Strategies for the biological synthesis of D-glucuronic acid and its derivatives.

D-glucuronic acid is a kind of glucose derivative, which has excellent properties such as anti-oxidation, treatment of liver disease and hyperlipidemia, and has been widely used in medicine, cosmetics, food and other fields. The traditional production methods of D-glucuronic acid mainly include natural extraction and chemical synthesis, which can no longer meet the growing market demand. The production of D-glucuronic acid by biocatalysis has become a promising alternative method because of its high efficiency and environmental friendliness. This review describes different production methods of D-glucuronic acid, including single enzyme catalysis, multi-enzyme cascade, whole cell catalysis and co-culture, as well as the intervention of some special catalysts. In addition, some feasible enzyme engineering strategies are provided, including the application of enzyme immobilized scaffold, enzyme mutation and high-throughput screening, which provide good ideas for the research of D-glucuronic acid biocatalysis.

Copper Nanocubes for CO2 Reduction in Gas Diffusion Electrodes.

Electroreduction of CO2 represents a promising solution for addressing the global challenges in energy and sustainability. This reaction is highly sensitive to the surface structure of electrocatalysts and the local electrochemical environment. We have investigated the effect of Cu nanoparticle shape on the electrocatalysis of CO2 reduction by using gas-diffusion electrodes (GDEs) and flowing alkaline catholytes. Cu nanocubes of ∼70 nm in edge length are synthesized with {100} facets preferentially exposed on the surface. They are demonstrated to possess substantially enhanced catalytic activity and selectivity for CO2 reduction, compared to Cu nanospheres of similar particle sizes. The electrocatalytic performance was further found to be dependent on the concentration of electrolyte (KOH). The Cu nanocubes reach a Faradaic efficiency of 60% and a partial current density of 144 mA/cm2 toward ethylene (C2H4) production, with the catalytic enhancement being attributable to a combination of surface structure and electrolyte alkalinity effects.

Ensemble Effect in Bimetallic Electrocatalysts for CO2 Reduction.

Alloying is an important strategy for the design of catalytic materials beyond pure metals. The conventional alloy catalysts however lack precise control over the local atomic structures of active sites. Here we report on an investigation of the active-site ensemble effect in bimetallic Pd-Au electrocatalysts for CO2 reduction. A series of Pd@Au electrocatalysts are synthesized by decorating Au nanoparticles with Pd of controlled doses, giving rise to bimetallic surfaces containing Pd ensembles of various sizes. Their catalytic activity for electroreduction of CO2 to CO exhibits a nonlinear behavior in dependence of the Pd content, which is attributed to the variation of Pd ensemble size and the corresponding tuning of adsorption properties. Density functional theory calculations reveal that the Pd@Au electrocatalysts with atomically dispersed Pd sites possess lower energy barriers for activation of CO2 than pure Au and are also less poisoned by strongly binding *CO intermediates than pure Pd, with an intermediate ensemble size of active sites, such as Pd dimers, giving rise to the balance between these two rate-limiting factors and achieving the highest activity for CO2 reduction.

Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models.

Objective: To validated a classifier to distinguish the status of rotator cuff tear and predict post-operative re-tear by utilizing magnetic resonance imaging (MRI) markers. Methods: This retrospective study included patients with healthy rotator cuff and patients diagnosed as rotator cuff tear (RCT) by MRI. Radiomics features were identified from the pre-operative shoulder MRI and selected by using maximum relevance minimum redundancy (MRMR) methods. A radiomics model for diagnosis of RCT was constructed, based on the 3D volume of interest (VOI) of supraspinatus. Another model for the prediction of rotator re-tear after rotator cuff repair (Re-RCT) was constructed based on VOI of humerus, supraspinatus, infraspinatus and other clinical parameters. Results: The model for diagnosing the status of RCT produced an area under the receiver operating characteristic curve (AUC) of 0.989 in the training cohort and 0.979 for the validation cohort. The radiomics model for predicting Re-RCT produced an AUC of 0.923 ± 0.017 for the training dataset and 0.790 ± 0.082 for the validation dataset. The nomogram combining radiomics features and clinical factors yielded an AUC of 0.961 ± 0.020 for the training dataset and 0.808 ± 0.081 for the validation dataset, which displayed the best performance among all models. Conclusion: Radiomics models for the diagnosis of rotator cuff tear and prediction of post-operative Re-RCT yielded a decent prediction accuracy.

A Clinic-Radiomics Model for Predicting the Incidence of Persistent Organ Failure in Patients with Acute Necrotizing Pancreatitis.

