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Abstract Background The effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear. Methods A total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan–Meier analysis were used to assess poor prognosis. Results During the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54–5.66]; p = 0.001) and CAD-RADS categories 3–5 (HR = 16.31[6.34–41.92]; p

AimThe aim of this study was to develop a validated nomogram to predict the risk of postoperative complications in colorectal cancer (CRC) patients by analyzing the factors that contribute to these complications.MethodsWe retrospectively collected clinical information on patients who underwent CRC surgery at a single clinical center from January 2021 to December 2021. Univariate and multivariate logistic regression analysis to identify independent risk factors for postoperative complications and to develop a predictive model. A receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) to assess the predicted probability. Calibration curve was drawn to compare the predicted probability of the nomogram with the actual probability, and decision curve analysis (DCA) was employed to evaluate the clinical utility of the nomogram.ResultsA total of 190 CRC patients were included in this study. We retrospectively collected baseline information, clinical information, surgical information, and nutrition-related indicators for all patients. Through multivariate logistic regression analysis, preoperative albumin (p = 0.041, OR = 0.906, 95% CI = 0.824–0.996), surgical time (p = 0.009, OR = 1.006, 95% CI = 1.001–1.010), waistline (p = 0.049, OR = 1.011, 95% CI = 1.002–1.020) and phase angle (PA) (p = 0.022, OR = 0.615, 95% CI = 0.405–0.933) were identified as independent risk factors for postoperative complications in CRC, and a nomogram prediction model was established using the above four variables. The AUC of 0.706 for the ROC plot and the high agreement between predicted and actual probabilities in the calibration curves suggested that the prediction model has good predictive power. The DCA also confirmed the good clinical performance of the nomogram.ConclusionThis study developed a nomogram to predict the risk of postoperative complications in CRC patients, providing surgeons with a reliable reference to personalized patient management in the perioperative period and preoperative nutritional interventions.

The digital twin is the concept of transcending reality, which is the reverse feedback from the real physical space to the virtual digital space. People hold great prospects for this emerging technology. In order to realize the upgrading of the digital twin industrial chain, it is urgent to introduce more modalities, such as vision, haptics, hearing and smell, into the virtual digital space, which assists physical entities and virtual objects in creating a closer connection. Therefore, perceptual understanding and object recognition have become an urgent hot topic in the digital twin. Existing surface material classification schemes often achieve recognition through machine learning or deep learning in a single modality, ignoring the complementarity between multiple modalities. In order to overcome this dilemma, we propose a multimodal fusion network in our article that combines two modalities, visual and haptic, for surface material recognition. On the one hand, the network makes full use of the potential correlations between multiple modalities to deeply mine the modal semantics and complete the data mapping. On the other hand, the network is extensible and can be used as a universal architecture to include more modalities. Experiments show that the constructed multimodal fusion network can achieve 99.42% classification accuracy while reducing complexity.

Abstract. Background:. Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. Methods:. In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. Results:. A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94–0.99), 0.93 (95% CI: 0.85–0.98), and 0.94 (95% CI: 0.87–0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63–0.83), 0.72 (95% CI: 0.62–0.82), and 0.73 (95% CI: 0.62–0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model (P

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Abstract Background The advanced lung cancer inflammation index (ALI) is a comprehensive assessment indicator that can reflect inflammation and nutrition conditions. However, there are some controversies about whether ALI is an independent prognostic factor for gastrointestinal cancer patients undergoing surgical resection. Thus, we aimed to clarify its prognostic value and explore the potential mechanisms. Methods Four databases including PubMed, Embase, the Cochrane Library, and CNKI were used for searching eligible studies from inception to June 28, 2022. All gastrointestinal cancers, including colorectal cancer (CRC), gastric cancer (GC), esophageal cancer (EC), liver cancer, cholangiocarcinoma, and pancreatic cancer were enrolled for analysis. We focused on prognosis most in the current meta-analysis. Survival indicators, including overall survival (OS), disease-free survival (DFS), and cancer-special survival (CSS) were compared between the high ALI group and the low ALI group. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was submitted as a supplementary document. Results We finally included fourteen studies involving 5091 patients in this meta-analysis. After pooling the hazard ratios (HRs) and 95% confidence intervals (CIs), ALI was found to be an independent prognostic factor for both OS (HR = 2.09, I2 = 92%, 95% CI = 1.53 to 2.85, P

