MOPDDI: Predicting Drug-Drug Interaction Events Based on Multimodal Mutual Orthogonal Projection and Intermodal Consistency Loss.

Predicting accurately the mechanisms of drug-drug interaction (DDI) events is crucial in drug research and development. Existing methods used to predict these events are primarily based on deep learning and have achieved satisfactory results. However, they rarely consider the presence of redundant co-information between the multimodal data of a drug and the need for consistency in the predicted features of each drug modality. Herein, we propose a new method for drug interaction event prediction based on multimodal mutual orthogonal projection and intermodal consistency loss. Our method obtains the features of each modality through a multimodal mutual orthogonal projection module, which eliminates redundant common information with other modalities. In addition, we use the consistency loss between modalities and make the predicted features of each modality more similar. In comparative experiments, our proposed method achieves a prediction accuracy of 0.9500, and an area under the precision-recall (AUPR) curve is 0.9833 for known DDIs. This method outperforms existing methods. The results show that the proposed method is capable of accurately predicting DDIs. The source code is available at https://github.com/xiaqixiaqi/MOPDDI.

Prediction of OCT images of short-term response to anti-VEGF treatment for diabetic macular edema using different generative adversarial networks.

PURPOSE: This study sought to assess the predictive performance of optical coherence tomography (OCT) images for the response of diabetic macular edema (DME) patients to anti-vascular endothelial growth factor (VEGF) therapy generated from baseline images using generative adversarial networks (GANs). METHODS: Patient information, including clinical and imaging data, was obtained from inpatients at the Ophthalmology Department of Qilu Hospital. 715 and 103 pairs of pre-and post-treatment OCT images of DME patients were included in the training and validation sets, respectively. The post-treatment OCT images were used to assess the validity of the generated images. Six different GAN models (CycleGAN, PairGAN, Pix2pixHD, RegGAN, SPADE, UNIT) were applied to predict the efficacy of anti-VEGF treatment by generating OCT images. Independent screening and evaluation experiments were conducted to validate the quality and comparability of images generated by different GAN models. RESULTS: OCT images generated f GAN models exhibited high comparability to the real images, especially for edema absorption. RegGAN exhibited the highest prediction accuracy over the CycleGAN, PairGAN, Pix2pixHD, SPADE, and UNIT models. Further analyses were conducted based on the RegGAN. Most post-therapeutic OCT images (95/103) were difficult to differentiate from the real OCT images by retinal specialists. A mean absolute error of 26.74 ± 21.28 µm was observed for central macular thickness (CMT) between the synthetic and real OCT images. CONCLUSION: Different generative adversarial networks have different prognostic efficacy for DME, and RegGAN yielded the best performance in our study. Different GAN models yielded good accuracy in predicting the OCT-based response to anti-VEGF treatment at one month. Overall, the application of GAN models can assist clinicians in prognosis prediction of patients with DME to design better treatment strategies and follow-up schedules.

Enhanced Electrocatalytic Activity of Murdochite-Type Ni6MnO8 for Water Oxidation via Surface Reconstruction.

Ni-Mn-based binary oxides are deemed as prospective electrocatalysts for water oxidation. Here, a murdochite-type Ni6MnO8 electrocatalyst for water oxidation is reported. Murdochite-type Ni6MnO8 with hollow sphere (NMO-HS) and microflower (NMO-MF) structures has been controllably synthesized. After an in-situ activation process, the NMO-MF affords a superior activity for oxygen evolution reaction in 0.1 M KOH. A low overpotential of 370 mV at 10 mA cm-2 is obtained, and the mass activity of activated NMO-MF is 1.96 times that of commercial IrO2/C. As revealed by in-situ Raman spectra, Ni species in activated NMO-MF act as intrinsic active sites, and the in-situ formed NiOOH on the surface during the activation process is identified to contribute to the significantly enhanced catalytic activity. The Zn-air battery assembled with a NMO-MF cathode showed an exceptional power density (0.228 W cm-2) and long-term cycling stability (148 h).