Background: Persistent organ failure (POF) is the leading cause of death in patients with acute necrotizing pancreatitis (ANP). Although several risk factors have been identified, there remains a lack of efficient instruments to accurately predict the incidence of POF in ANP. Methods: Retrospectively, the clinical and imaging data of 178 patients with ANP were collected from our database, and the patients were divided into training (n = 125) and validation (n = 53) cohorts. Through computed tomography image acquisition, the volume of interest segmentation, and feature extraction and selection, a pure radiomics model in terms of POF prediction was established. Then, a clinic-radiomics model integrating the pure radiomics model and clinical risk factors was constructed. Both primary and secondary endpoints were compared between the high- and low-risk groups stratified by the clinic-radiomics model. Results: According to the 547 selected radiomics features, four models were derived from features. A clinic-radiomics model in the training and validation sets showed better predictive performance than pure radiomics and clinical models. The clinic-radiomics model was evaluated by the ratios of intervention and mechanical ventilation, intensive care unit (ICU) stays, and hospital stays. The results showed that the high-risk group had significantly higher intervention rates, ICU stays, and hospital stays than the low-risk group, with the confidence interval of 90% (p < 0.1 for all). Conclusions: This clinic-radiomics model is a useful instrument for clinicians to evaluate the incidence of POF, facilitating patients' and their families' understanding of the ANP prognosis.

Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma.

In this study, we aim to develop and validate a radiomics model for pretreatment prediction of RPS6K expression in hepatocellular carcinoma (HCC) patients, thus helping clinical decision-making of mTOR-inhibitor (mTORi) therapy. We retrospectively enrolled 147 HCC patients, who underwent curative hepatic resection at First Affiliated Hospital Zhejiang University School of Medicine. RPS6K expression was determined with immunohistochemistry staining. Patients were randomly split into training or validation cohorts on a 7:3 ratio. Radiomics features were extracted from T2-weighted and diffusion-weighted images. Machine learning algorithms including multiple logistic regression (MLR), supporting vector machine (SVM), random forest (RF), and artificial neural network (ANN) were applied to construct the predictive model. A nomogram was further built to visualize the possibility of RPS6K expression. The area under the receiver operating characteristic (AUC) was used to evaluate the performance of diagnostic models. 174 radiomics features were confirmed correlated with RPS6K expression. Amongst all built models, the ANN-based hybrid model exhibited best predictive ability with AUC of 0.887 and 0.826 in training and validation cohorts. ALB was identified as the key clinical index, and the nomogram displayed further improved ability with AUC of 0.917 and 0.845. In this study, we proved MRI-based radiomics model and nomogram can accurately predict RPS6K expression non-invasively, thus providing help for clinical decision making for mTORi therapy.

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography.

BACKGROUND: The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters. METHODS: Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk. FINDINGS: The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set. INTERPRETATION: This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies. FUNDING: This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Optimal torque distribution of distributed driving AGV under the condition of centroid change.

The torque distribution is researched under the condition of the centroid position of distributed drive automatic guided vehicle (AGV) with load platform and is uncertain due to the unknown movable load. The whole vehicle model under centroid variation, the efficiency model of the hub motor and the torque distribution control strategy based on a PID neural network are established. A hierarchical controller is designed to accurately ensure the economy and stability of the vehicle. Simulations of the proposed control strategy are conducted, the results show that the total power and lateral deviation distance of the driving wheels are reduced by 17.63% and 61.54% under low load conditions and 15.54% and 61.39% under high load conditions, respectively, compared with those of the driving wheels under the average torque distribution, and the goal of close slip rates of the driving wheels is achieved. A system prototype is developed and tested, and the experimental results agree with the simulation within error permissibility. The margin of error is less than 5.8%, the results demonstrate that the proposed control strategy is effective. This research can provide a theoretical and experimental basis for the torque optimization distribution of distributed drive AGVs under centroid variation conditions.

MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has poor prognosis. Cytokeratin (CK)19-positive (CK19+) HCC is especially aggressive; early identification of this subtype and timely intervention can potentially improve clinical outcomes. In the present study, we developed a preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI)-based radiomics model for noninvasive and accurate classification of CK19+ HCC. A multicenter and time-independent cohort of 257 patients were retrospectively enrolled (training cohort, n = 143; validation cohort A, n = 75; validation cohort B, n = 39). A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. Multiple logistic regression, support vector machine, random forest, and artificial neural network (ANN) algorithms were used to construct the radiomics model, and the area under the receiver operating characteristic (AUROC) curve was used to evaluate the diagnostic performance of corresponding classifiers. The incidence of CK19+ HCC was significantly higher in male patients. The ANN-derived combined classifier comprising 12 optimal radiomics features showed the best diagnostic performance, with AUROCs of 0.857, 0.726, and 0.790 in the training cohort and validation cohorts A and B, respectively. The combined model based on multisequence MRI radiomics features can be used for preoperative noninvasive and accurate classification of CK19+ HCC, so that personalized management strategies can be developed.

