More than 100 amphiphilic phosphoesters, possible tetrahedral transition-state analogues capable of coordinating to the calcium ion at the active site of phospholipase A2, were designed, synthesized, and tested as inhibitors for the hydrolysis of 1,2-dimyristoyl-sn-glycero-3-phosphomethanol vesicles in the scooting mode. This assay system permits the study of structurally diverse inhibitors with phospholipase A2S from different sources, and it is not perturbed by factors that change the quality of the interface. As a prototype, 1-hexadecyl-3-trifluoroethylglycero-2-phosphomethanol (MJ33) was investigated in detail. Only the (S)-(+) analogue of MJ33 is inhibitory, and it is as effective as the sn-2 phosphonate or the sn-2 amide analogues of sn-3 phospholipids. The inhibitory potencies of the various phosphoesters depended strongly on the stereochemical and structural features, and the mole fractions of inhibitors required for 50% inhibition, X1(50), ranged from more than 1 to less than 0.001 mole fraction. The affinity of certain inhibitors for enzymes from different sources differed by more than 200-fold. The inhibitors protected the catalytic site residue His-48 from alkylation in the presence of calcium but not barium as expected if the formation of the EI complex is supported only by calcium. The equilibrium dissociation constant for the inhibitor bound to the enzyme at the interface was correlated with the XI(50) values, which were different if the inhibition was monitored in the pseudo-zero-order or the first-order region of the progress curve. These results show that the inhibitors described here interfered only with the catalytic turnover by phospholipase A2's bound to the interface, their binding to the enzyme occurred through calcium, and the inhibitors did not have any effect on the dissociation of the enzyme bound to the interface.

PurposeMachine learning (ML) can extract high-throughput features of images to predict disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model to predict the risk of breast cancer.MethodsThe mpMRI included non-enhanced and enhanced T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, and Vp. Regions of interest were annotated in an enhanced T1WI map and mapped to other maps in every slice. 1,132 features and top-10 principal components were extracted from every parameter map. Single-parametric and multi-parametric ML models were constructed via 10 rounds of five-fold cross-validation. The model with the highest area under the curve (AUC) was considered as the optimal model and validated by calibration curve and decision curve. Nomogram was built with the optimal ML model and patients’ characteristics.ResultsThis study involved 144 malignant lesions and 66 benign lesions. The average age of patients with benign and malignant lesions was 42.5 years old and 50.8 years old, respectively, which were statistically different. The sixth and fourth principal components of Ktrans had more importance than others. The AUCs of Ktrans, Kep, Ve and Vp, non-enhanced T1WI, enhanced T1WI, T2WI, and ADC models were 0.86, 0.81, 0.81, 0.83, 0.79, 0.81, 0.84, and 0.83 respectively. The model with an AUC of 0.90 was considered as the optimal model which was validated by calibration curve and decision curve. Nomogram for the prediction of breast cancer was built with the optimal ML models and patient age.ConclusionNomogram could improve the ability of breast cancer prediction preoperatively.