Abstract Background Differentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST. Methods One hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer–Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness. Results The age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer–Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. Conclusions A radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

Abstract Background To establish and verify a radiomics nomogram for differentiating isolated micronodular adrenal hyperplasia (iMAD) from lipid-poor adenoma (LPA) based on computed tomography (CT)-extracted radiomic features. Methods A total of 148 patients with iMAD or LPA were divided into three cohorts: a training cohort (n = 72; 37 iMAD and 35 LPA), a validation cohort (n = 36; 22 iMAD and 14 LPA), and an external validation cohort (n = 40; 20 iMAD and 20 LPA). Radiomics features were extracted from contrast-enhanced and non-contrast CT images. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a triphasic radiomics model and unenhanced radiomics model using reproducible radiomics features. A clinical model was constructed using certain laboratory variables and CT findings. Radiomics nomogram was established by selected radiomics signature and clinical factors. Nomogram performance was assessed by calibration curve, the areas under receiver operating characteristic curves (AUC), and decision curve analysis (DCA). Results Eleven and eight extracted features were finally selected to construct an unenhanced radiomics model and a triphasic radiomics model, respectively. There was no significant difference in AUC between the two models in the external validation cohort (0.838 vs. 0.843, p = 0.949). The radiomics nomogram inclusive of the unenhanced model, maximum diameter, and aldosterone showed the AUC of 0.951, 0.938, and 0.893 for the training, validation, and external validation cohorts, respectively. The nomogram showed good calibration, and the DCA demonstrated the superiority of the nomogram compared with the clinical factors model alone in terms of clinical usefulness. Conclusions A radiomics nomogram based on unenhanced CT images and clinical variables showed favorable performance for distinguishing iMAD from LPA. In addition, an efficient unenhanced model can help avoid extra contrast-enhanced scanning and radiation risk.

Key points Distinguishing carotid plaques with intraplaque hemorrhage is difficult by conventional imaging. A radiomics nomogram facilitates differentiation of carotid plaques with intraplaque hemorrhage. CT-based nomogram may be a non-invasive tool to identify carotid plaque vulnerability.

Abstract Background Elucidating the phylogenetic relationships within species-rich genera is essential but challenging, especially when lineages are assumed to have been going through radiation events. Mahonia Nutt. (Berberidaceae) is a genus with cosmopolitan distribution, comprising approximately 100 species, two of which are known as Caulis Mahoniae (M. bealei and M. fortunei) with crucial pharmacological significance in Chinese herbal medicine. Mahonia is a taxonomically challenging genus, and intrageneric phylogenetic relationships still need to be explored using genome data. Universal DNA barcodes and floral morphological attributes have limited discriminatory power in Mahonia. Results We sequenced 17 representative plastomes and integrated three published plastome data together to conduct comparative and phylogenetic analyses. We found that Mahonia and Berberis share a large IR expansion (~ 12 kb), which is recognized as a typical character of Berberideae. Repeated sequences are revealed in the species of Mahonia, which are valuable for further population genetic studies. Using a comparative plastome analysis, we determined eight hypervariable regions whose discriminative power is comparable to that of the whole plastid genomes. The incongruence of the ITS and the plastome tree topologies may be ascribed to ancestral hybridization events and/or to incomplete lineage sorting. In addition, we suggest that leaf epidermal characters could help to distinguish closely related species in Mahonia. Conclusions We propose an integrative approach combining special barcodes and micromorphological traits to circumscribe Mahonia species. The results cast a new light on the development of an integrative method for accurate species circumscription and provide abundant genetic resources for further research on Mahonia.

Abstract Background Whether hypokalemia can affect the short-term outcomes of CRC patients after radical surgery remains unclear. The purpose of this study was to investigate the impact of preoperative hypokalemia on the short-term outcomes for colorectal cancer (CRC) patients who underwent radical CRC surgery using propensity score matching (PSM). Methods We retrospectively enrolled consecutive CRC patients from Jan 2011 to Dec 2021 in a single-center hospital. Hypokalemia was defined as a serum potassium concentration 0.05). Regarding short-term outcomes, the hypokalemia group had a longer hospital stay (p = 0.028), a higher proportion of overall complications (p = 0.048) and a higher incidence of postoperative pneumonia (p = 0.008) after PSM. Moreover, hypokalemia (p = 0.036, OR = 1.291, 95% CI = 1.017–1.639) was an independent risk factor for overall complications. Conclusion Preoperative hypokalemia could increase complications after CRC surgery and prolong the hospital stay. Moreover, preoperative hypokalemia was an independent risk factor for overall complications.