Integrating intratumoral and peritumoral features to predict tumor recurrence in intrahepatic cholangiocarcinoma.

Previous studies have suggested that the intratumoral texture features may reflect the tumor recurrence risk in intrahepatic cholangiocarcinoma (ICC). The peritumoral features may be associated with the distribution of microsatellites. Therefore, integrating the imaging features based on intratumoral and peritumoral areas may provide more accurate predictions in tumor recurrence (both early and late recurrences) than the predictions conducted based on the intratumoral area only. This retrospective study included 209 ICC patients. We divided the patient population into two sub-groups according to the order of diagnosis time: a training cohort (159 patients) and an independent validation cohort (50 patients). The MR imaging features were quantified based on the intratumoral and peritumoral (3 and 5 mm) areas. The radiomics signatures, clinical factor-based models and combined radiomics-clinical models were developed to predict the tumor recurrence. The prediction performance was measured based on the validation cohort using the area under receiver operating characteristic curve (AUC) index. For the prediction of early recurrence, the combined radiomics-clinical model of intratumoral area with 5 mm peritumoral area showed the highest performance (0.852(95% confidence interval (CI), 0.724-0.937)). The AUC for the clinical factor-based model was 0.805(95%CI, 0.668-0.903). For the prediction of late recurrence, the radiomics signature of intratumoral area with 5 mm peritumoral area had the optimal performance with an AUC of 0.735(95%CI, 0.591-0.850). The clinical factor-based showed inferior performance (0.598(95%CI, 0.450-0.735)). For both early and late recurrences prediction, the optimal models were all constructed using imaging features extracted based on intratumoral and peritumoral areas together. These suggested the importance of involving the intratumoral and peritumoral areas in the radiomics studies.

Deep neural network-based approach to improving radiomics analysis reproducibility in liver cancer: effect on image resampling.

Objectives.To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.Methods.CT images with ≤ 1 mm ST in the public dataset were converted to low-resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm ST and 5 mm ST to 1 mm ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.Results.CT images of 108 patients were used for training (n = 63), validation (n = 11) and testing (n = 34). The DNN method showed significantly higher PSNR and SSIM values (p < 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3-1 mm, and from 305(64%) to 353(74%) for the conversion of 5-1 mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.Conclusions.The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.

Radiomics analysis combining unsupervised learning and handcrafted features: A multiple-disease study.

PURPOSE: To study and investigate the synergistic benefit of incorporating both conventional handcrafted and learning-based features in disease identification across a wide range of clinical setups. METHODS AND MATERIALS: In this retrospective study, we collected 170, 150, 209, and 137 patients with four different disease types associated with identification objectives : Lymph node metastasis status of gastric cancer (GC), 5-year survival status of patients with high-grade osteosarcoma (HOS), early recurrence status of intrahepatic cholangiocarcinoma (ICC), and pathological grades of pancreatic neuroendocrine tumors (pNETs). Computed tomography (CT) and magnetic resonance imaging (MRI) were used to derive image features for GC/HOS/pNETs and ICC, respectively. In each study, 67 universal handcrafted features and study-specific features based on the sparse autoencoder (SAE) method were extracted and fed into the subsequent feature selection and learning model to predict the corresponding disease identification. Models using handcrafted alone, SAE alone, and hybrid features were optimized and their performance was compared. Prominent features were analyzed both qualitatively and quantitatively to generate study-specific and cross-study insight. In addition to direct performance gain assessment, correlation analysis was performed to assess the complementarity between handcrafted features and SAE features. RESULTS: On the independent hold-off test, the handcrafted, SAE, and hybrid features based prediction yielded area under the curve of 0.761 versus 0.769 versus 0.829 for GC, 0.629 versus 0.740 versus 0.709 for HOS, 0.717 versus 0.718 versus 0.758 for ICC, and 0.739 versus 0.715 versus 0.771 for pNETs studies, respectively. In three out of the four studies, prediction using the hybrid features yields the best performance, demonstrating the general benefit in using hybrid features. Prediction with SAE features alone had the best performance in the HOS study, which may be explained by the complexity of HOS prognosis and the possibility of a slight overfit due to higher correlation between handcrafted and SAE features. CONCLUSION: This study demonstrated the general benefit of combing handcrafted and learning-based features in radiomics modeling. It also clearly illustrates the task-specific and data-specific dependency on the performance gain and suggests that while the common methodology of feature combination may be applied across various studies and tasks, study-specific feature selection and model optimization are still necessary to achieve high accuracy and robustness.

