Access to 2,4-Disubstituted Pyrrole-Based Polymer with Long-Wavelength and Stimuli-Responsive Properties via Copper-Catalyzed [3+2] Polycycloaddition.

Pyrrole-based polymers (PBPs), a type of fascinating functional polymers, play a crucial role in materials science. However, efficient synthetic strategies of PBPs with diverse structures are mainly focused on conjugated polypyrroles and still remain challenging. Herein, an atom and step economy protocol is described to access various 2,4-disubstituted PBPs by in situ formation of pyrrole core structure via copper-catalyzed [3+2] polycycloaddition of dialkynones and diisocyanoacetates. A series of PBPs is prepared with high molecular weight (Mw up to 18 200 Da) and moderate to good yield (up to 87%), which possesses a fluorescent emission located in the green to yellow light region. Blending the PBPs with polyvinyl alcohol, the stretchable composite films exhibit a significant strengthening of the mechanical properties (tensile stress up to 59 MPa, elongation at break >400%) and an unprecedented stress-responsive luminescence enhancement that over fourfold fluorescent emission intensity is maintained upon stretching up to 100%. On the basis of computational studies, the unique photophysical and mechanical properties are attributed to the substitution of carbonyl chromophores on the pyrrole unit.

Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma.

OBJECTIVES: To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS: A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI between 2016 and 2022 were enrolled in the training and validation group (53 patients and 25 patients, respectively). Images including qualitative, quantitative MRI features and clinical data were evaluated. Univariate analysis and multivariate logistic regression were used to select the independent predictors and establish different predictive models. The predictive performance was validated by operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). The validation cohort was used to test the predictive performance of the optimal model. The nomogram was constructed with the optimal model. RESULTS: In the training cohort, independent predictors obtained from the combined model were DWI (OR 1822.741; 95% CI 6.189, 536,781.805; P = 0.01) and HBP enhancement pattern (OR 14.270; 95% CI 1.044, 195.039; P = 0.046). The combined model showed the good performance (AUC 0.981; 95% CI 0.952, 1.000) for predicting Ki-67 expression. In the validation cohort, The combined model (AUC 0.909; 95% CI 0.787, 1.000)showed the best performance compared to the clinical model (AUC 0.448; 95% CI 0.196, 0.700) and MRI model (AUC 0.770; 95% CI 0.570, 0.970). CONCLUSION: This new nomogram has a good performance in predicting Ki-67 expression in patients with IMCC, which could help the decision-making of the patients' therapy strategies.

Machine learning based on gadoxetic acid-enhanced MRI for differentiating atypical intrahepatic mass-forming cholangiocarcinoma from poorly differentiated hepatocellular carcinoma.

PURPOSE: The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for differentiating atypical intrahepatic mass-forming cholangiocarcinoma (aIMCC) from poorly differentiated hepatocellular carcinoma (pHCC). MATERIALS AND METHODS: A total of 134 patients (51 aIMCC and 83 pHCC) who underwent Gadoxetic acid-enhanced MRI between March 2016 and March 2022 were enrolled in this study and then randomly assigned to the training and validation cohorts by 7:3 (93 patients and 41 patients, respectively). The radiomics features were extracted from the hepatobiliary phase of Gadoxetic acid-enhanced MRI. In the training cohort, the SelectKBest and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features. The clinical, radiomics, and clinical-radiomics model were established using four machine learning algorithms. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve. Comparison of the radiomics and clinical-radiomics model was done by the Delong test. The clinical usefulness of the model was evaluated using decision curve analysis (DCA). RESULTS: In 1132 extracted radiomic features, 15 were selected to develop radiomics signature. For identifying aIMCC and pHCC, the radiomics model constructed by random forest algorithm showed the high performance (AUC = 0.90) in the training cohort. The performance of the clinical-radiomics model (AUC = 0.89) was not significantly different (P = 0.88) from that of the radiomics model constructed by random forest algorithm (AUC = 0.86) in the validation cohort. DCA demonstrated that the clinical-radiomics model constructed by random forest algorithm had a high net clinical benefit. CONCLUSION: The clinical-radiomics model is an effective tool to distinguish aIMCC from pHCC and may provide additional value for the development of treatment plans.

Renewable high-density spiro-fuels from lignocellulose-derived cyclic ketones.

Renewable high-density spiro-fuels are synthesized from lignocellulose-derived cyclic ketones for the first time, which show higher density, higher neat heat of combustion and lower freezing point compared with other biofuels synthesized from the same feedstock, and thus represent a new type of renewable high-density fuel attractive for practical applications.