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Purpose: Prediction of Fuhrman nuclear grade is crucial for making informed herapeutic decisions in clear cell renal cell carcinoma (ccRCC). The current study aimed to develop a multi-information fusion model utilizing computed tomography (CT)-based features of tumors and preoperative biochemical parameters to predict the Fuhrman nuclear grade of ccRCC in a non-invasive manner. Methods: 218 ccRCC patients confirmed by histopathology were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and establish a model for predicting the Fuhrman grade in ccRCC. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration, the 10-fold cross-validation method, bootstrapping, the Hosmer–Lemeshow test, and decision curve analysis (DCA). Results: R.E.N.A.L. Nephrometry Score (RNS) and serum tumor associated material (TAM) were identified as independent predictors for Fuhrman grade of ccRCC through multivariate logistic regression. The areas under the ROC curve (AUC) for the multi-information fusion model composed of the above two factors was 0.810, higher than that of the RNS (AUC 0.694) or TAM (AUC 0.764) alone. The calibration curve and Hosmer–Lemeshow test showed the integrated model had a good fitting degree. The 10-fold cross-validation method (AUC 0.806) and bootstrap test (AUC 0.811) showed the good stability of the model. DCA demonstrated that the model had superior clinical utility. Conclusion: A multi-information fusion model based on CT features of tumor and routine biochemical indicators, can predict the Fuhrman grade of ccRCC using a non-invasive approach. This model holds promise for assisting clinicians in devising personalized management strategies. [ABSTRACT FROM AUTHOR]

Membership determination of text strings has been an important procedure for analyzing textual data of a tremendous amount, especially when time is a crucial factor. Bloom filter has been a well-known approach for dealing with such a problem because of its succinct structure and simple determination procedure. As determination of membership with classification is becoming increasingly desirable, parallel Bloom filters are often implemented for facilitating the additional classification requirement. The parallel Bloom filters, however, tend to produce additional false-positive errors since membership determination must be performed on each of the parallel layers. We propose a scheme based on CMAC, a neural network mapping, which only requires a single-layer calculation to simultaneously obtain information of both the membership and classification. A hash function specifically designed for text strings is also proposed. The proposed scheme could effectively reduce false-positive errors by converging the range of membership acceptance to the minimum for each class during the neural network mapping. Simulation results show that the proposed scheme committed significantly less errors than the benchmark, parallel Bloom filters, with limited and identical memory usage at different classification levels. [ABSTRACT FROM AUTHOR]

Purpose: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Methods: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Results: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Conclusion: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. Methods: Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. Results: The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. Conclusions: Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance. [ABSTRACT FROM AUTHOR]

Introduction: Whether metformin reduces all-cause cardiovascular mortality and the incidence of cardiovascular events in patients with pre-existing cardiovascular diseases (CVD) remains inconclusive. Some randomised controlled trials (RCTs) and cohort studies have shown that metformin is associated with an increased risk of mortality and cardiovascular events. Methods: We conducted a pooling synthesis to assess the effects of metformin in all-cause cardiovascular mortality and incidence of cardiovascular events in patients with CVD. Studies published up to October 2021 in PubMed or Embase with a registration in PROSPERO (CRD42020189905) were collected. Both RCT and cohort studies were included. Hazard ratios (HR) with 95% CI were pooled across various trials using the random-effects model. Results: This study enrolled 35 published studies (in 14 publications) for qualitative synthesis and identified 33 studies (published in 26 publications) for quantitative analysis. We analysed a total of 61,704 patients, among them 58,271 patients were used to calculate all-cause mortality while 12,814 patients were used to calculate cardiovascular mortality. Compared with non-metformin control, metformin usage is associated with a reduction in all-cause mortality (HR: 0.90; 95% CI 0.83, 0.98; p = 0.01), cardiovascular mortality (HR: 0.89; 95% CI 0.85, 0.94; p < 0.0001), incidence of coronary revascularisation (HR: 0.79; 95% CI 0.64, 0.98; p = 0.03), and heart failure (HR: 0.90; 95% CI 0.87, 0.94; p < 0.0001) in patients with pre-existing cardiovascular diseases. Conclusion: Metformin use is associated with a reduction in all-cause mortality, cardiovascular mortality, incidence of coronary revascularisation, and heart failure in patients with CVD; however, metformin usage was not associated with reduction in the incidence of myocardial infarction, angina, or stroke. [ABSTRACT FROM AUTHOR]

Deep aquifers are a common geological disaster source in coal mines and they can lead to water and mud inrush disasters. Grouting is the most effective method to reinforce aquifers and block water. However, grouting only at the water inrush area cannot obtain a stable effect. A combination of groundwater runoff analysis and grouting keyholes optimization was adopted to solve the problem of water inrush in deep mines. First, the structural pattern of the grouting area was identified and the position of the water inrush channel was deduced, yielding the mine's groundwater runoff pattern. Then, the quality of grouting holes was evaluated by AHP analysis and the keyholes in the runoff network were determined according to the maximum membership degree principle. Finally, the keyholes were blocked by grouting. After the grouting treatment, the grouting effect was evaluated by using working face water inflow and water detection radar. The results indicate that combining groundwater runoff analysis with keyhole selection can effectively control water inrush in deep mining. [ABSTRACT FROM AUTHOR]

