between the models (p > 0.05 for both). SHAP value analysis for the minAV model suggested that minAVs had the highest absolute weight (AW) and negative contribution. CONCLUSION: The XGBoost predictive model based on minAVs demonstrates effective discrimination between adrenal lipid-poor adenomas and non-adenomas. The minAV variable is easily obtainable, and its diagnostic performance is comparable to that of the CTh model. This provides a basis for patient diagnosis and treatment plan selection.

Combination of Epicardial Adipose Tissue and Left Atrial Low-Voltage Areas Predicting Atrial Fibrillation Recurrence after Radiofrequency Ablation.

INTRODUCTION: Atrial fibrillation (AF) is a common arrhythmia, with radiofrequency catheter ablation (RFCA) being first-line therapy. However, the high rate of post-ablation recurrence necessitates the identification of predictors for recurrence risk. Left atrial low-voltage areas (LA-LVASs), reflecting atrial fibrosis, have been confirmed to be related to recurrence of AF. Recently, epicardial adipose tissue (EAT) has been studied due to its role in initiating and maintaining AF. In this study, we try to evaluate the significance of the combined use of left atrial epicardial adipose tissue (LA-EAT) and percentage of LA-LVAs (LA-LVAs%) for predicting the recurrence of AF. METHODS: A total of 387 patients with AF who had undergone RFCA for the first time were followed up for 1, 3, 6, and 12 months. They were divided into two groups: the recurrence group (n = 90) and the non-recurrence group (n = 297). Before the ablation, all patients underwent computed tomography angiography examination of the left atrium, and the LA-EAT was measured using medical software (Advantage Workstation 4.6, GE, USA). After circumferential pulmonary vein isolation, a three-dimensional mapping system was used to map the LA endocardium and evaluate the LA-LVAs in sinus rhythm. RESULTS: After a median follow-up of 10.2 months, 90 patients developed AF recurrence after RFCA. Compared to patients without recurrence, the volume of LA-EAT (33.45 ± 13.65 vs. 26.27 ± 11.38; p < 0.001) and the LA-LVAs% (1.60% [0%, 9.99%] vs. 0.00% [0%, 2.46%]; p < 0.001) was significantly higher. Multivariate analysis indicated that PersAF, LA-EAT volume, and LA-LVAs% were independent predictors. Compared to PersAF (AUC 0.628; specificity 0.646; sensitivity 0.609), LA-EAT volume (AUC 0.655; specificity 0.675; sensitivity 0.586), or LA-LVAs% (AUC 0.659; specificity 0.836; sensitivity 0.437), the combined use of LA-EAT volume and LA-LVAs% offers higher accuracy for predicting AF recurrence after ablation (AUC 0.738; specificity 0.761; sensitivity 0.621). CONCLUSION: The combined LA-EAT and LA-LVAs% can effectively predict the risk of AF recurrence after radiofrequency ablation.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma.

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Artificial intelligence model system for bone age assessment of preschool children.

BACKGROUD: Our study aimed to assess the impact of inter- and intra-observer variations when utilizing an artificial intelligence (AI) system for bone age assessment (BAA) of preschool children. METHODS: A retrospective study was conducted involving a total sample of 53 female individuals and 41 male individuals aged 3-6 years in China. Radiographs were assessed by four mid-level radiology reviewers using the TW3 and RUS-CHN methods. Bone age (BA) was analyzed in two separate situations, with/without the assistance of AI. Following a 4-week wash-out period, radiographs were reevaluated in the same manner. Accuracy metrics, the correlation coefficient (ICC)and Bland-Altman plots were employed. RESULTS: The accuracy of BAA by the reviewers was significantly improved with AI. The results of RMSE and MAE decreased in both methods (p < 0.001). When comparing inter-observer agreement in both methods and intra-observer reproducibility in two interpretations, the ICC results were improved with AI. The ICC values increased in both two interpretations for both methods and exceeded 0.99 with AI. CONCLUSION: In the assessment of BA for preschool children, AI was found to be capable of reducing inter-observer variability and enhancing intra-observer reproducibility, which can be considered an important tool for clinical work by radiologists. IMPACT: The RUS-CHN method is a special bone age method devised to be suitable for Chinese children. The preschool stage is a critical phase for children, marked by a high degree of variability that renders BA prediction challenging. The accuracy of BAA by the reviewers can be significantly improved with the aid of an AI model system. This study is the first to assess the impact of inter- and intra-observer variations when utilizing an AI model system for BAA of preschool children using both the TW3 and RUS-CHN methods.

