Two 3-D metal organic frameworks containing 2,2′-bipyridine-5,5′-dicarboxylic acid: synthesis, structure, and magnetic properties

<div><p>Two metal organic frameworks, {[M(H₂BPDC)(SO₄)]}_n (M = Mn (<b>1</b>), Zn (<b>2</b>)); BPDC = 2,2′-bipyridine-5,5′-dicarboxylic anion), have been synthesized under hydrothermal conditions. The structure analyses of <b>1</b> and <b>2</b> reveal that the two compounds have similar 3-D structures. Compound <b>1</b> crystallizes in the orthorhombic system with space group <i>Pnma</i>, while <b>2</b> displays a monoclinic system with space group <i>P21/n</i>. Magnetic investigation suggests that weak antiferromagnetic coupling exists between adjacent Mn²⁺ ions in <b>1</b>.</p></div

Chelating-Template-Assisted <i>in Situ</i> Encapsulation of Zinc Ferrite Inside Silica Mesopores for Enhanced Gas-Sensing Characteristics

A facile <i>in situ</i> approach has been designed to synthesize zinc ferrite/mesoporous silica guest–host composites. Chelating surfactant, <i>N</i>-hexadecyl ethylenediamine triacetic acid, was employed as structure-directing agent to fabricate mesoporous silica skeleton and simultaneously as complexing agent to incorporate stoichiometric amounts of zinc and iron ions into silica cavities. On this basis, spinel zinc ferrite nanoparticles with grain sizes less than 3 nm were encapsulated in mesoporous channels after calcination. The silica mesostructure, meanwhile, displayed a successive transformation from hexagonal <i>p</i>6<i>mm</i> through bicontinuous cubic <i>Ia</i>3̅<i>d</i> to lamellar phase with increasing the dopant concentration in the initial template solution. In comparison with zinc ferrite nanopowder prepared without silica host, the composite with bicontinuous architecture exhibited higher sensitivity, lower detection limit, lower optimum working temperature, quicker response, and shorter recovery time in sensing performance toward hydrogen sulfide. The significant improvements are from the high surface-to-volume ratio of the guest oxides and the three-dimensional porous structure of the composite. We believe the encapsulation route presented here may pave the way for directly introducing complex metal oxide into mesoporous silica matrix with tailorable mesophases for applications in sensing or other fields

Image_3_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_2_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_1_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p