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A highly stable fluorescent terbium MOF (Tb4(paip)6·1.2H2O, paip = 5-(1H-pyrazole-4-yl)isophthalate) showing a sharp green emission (545 nm) and a quantum yield of 21.0% was successfully synthesized. This compound is shown to be a recyclable sensor for detecting picric acid in aqueous solution with both high sensitivity and selectivity, attributed to the electron transfer quenching mechanism.

BACKGROUND: This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. METHODS: A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20–80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose(4) levels 1–7 and iterative model reconstruction (IMR) levels 1–3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose(4) level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. RESULTS: For the phantom study, the iDose(4) and IMR groups had significantly higher SNR and CNR than the FBP group (all P0.05), while AS from the IMR (level 1–3) image series were lower than the FBP group (all P

BACKGROUND High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. PURPOSE To develop a radiomics nomogram for high-risk EC prediction preoperatively. STUDY TYPE Retrospective. POPULATION In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). FIELD STRENGTH/SEQUENCE 1.5/3T scanners; T2 -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. ASSESSMENT A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). STATISTICAL TESTS Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. RESULTS The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. DATA CONCLUSION The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.

To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively. During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27–85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B–E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model. The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone. The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC. • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.

Background: High–risk and low–risk endometrial cancer (EC) differ in the extent of the surgical procedure. Assessment of the risk stratification of EC is essential for planning the appropriate surgery. The aim of this study was to develop an radiomics nomogram for preoperative prediction of high–risk EC. Methods: A total of 718 histopathologically confirmed EC patients from five centers were retrospectively divided into a primary cohort (n = 285, Center A), an internal validation cohort (n = 109, Center A), and an external validation cohort (n = 324, Centers B to E). A binary least absolute shrinkage and selection operator logistic regression analysis was used to select radiomics features from MRIs and multivariate logistic regression analysis was used to select clinical features for high–risk EC. A radiomics nomogram was generated by combining the selected radiomics features (radiomics signatures) with the selected clinical features (clinical signatures). A receiver operating characteristic curve and a decision curve were generated to evaluate the predictive performance and clinical usefulness of radiomics signatures, clinical signatures, and radiomics nomogram. The radiomics nomogram was further used to assess deep myometrial invasion (DMI) and other risk factors of EC. Findings: Of the 358 extracted radiomics features, 17 were ultimately selected from MRIs. The clinical signatures consisted of metabolic syndrome, cancer antigen 125, age, tumor size, and tumor grade following curettage. The radiomics nomogram had highest areas under the curve and achieved the highest clinical net benefit for the decision curve. The radiomics nomogram assessed DMI and other risk factors of EC with high clinical benefit. Interpretation: The radiomics nomogram exhibited good performance in the individual prediction of high–risk EC with good clinical net benefit and good application in assessing DMI and other risk factors of EC. Funding Statement: National Natural Science Foundation of China (No. 81971579). Declaration of Interests: The authors stated: "There are no conflicts of interest." Ethics Approval Statement: This study was approved by the Obstetrics and Gynecology Hospital of Fudan University Institutional Review Board, and informed consent was waived.

Objectives. This study is undertaken to investigate the relation between osteopontin (OPN) and Wnt5a expression in the progression and pathogenesis of osteoarthritis (OA).Methods. 50 cartilage tissues from knee OA patients and normal controls were divided into four groups of severe, moderate, minor, and normal lesions based on the modified grading system of Mankin. Immunohistochemistry and real-time PCR were utilized to analyze the OPN and Wnt5a expression in articular cartilage. Besides, the relations between OPN and Wnt5a expression and the severity of OA were explored.Results. OPN and Wnt5a could be identified in four groups’ tissues. Amongst the groups, the intercomparisons of OPN expression levels showed statistical differences (P<0.01). Besides, the intercomparisons of Wnt5a expression degrees showed statistical differences (P<0.05), except that between the minor and normal groups (P>0.05). The scores of Mankin were demonstrated to relate to OPN expression (r=-0.847,P<0.01) and Wnt5a expression in every group (r=-0.843,P<0.01). Also, a positive correlation can be observed between the OPN and Wnt5a expression (r=0.769,P<0.01).Conclusion. In articular cartilage, the expressions of OPN and Wnt5a are positively related to progressive damage of knee OA joint. The correlation between Wnt5a and OPN might be important to the progression and pathogenesis of knee OA.

A novel unmediated hydrogen peroxidase biosensor was developed by immobilizing horseradish peroxidase (HRP) into a tin oxide sol‐gel film at a glass carbon electrode surface. Morphology of the sol‐gel film and HRP entrapped in the sol‐gel film were characterized by transmission electron microscopy (TEM) and Fourier‐transform infrared (FTIR) spectroscopy, respectively, showing the uniform porous network structure in the presence of nano‐SnO2 particles and the retention of the enzymatic bioactivity with its native secondary structure. The direct electrochemistry of HRP incorporated in the film was investigated using cyclic voltammetry. The biosensor exhibited good stability and electrocatalytic activity toward the reduction of hydrogen peroxide. The linear concentration range of the biosensor was from 0.01 mM to 0.25 mM. The apparent Michaelis‐Menten constant K M app was estimated to be 0.166 mM.