20(S)-Protopanaxatriol (Ppt) Exhibits Inhibition Towards UDPGlucuronosyltransferase (UGT)-Catalyzed Zidovudine Glucuronidation

Drug-drug interaction (DDI) is a challenging problem for treatment of HIV-infected patients. Zidovudine (AZT), prescribed under the names Retrovir and Retrovis, is the first U.S. government-approved antiretroviral drug used for the successful treatment of HIV/AIDS infectiousness. Given that ginseng is frequently utilized in combination with AZT and AZT is mainly eliminated by UDP-glucuronosyltransferase 2B7, the aim of present study is to investigate the inhibition of UGT2B7-catalyzed AZT glucuronidation by 20(S)-protopanaxatriol type (Ppt) which is the main ginsenoside absorbed into the plasma. The results showed that ppt competitively inhibited UGT2B7-catalyzed AZT glucuronidation, and the inhibition kinetic parameter (Ki ) was determined to be 24.7 μM. Using the maximum plasma concentration of ppt (Cmax ), the alteration of area under the curve (AUC) of AZT was 6 %. All these results provide important information for understanding ginseng-AZT interaction. However, considering the complication of herbs and individuals, the in vitro-in vivo extrapolation (IV-IVE) results should be explained with more caution.Colegio de Farmacéuticos de la Provincia de Buenos Aire

20(S)-Protopanaxatriol (Ppt) Exhibits Inhibition Towards UDPGlucuronosyltransferase (UGT)-Catalyzed Zidovudine Glucuronidation

Drug-drug interaction (DDI) is a challenging problem for treatment of HIV-infected patients. Zidovudine (AZT), prescribed under the names Retrovir and Retrovis, is the first U.S. government-approved antiretroviral drug used for the successful treatment of HIV/AIDS infectiousness. Given that ginseng is frequently utilized in combination with AZT and AZT is mainly eliminated by UDP-glucuronosyltransferase 2B7, the aim of present study is to investigate the inhibition of UGT2B7-catalyzed AZT glucuronidation by 20(S)-protopanaxatriol type (Ppt) which is the main ginsenoside absorbed into the plasma. The results showed that ppt competitively inhibited UGT2B7-catalyzed AZT glucuronidation, and the inhibition kinetic parameter (Ki ) was determined to be 24.7 μM. Using the maximum plasma concentration of ppt (Cmax ), the alteration of area under the curve (AUC) of AZT was 6 %. All these results provide important information for understanding ginseng-AZT interaction. However, considering the complication of herbs and individuals, the in vitro-in vivo extrapolation (IV-IVE) results should be explained with more caution.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Some stability properties of T. Chan’s preconditioner

AbstractA matrix is said to be stable if the real parts of all the eigenvalues are negative. In this paper, for any matrix An, we give some sufficient and necessary conditions for the stability of T. Chan’s preconditioner cU(An)

Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of permanent blindness worldwide. Designing an automatic grading system with good generalization ability for DR and DME is vital in clinical practice. However, prior works either grade DR or DME independently, without considering internal correlations between them, or grade them jointly by shared feature representation, yet ignoring potential generalization issues caused by difficult samples and data bias. Aiming to address these problems, we propose a framework for joint grading with the dynamic difficulty-aware weighted loss (DAW) and the dual-stream disentangled learning architecture (DETACH). Inspired by curriculum learning, DAW learns from simple samples to difficult samples dynamically via measuring difficulty adaptively. DETACH separates features of grading tasks to avoid potential emphasis on the bias. With the addition of DAW and DETACH, the model learns robust disentangled feature representations to explore internal correlations between DR and DME and achieve better grading performance. Experiments on three benchmarks show the effectiveness and robustness of our framework under both the intra-dataset and cross-dataset tests.Comment: Accepted by MICCAI2

Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains

Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide. Early and accurate grading of its severity is crucial for disease management. Although deep learning has shown great potential for automated DR grading, its real-world deployment is still challenging due to distribution shifts among source and target domains, known as the domain generalization problem. Existing works have mainly attributed the performance degradation to limited domain shifts caused by simple visual discrepancies, which cannot handle complex real-world scenarios. Instead, we present preliminary evidence suggesting the existence of three-fold generalization issues: visual and degradation style shifts, diagnostic pattern diversity, and data imbalance. To tackle these issues, we propose a novel unified framework named Generalizable Diabetic Retinopathy Grading Network (GDRNet). GDRNet consists of three vital components: fundus visual-artifact augmentation (FundusAug), dynamic hybrid-supervised loss (DahLoss), and domain-class-aware re-balancing (DCR). FundusAug generates realistic augmented images via visual transformation and image degradation, while DahLoss jointly leverages pixel-level consistency and image-level semantics to capture the diverse diagnostic patterns and build generalizable feature representations. Moreover, DCR mitigates the data imbalance from a domain-class view and avoids undesired over-emphasis on rare domain-class pairs. Finally, we design a publicly available benchmark for fair evaluations. Extensive comparison experiments against advanced methods and exhaustive ablation studies demonstrate the effectiveness and generalization ability of GDRNet.Comment: Earyly Accepted by MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer Assisted Interventio