Prognostic analysis of 131I efficacy after papillary thyroid carcinoma surgery based on CT radiomics.

OBJECTIVE: To develop and validate a radiomics-clinical combined model combining preoperative CT and clinical data from patients with papillary thyroid carcinoma (PTC) to predict the efficacy of initial postoperative 131I treatment. METHODS: A total of 181 patients with PTC who received total thyroidectomy and initial 131I treatment were divided into training and testing sets (7:3 ratio). Univariate analysis and multivariate logistic regression were used to screen clinical factors affecting the therapeutic response to 131I treatment and construct a clinical model. Radiomics features extracted from preoperative CT images of PTCs were dimensionally reduced through recursive feature elimination and least absolute shrinkage and selection operator. Logistic regression was used to establish a radiomics model, and a radiomics-clinical combined model was developed by integrating the clinical model. The area under the curve (AUC), sensitivity, and specificity were used to evaluate the prediction performance of each model. RESULTS: Multivariate analysis revealed that pre-131I treatment sTg was an independent clinical risk factor affecting the efficacy of initial 131I treatment (P = 0.002), and the AUC, sensitivity, and specificity for predicting the efficacy of initial 131I treatment were 0.895, 0.899, and 0.816, respectively. After dimensionality reduction, 14 key CT radiomics features of PTCs were included. The established radiomics model predicted the efficacy of 131I treatment in the training and testing sets with AUCs of 0.825 and 0.809, sensitivities of 0.828 and 0.636, and specificities of 0.745 and 0.944, respectively. The combined model improved the AUC, sensitivity, and specificity in both sets. CONCLUSION: The preoperative CT-based radiomics model can effectively predict the efficacy of initial postoperative 131I treatment in patients with intermediate- or high-risk PTC, and the radiomics-clinical combined model exhibits better predictive performance.

Theoretical Prediction and Experimental Synthesis of Zr3AC2 (A = Cd, Sb) Phases.

MAX phases have great research value and application prospects, but it is challenging to synthesize the MAX phases containing Cd and Sb for the time being. In this paper, we confirmed the existence of the 312 MAX phases of Zr3CdC2 and Zr3SbC2, both from theoretical calculations and experimental synthesis. The Zr3AC2 (A = Cd, Sb) phase was predicted by the first-principles calculations, and the two MAX phases were confirmed to meet the requests of thermal, thermodynamic, and mechanical stabilities using formation energy, phonon dispersion, and the Born-Huang criteria. Their theoretical mechanical properties were also systematically investigated. It was found that the elastic moduli of Zr3CdC2 and Zr3SbC2 were 162.8 GPa and 164.3 GPa, respectively. Then, differences in the mechanical properties of Zr3AC2 (A = Cd, In, Sn, and Sb) were explained using bond layouts and charge transfers. The low theoretical Vickers hardness of the Zr3CdC2 (5.4 GPa) and Zr3SbC2 (4.3 GPa) phases exhibited excellent machinability. Subsequently, through spark plasma sintering, composites containing Zr3CdC2 and Zr3SbC2 phases were successfully synthesized at the temperatures of 850 °C and 1300 °C, respectively. The optimal molar ratio of Zr:Cd/Sb:C was determined as 3:1.5:1.5. SEM and the EDS results analysis confirmed the typical layered microstructure of Zr3CdC2 and Zr3SbC2 grains.

A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features.

OBJECTIVE: The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated. METHODS: A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC). RESULTS: In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21-2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42-4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840-0.946), and in the validation set, it was 0.910 (95% CI: 0.823-0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis. CONCLUSION: The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study.

BACKGROUND: Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. METHODS: We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. FINDINGS: The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). INTERPRETATION: This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. FUNDING: National Natural Science Foundation of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis.