Generalized methodology for radiomic feature selection and modeling in predicting clinical outcomes.

Background.Quantitative radiomic features of medical images could provide clinical significance in assisting decision-making, but the existing feature selection and modeling methods are usually parameter-dependent. We aim to develop and validate a generalized radiomic method applicable to a variety of clinical outcomes.Methods and materials.A generalized methodology for radiomic feature selection and modeling ('GRFM' for short), including two-step feature selection and logistic regression, was proposed for studying clinical outcomes correlations. The two-step feature selection consists of Pearson correlation analysis followed by a sequential forward floating selection algorithm to identify robust feature subsets. We also applied an adaptive searching strategy to systematically determine globally optimal parameters, rather than relying on preset parameters. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of three outcomes: lymph node metastasis of gastric cancer (GC), the five-year survival status of high-grade osteosarcoma (HOS), and the pathological grade of pancreatic neuroendocrine tumors (pNETs).Results.The optimal Pearson thresholds were 0.85, 0.80 and 0.75, and the optimal feature numbers were 11, 14 and 8 in GC, HOS and pNETs, respectively. The AUC values of the three predictive models combined with the corresponding parameters were 0.9017 versus 0.9026, 0.7652 versus 0.7113, and 0.8438 versus 0.8212 for the training and validation cohorts, showing promissing generality and classifier performance .Conclusion.The proposed method was helpful in predicting different clinical outcomes, and has potential application as a general and noninvasive prediction tool to guide clinical decision-making in various cancer sites.

Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images.

BACKGROUND: This study aimed to determine the impact of including radiomics analysis of non-tumorous bone region of interest in improving the performance of pathological response prediction to chemotherapy in high-grade osteosarcomas (HOS), compared to radiomics analysis of tumor region alone. METHODS: This retrospective study included 157 patients diagnosed with HOS between November 2013 and November 2017 (age range, 5-44 years; mean age, 16.99 ±7.42 years), in which 69 and 88 patients were diagnosed as pathological good response (pGR) and non-pGR, respectively. Radiomics features were extracted from tumor and non-tumorous bone regions based on diagnostic CT images. Pathological response classifiers were developed and validated via leave-one-out cross validation (LOOCV) and independent validation methods by using the area under the receiver operating characteristic curve (AUC) value as the figure of merit. RESULTS: Using the LOOCV, the classifiers combining features from tumor and non-tumorous regions showed better prediction performance than those from tumor region alone (AUC, 0.8207±0.0043 vs. 0.7799±0.0044). The combined classifier also showed better performance than the tumor feature-based classifier in both training and validation datasets [training dataset: 0.791, 95% confidence interval (CI), 0.706-0.860 vs. 0.766, 95% CI, 0.679-0.840; validation dataset: 0.816, 95% CI, 0.662-0.920 vs. 0.766, 95% CI, 0.606-0.885]. CONCLUSIONS: Radiomics analysis of combined tumor and non-tumorous bone features showed improved performance of pathological response prediction to chemotherapy in HOS compared to that of tumor features alone. Moreover, the proposed classifier had the potential to predict pathological response to chemotherapy for HOS patients.

Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.

BACKGROUND: Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non-small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone-beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. METHODS: A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression-free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. RESULTS: Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. CONCLUSIONS: Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. KEY POINTS: Significant findings of the study: A model with cone-beam CT radiomics features plus pre-treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. WHAT THIS STUDY ADDS: In the prediction of PFS and lung toxicity in early-stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.

Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.

OBJECTIVES: This work aims to study the variation, robustness, and feature redundancy of PET/MR radiomic features in the primary tumor of nasopharyngeal carcinoma (NPC). PROCEDURES: PET/MR scans of 21 NPC patients were used in this study. The primary tumor volumes were defined using PET, T2-weighted-MR (T2-MR), and diffusion-weighted MR (DW-MR) images. A random-dilation-erosion method was used to simulate 10 sets of tumor volumes for identifying features invariant with manual segmentation uncertainties. Feature robustness was evaluated against imaging modalities, pixel sizes, slice thickness, and grey-level bin sizes using intraclass correlation coefficient (ICC) and spearman correlation coefficient. Feature redundancy was analyzed using the hierarchical cluster analysis. RESULTS: Voxel size of 0.5 × 0.5 × 1.0 mm3 was found optimal for robust feature extraction from PET and MR. Normalized grey level of 64 and 128 was suggested for PET and MR, respectively. The features from wavelet-transformed images were less stable than those from the original images. The robustness analysis and volume correlation analysis identified 335 (62.04 %) PET features, 240 (44.44 %) T2-MR features, and 366 (67.78 %) DW-MR features. The cluster analysis grouped PET, T2-MR, and DW-MR features into 106, 83, and 133 representative features, respectively. CONCLUSIONS: The present study analyzed and identified robust features extracted from tumor volumes on PET/MR, which can provide guidance and promote standardization for PET/MR radiomic studies in NPC.