With the increasing demand for reliable power supply and the widespread integration of distributed energy sources, the topology of distribution networks is subject to frequent changes. Consequently, the dynamic alterations in the connection relationships between distribution transformers and feeders occur frequently, and these changes are not accurately monitored by grid companies in real-time. In this paper, we present a data-driven machine learning approach for identifying the feeder-transformer relationship in distribution networks. Initially, we preprocess the collected three-phase voltage magnitude data of distribution transformers, addressing data quality and enhancing usability through three-phase voltage normalization. Subsequently, we derive the correlation coefficient calculations between distribution transformers, as well as between distribution transformers and feeders. To tackle the challenging task of determining the correlation coefficient threshold, we propose a multi-feature fusion approach. We extracted additional features from the feeders and combined them with the correlation coefficients to create a feature matrix. Machine learning algorithms were then applied to calculate the results. Through experimentation on a real distribution network in Jiangxi province, we demonstrated the effectiveness of the proposed method. When compared to other approaches, our method achieved outstanding results with an F1 score of 0.977, indicating high precision and recall. The precision value was 0.973 and the recall value was 0.981. Importantly, our method eliminates the need for additional measurement installations, as the required data can be obtained using existing collection devices. This significantly reduces the application cost associated with implementing our approach.

Abstract Purpose The purpose of the current study was to compare the outcomes of temporary stoma through the specimen extraction site (SSES) and stoma through a new site (SNS) after laparoscopic low anterior resection. Methods The rectal cancer patients who underwent laparoscopic low anterior resection plus temporary ileostomy were recruited in a single clinical database from Jun 2013 to Jun 2020. The SSES group and the SNS group were compared using propensity score matching (PSM) analysis. Results A total of 257 rectal cancer patients were included in this study, there were 162 patients in the SSES group and 95 patients in the SNS group. After 1:1 ratio PSM, there was no difference in baseline information (p > 0.05). The SSES group had smaller intraoperative blood loss (p = 0.016 0.05). In the multivariate analysis, longer operation time was an independent factor (p = 0.019

Abstract Purpose The purpose of this study is to analyze the effect of body mass index (BMI) on patients with concurrent colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM). Methods Patients who underwent primary radical CRC surgery from Jan 2011 to Jan 2020 were retrospectively collected. The perioperative information, overall survival (OS) and disease-free survival (DFS) were compared between the higher BMI group and the lower BMI group. Results A total of 574 patients with concurrent CRC and T2DM were included in this study. The higher BMI group had higher portion of hypertension (p

Abstract Purpose The purpose of this study was to investigate the short-term outcomes and prognosis of elderly and very elderly colorectal cancer (CRC) patients after primary CRC surgery using propensity score matching (PSM). Methods This study retrospectively collected the medical records of CRC patients ≥ 65 years old undergoing primary CRC surgery from Jan 2011 to Jan 2020. Short-term outcomes, overall survival (OS) and disease-free survival (DFS) were compared between very elderly CRC patients (≥ 80 years old) and elderly CRC patients (65–79 years old). Results A total of 2084 patients were enrolled for analysis. After PSM, 331 very elderly patients were matched to 331 elderly patients. In terms of short-term outcomes, the very elderly patients had longer postoperative hospital stays (p = 0.007) after PSM. In terms of OS, it was found that age (p

Abstract Purpose The purpose of the current study is to analyze the difference of short-term and oncologic outcomes between younger and older colorectal cancer (CRC) patients who underwent primary CRC surgery using a propensity score matching (PSM) analysis. Methods We retrospectively collected CRC patients who underwent primary surgery in a single clinical database from Jan 2011 to Jan 2020. The short-term and oncologic outcomes were compared between younger aged group and older aged group. Results A total of 4599 patients were included in this study, and there were 4196 patients in older aged group and 403 patients in younger aged group. After 1:1 ratio PSM, there were 401 patients in each group. No significant difference was found in terms of baseline information after PSM (p>0.05). Younger aged group had larger retrieved lymph nodes before (p