BACKGROUND: Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs. METHODS: Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data. RESULTS: This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85). CONCLUSIONS: These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer.

Purpose: Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC. Methods: A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results: Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890-0.986) compared with the radiomics model (0.829, 95%CI: 0.738-0.898) and the deep learning model (0.935, 95%CI: 0.865-0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779-0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628-0.897) and the deep learning model (0.867, 95% CI: 0.724-0.952). Conclusion: The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.

Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities.

OBJECTIVES: Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment. MATERIALS AND METHODS: A total of 1048 patients comprised the internal (474 patients with SMA abnormalities, 474 controls) and external testing (50 patients with SMA abnormalities, 50 controls) cohorts. The internal cohort was divided into the training cohort (n = 776), validation cohort (n = 86), and internal testing cohort (n = 86). A total of 5 You Only Look Once version 8 (YOLOv8)-based DL submodels were developed, and the performance of the optimal submodel was compared with that of a clinical model and of experienced radiologists. RESULTS: Of the submodels, YOLOv8x had the best performance. The area under the curve (AUC) of the YOLOv8x submodel was higher than that of the clinical model (internal test set: 0.990 vs 0.878, P =.002; external test set: 0.967 vs 0.912, P =.140) and that of all radiologists (P <.001). The YOLOv8x submodel, when compared with radiologist assessment, demonstrated higher sensitivity (internal test set: 100.0 % vs 70.7 %, P =.002; external test set: 96.0 % vs 68.8 %, P <.001) and specificity (internal test set: 90.7 % vs 66.0 %, P =.025; external test set: = 88.0 % vs 66.0 %, P <.001). CONCLUSION: Using plain CT images, YOLOv8x was able to efficiently identify cases of SMA abnormalities. This could potentially improve early diagnosis accuracy and thus improve clinical outcomes.

Electrosynthesis of Hydrogen Peroxide Enabled by Exceptional Molecular Ni Sites in a Graphene-Supported Nickel Organic Framework.

Electrosynthesis of hydrogen peroxide (H2O2) from 2e- transfer of the oxygen reduction reaction (2e--ORR) is a potential alternative to the traditional anthraquinone process. Two-dimensional (2D) metal-organic frameworks (MOFs) supported by carbon are frequently reported as promising 2e--ORR catalysts. Herein, a graphene-supported 2D MOF of Ni3(2,3,6,7,10,11-hexahydrotriphenylene)2 is synthesized through a common hydrothermal method, which exhibits high 2e--ORR performance. It is discovered that except for emerging MOFs, exceptional molecularly dispersed Ni sites coexist in the synthesis that have the same coordination sphere of the NiO4C4 moiety as the MOF. The molecular Ni sites are more catalytically active. The graphene support contains a suitable amount of residual oxygen groups, leading to the generation of those molecularly dispersed Ni sites. The oxygen groups exhibit a moderate electron-withdrawing effect at the outer sphere of Ni sites to slightly increase their oxidation state. This interaction decreases overpotentials and kinetically improves the selectivity of the 2e- reaction pathway.

A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study.

Objective: This study aimed to investigate the application of modified region-of-interest (ROI) segmentation method in unenhanced computed tomography in the radiomics model of adrenal lipid-poor adenoma, and to evaluate the diagnostic performance using an external medical institution data set and select the best ROI segmentation method. Methods: The imaging data of 135 lipid-poor adenomas and 102 non-adenomas in medical institution A and 30 lipid-poor adenomas and 43 non-adenomas in medical institution B were retrospectively analyzed, and all cases were pathologically or clinically confirmed. The data of Institution A builds the model, and the data of Institution B verifies the diagnostic performance of the model. Semi-automated ROI segmentation of tumors was performed using uAI software, using maximum area single-slice method (MAX) and full-volume method (ALL), as well as modified single-slice method (MAX_E) and full-volume method (ALL_E) to segment tumors, respectively. The inter-rater correlation coefficients (ICC) was performed to assess the stability of the radiomics features of the four ROI segmentation methods. The area under the curve (AUC) and at least 95% specificity pAUC (Partial AUC) were used as measures of the diagnostic performance of the model. Results: A total of 104 unfiltered radiomics features were extracted using each of the four segmentation methods. In the ROC analysis of the radiomics model, the AUC value of the model constructed by MAX was 0.925, 0.919, and 0.898 on the training set, the internal validation set, and the external validation set, respectively, and the AUC value of MAX_E was 0.937, 0.931, and 0.906, respectively. The AUC value of ALL was 0.929, 0.929, and 0.918, and the AUC value of ALL_E was 0.942, 0.926, and 0.927, respectively. In all samples, the pAUCs of MAX, MAX_E, ALL, and ALL_E were 0.021, 0.025, 0.018, and 0.028, respectively. Conclusion: The diagnostic performance of the radiomics model constructed based on the full-volume method was better than that of the model based on the single-slice method. The model constructed using the ALL_E method had a stronger generalization ability and the highest AUC and pAUC value.