A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis.

The purpose of this study was to investigate the predictive performance of 2D and 3D image features across multi-organ cancers using multi-modality images in radiomics studies. In this retrospective study, we included 619 patients with three different cancer types (intrahepatic cholangiocarcinoma (ICC), high-grade osteosarcoma (HOS), pancreatic neuroendocrine tumors (pNETs)) and four clinical end points (early recurrence (ER), lymph node metastasis (LNM), 5-year survival and histologic grade). The image features included fifty-eight 2D image features and fifty-eight 3D image features. The 3D image features were extracted based on the 3D tumor volumes. The 2D image features were extracted based on 2D tumor region, which was the layer with the maximum tumor diameter within the 3D tumor volume. The predictive performance of individual 2D and 3D image feature was measured using the area under the receiver operating characteristic curve (AUC) with univariate analysis. Radiomics signatures were further developed using multivariable analysis with 4-fold cross-validation method. Using univariate analysis, we found that more 3D image features showed the statistically predictive capabilities than 2D image features across all the included cancer types. By comparing the predictive performance of radiomics signatures developed by 2D and 3D image features, we observed better prediction performance in radiomics signatures based on 3D image features than those based on 2D image features for patients with ICC and HGO. Meanwhile, the signatures based on 2D and 3D image features performed closely in the pNETs dataset with the clinical end point of the histologic grade. The reason for this inconsistent result might be that the gross tumor volumes of pNETs were generally small. The tumor heterogeneity was mostly presented in the middle several layers within the tumor volume. Both 2D and 3D image features have certain predictive capacities. By contrast, the 3D image features show better or close predictive performance than 2D image features using both univariate analysis and multivariate analysis. In brief, 3D image features are recommended in radiomics studies.

Effects of pyrroloquinoline quinone supplementation on growth performance and small intestine characteristics in weaned pigs.

This study was conducted to explore the effect of graded levels of pyrroloquinoline quinone disodium (PQQ·Na2) on the performance and intestinal development of weaned pigs. A total of 216 pigs weaned at 28 d were assigned in a randomized complete block design to 6 diets containing 0, 1.5, 3.0, 4.5, 6.0, or 7.5 mg/kg PQQ·Na2 for 28 d. Performance, diarrhea incidence, intestinal morphology, redox status, cytokines, and the expression of tight junction proteins were determined. Pigs had increased ADG (linear, P < 0.01), G:F (quadratic, P < 0.01), and lower diarrhea incidence (P < 0.01) with the increase of PQQ·Na2 supplementation. Villus height increased (quadratic, P < 0.01) in all segments of the small intestine, and crypt depth in the duodenum and jejunum was decreased (linear, P < 0.05) in pigs with the increase of PQQ·Na2 supplementation. Pigs fed PQQ·Na2-supplemented diets had higher (P < 0.05) activities of antioxidant enzymes including total superoxide dismutase in duodenum, jejunum, and ileum; glutathione peroxidase (GSH-Px) in jejunum and ileum; catalase (CAT) in duodenum and ileum; and lower (P < 0.05) malondialdehyde concentrations in the intestinal mucosa of all segments. In the intestinal mucosa, cytokines including interleukin (IL)-1ß, IL-2, and interferon-γ were significantly decreased (P < 0.05) in pigs fed PQQ·Na2-supplemented diets. The protein expression of zonula occluden protein-1 (ZO-1) and occludin in the jejunum was significantly increased (P < 0.05) in pigs fed diets containing PQQ·Na2. In conclusion, these results have indicated that dietary PQQ·Na2 supplementation improves growth performance and gut health in weaned pigs. Moreover, pigs fed diet with as low as 1.5-mg/kg PQQ·Na2 have better performance compared with pigs fed no PQQ·Na2-supplemented diet; pigs fed diet with 4.5-mg/kg PQQ·Na2 have highest G:F among treatments during the whole period.