A quantitative analysis of contrast-enhanced spectral mammography (CESM) enhancement was conducted for the early prediction of the pathological response after neoadjuvant chemotherapy (NAC). Retrospective analysis of the data of 111 patients was conducted, and all of them underwent NAC in our hospital and surgical resection after the end of all cycles from January 2018 to May 2019. They were divided into pathological complete response (PCR) and non-PCR groups. We determined whether a statistical difference in the percentage of CESM grey value reduction (ΔCGV) was present in the PCR and non-PCR groups and whether a statistical difference was observed in the diagnostic efficiency of craniocaudal (CC) and mediolateral oblique (MLO) view subtraction images. Independent sample t-test was used to compare different groups, the receiver operating characteristic (ROC) curve was used to compare the diagnostic efficacy of CC and MLO for pathological response after NAC, and the Delong test was used to compare the area under the ROC curve (AUC). Statistical significance was considered at P < 0.05. A statistical difference was observed in the ΔCGV in the PCR and non-PCR groups. No statistical difference was observed in the AUCs of CC and MLO view subtraction images. The ΔCGV can be used as a quantitative index to predict PCR early, and no statistical difference was observed in the diagnostic efficacy of CC and MLO view subtraction images. [ABSTRACT FROM AUTHOR]

Objective: This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets. Results: The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689–0.858), 0.767 (95% CI 0.583–0.857), and 0.79 (95% CI 0.63–0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as − 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%. Conclusions: The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer. Key Points: • The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers. [ABSTRACT FROM AUTHOR]

The influence of lubrication on profile and flatness control during asymmetry self-compensating work roll (ASR) hot strip rolling process is studied in order to meet the requirement of schedule-free rolling (SFR) campaigns for non-oriented electrical steel production with identical strip width. The effects of lubrication on rolling force, surface topography, and work roll wear are analyzed through experimental data. A work roll wear model for a wide non-oriented electrical steel production considered lubrication during the hot rolling process is built, which can accurately reflect the work roll wear during the hot rolling process with lubrication. A 3D finite element model for roll stack considering lubrication is built. The effects of shifting strategy on ASR work roll wear contour, loaded roll gap crown, and edge drop are analyzed based on the models. The optimal shifting rhythm is 1, and the optimal shifting step is 2 ~ 3 mm. By combining the ASR technology with lubrication, the life of work rolls expands from 70 to 100 ~ 150 by different shifting step, the loaded roll gap crown can be efficiently controlled by bending force and the edge drop control ability can be improved efficiently within an entire rolling campaign. [ABSTRACT FROM AUTHOR]

This work reports an investigation on the performance improvement of bulk heterojunction polymer solar cells made of poly(3-hexylthiophene) and [6,6]-phenyl C-butyric acid methyl ester (P3HT:PCBM) blend system by introducing nickel nanoparticles (Ni NPs). Using the simplest cell structure, the addition ratio of 2 % of Ni NPs results in a remarkable increase in the light absorption and the short current, which brings an enhancement on power conversion efficiency with 25 % compared with the reference device. The analysis indicates that the electromagnetic surface wave generated from Ni NPs coupled with the photoactive layer forms surface plasmon resonance, which can result in a light trapping effect to increase the light absorption. As a conclusion, the inexpensive Ni NPs may provide an effect and alternative process on the performance improvement of the polymer solar devices. [ABSTRACT FROM AUTHOR]

A simple, low-cost, post-black etching process atop the random pyramidal emitter has been proposed and investigated. The multi-scale texture is achieved by combining nanoporous layer formed by the post-black etching with micron-scale pyramid texture. Compared to the pre-black etched Si solar cells, our experiments clearly show the advantage of post-black etched texturing: it enables high blue response and improved conversion efficiency. As a result, the enhancement of 7.1 mA/cm on the short-circuit current density and improvement of 31 % in the conversion efficiency have been reached. [ABSTRACT FROM AUTHOR]

The Si nanowire array (SiNWA) was achieved by metal-assisted etching. The effect of alkali-treating of SiNWA on the photovoltaic performance of solar cells was investigated. We found that the post-chemical treatment of SiNWA on solar cells could reduce (i) the surface reflection and (ii) the thickness of “dead layer” so that the short-wavelength spectral response is enhanced. As a result, a solar-spectrum-averaged reflectance of less than 3 % has been reached, a 3-fold enhancement on the spectral response of post-treated SiNWA cell at the wavelength of 400 nm has been observed, together resulting in a remarkable improvement of 45 % in the conversion efficiency. [ABSTRACT FROM AUTHOR]