Recovery and prognostic values of myocardial strain in acute anterior and non-anterior wall myocardial infarction.

BACKGROUND: To assess the recovery and prognostic values of myocardial strain using cardiac magnetic resonance (CMR)- feature tracking (FT) in acute anterior and non-anterior wall myocardial infarction. METHODS: 103 reperfused patients after STEMI who underwent CMR at about 4 days (baseline) and 4 months (follow-up) were included, including 48 and 55 patients with anterior wall myocardial infarction (AWMI) and non-anterior wall myocardial infarction(NAWMI). CMR-FT analysis was performed using cine images to measure LV global radial, circumferential, and longitudinal peak strains (GRS, GCS, and GLS, respectively). Infarct size (IS) and microvascular obstruction (MVO) were estimated by late-gadolinium enhancement imaging. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE) after infarction. RESULTS: Patients with AWMI had higher IS, higher MVO, lower ejection fraction, and more significantly impaired CMR-FT strain values than patients with NAWMI (all p<0.05). Global strain significantly improved at 4 months (all p<0.01), especial in NAWMI. GLS was an independent predictor (odds ratio = 2.08, 95% confidence interval = 1.032-4.227, p = 0.04] even after adjustment for IS and MVO. The optimal cutoff of GLS was -7.9%, with sensitivity and specificity were 73.3% and 75.0%, respectively. In receiver operating characteristic analysis, IS remained the strongest predictor (area under the curve [AUC] = 0.83, p<0.01), followed by MVO (AUC = 0.81, p<0.01) and GLS (AUC = 0.78, p<0.01). CONCLUSION: CMR-FT-derived global myocardial strains significantly improved over time, especial in NAWMI. GLS measurement independently predicted the occurrence of medium-term MACE.

Delays in first medical contact to primary interventional therapy and left ventricular remodelling in ST-segment elevation myocardial infarction.

BACKGROUND: Early reperfusion and early evaluation of adverse cardiovascular events have become important aspects of treatment for ST-segment elevation myocardial infarction post-primary percutaneous coronary intervention (PPCI). However, emergency medical service (EMS) delays always occur, especially in developing countries. AIMS: The aim of this study was to investigate the impact of EMS delays on short-term predictions of the severity of myocardial injury in STEMI patients after PPCI. METHODS: A total of 151 STEMI patients who underwent successful PPCI and two postoperative cardiac magnetic resonance (CMR) imaging examinations (1 week and 4 months postoperatively) were retrospectively analysed. CMR cine and late gadolinium enhancement (LGE) images were analysed to evaluate left ventricular (LV) function, LV global longitudinal peak strain (GLS) and scar characteristics. The time from first medical contact to balloon (FMC2B) and door-to-balloon (D2B) time, expressed in minutes, were recorded and compared with the recommended timelines. Unadjusted and multivariable analyses were used to assess the impact of EMS delays on short-term left ventricular remodelling (ALVR). RESULTS: EMS delays (FMC2B time > 90 min) led to larger infarct size (IS) and microcirculation obstruction (MVO) and poor recovery of the LV ejection fraction and GLS (all p < 0.05). Logistic regression analysis showed that an FMC2B time > 90 min (p = 0.028, OR = 2.661, 95% CI 1.112-6.367) and baseline IS (p = 0.016, OR = 1.079, 95% CI 1.015-1.148) were independent predictors of short-term ALVR. CONCLUSION: Delays in FMC2B time were strongly associated with short-term ALVR; shorter ischaemic times may improve the cardiac function and prognosis of patients.

Ultra-Fast Thermal Shock Evaluation of Ti2AlC Ceramic.

In this work, the rapid thermal shock behavior of Ti2AlC ceramics was studied using induction heating. The present evaluation method possesses the merits of very rapid heating within tens of seconds and fast quenching in water of less than 0.1 s, removing the shortcomings of traditional thermal shock. For comparison, the samples were also quenched in the air to investigate the thermal shock mechanisms. The results showed that the abnormal shock occurred in the samples when quenching in water, ascribed to the formed oxide layer on the surface of Ti2AlC ceramic inhibited the water penetration into the substrate. The quenched Ti2AlC samples still had a high residual flexural strength above 167 MPa up to 1150 °C, exhibiting promising applications in the high-temperature fields.

MAX-phase Derived Tin Diselenide for 2D/2D Heterostructures with Ultralow Surface/Interface Transport Barriers toward Li-/Na-ions Storage.

2D tin diselenide and its derived 2D heterostructures have delivered promising potentials in various applications ranging from electronics to energy storage devices. The major challenges associated with large-scale fabrication of SnSe2 crystals, however, have hindered its engineering applications. Herein, a tin-extraction synthetic method is proposed for producing large-size SnSe2 bulk crystals. In a typical synthesis, a Sn-containing MAX phase (V2 SnC) and a Se source are heat-treated under a reducing atmosphere, by which Sn is extracted from the V2 SnC phase as a rectified Sn source to form SnSe2 crystals in the cold zone. After the following liquid exfoliation, the obtained 2D SnSe2 nanosheets have a lateral size of a few centimeters and an atomic thickness. Furthermore, by coupling with 2D graphene to form 2D/2D SnSe2 /graphene heterostructured electrodes, as validated by theoretical calculation and experimental studies, the superior Li-/Na-ion storage performance with ultralow surface/interface ion transport barriers are achieved for rechargeable Li-/Na-ion batteries. This innovative synthetic strategy opens a new avenue for the large-scale synthesis of selenides and offers more options into the practical application of emerging 2D/2D heterostructure for electrochemical energy storage.

Ultrahigh Temperature Flash Sintering of Binder-Less Tungsten Carbide within 6 s.

We report on an ultrarapid (6 s) consolidation of binder-less WC using a novel Ultrahigh temperature Flash Sintering (UFS) approach. The UFS technique bridges the gap between electric resistance sintering (≪1 s) and flash spark plasma sintering (20-60 s). Compared to the well-established spark plasma sintering, the proposed approach results in improved energy efficiency with massive energy and time savings while maintaining a comparable relative density (94.6%) and Vickers hardness of 2124 HV. The novelty of this work relies on (i) multiple steps current discharge profile to suit the rapid change of electrical conductivity experienced by the sintering powder, (ii) upgraded low thermal inertia CFC dies and (iii) ultra-high consolidation temperature approaching 2750 °C. Compared to SPS process, the UFS process is highly energy efficient (≈200 times faster and it consumes ≈95% less energy) and it holds the promise of energy efficient and ultrafast consolidation of several conductive refractory compounds.

Spark Plasma Sintering of LiFePO4: AC Field Suppressing Lithium Migration.

Our work proposes a comparison between Spark Plasma Sintering of LiFePO4 carried out using an Alternating Current (AC) and Direct Current (DC). It quantifies the Li-ion migration using DC, and it validates such hypothesis using impedance spectroscopy, X-ray photoelectron spectroscopy and inductively coupled plasma optical emission spectroscopy. The use of an AC field seems effective to inhibit undesired Li-ion migration and achieve high ionic conductivity as high as 4.5 × 10-3 S/cm, which exceeds by one order of magnitude samples processed under a DC field. These results anticipate the possibility of fabricating a high-performance all-solid-state Li-ion battery by preventing undesired Li loss during SPS processing.