MP-Net: A Multi-Center Privacy-Preserving Network for Medical Image Segmentation.

In this paper, we present the Multi-Center Privacy-Preserving Network (MP-Net), a novel framework designed for secure medical image segmentation in multi-center collaborations. Our methodology offers a new approach to multi-center collaborative learning, capable of reducing the volume of data transmission and enhancing data privacy protection. Unlike federated learning, which requires the transmission of model data between the central server and local servers in each round, our method only necessitates a single transfer of encrypted data. The proposed MP-Net comprises a three-layer model, consisting of encryption, segmentation, and decryption networks. We encrypt the image data into ciphertext using an encryption network and introduce an improved U-Net for image ciphertext segmentation. Finally, the segmentation mask is obtained through a decryption network. This architecture enables ciphertext-based image segmentation through computable image encryption. We evaluate the effectiveness of our approach on three datasets, including two cardiac MRI datasets and a CTPA dataset. Our results demonstrate that the MP-Net can securely utilize data from multiple centers to establish a more robust and information-rich segmentation model.

Discovery of a Novel Benzothiadiazine-Based Selective Aldose Reductase Inhibitor as Potential Therapy for Diabetic Peripheral Neuropathy.

Aldose reductase 2 (ALR2), an activated enzyme in the polyol pathway by hyperglycemia, has long been recognized as one of the most promising targets for complications of diabetes, especially in diabetic peripheral neuropathy (DPN). However, many of the ALR2 inhibitors have shown serious side effects due to poor selectivity over aldehyde reductase (ALR1). Herein, we describe the discovery of a series of benzothiadiazine acetic acid derivatives as potent and selective inhibitors against ALR2 and evaluation of their anti-DPN activities in vivo. Compound 15c, carrying a carbonyl group at the 3-position of the thiadiazine ring, showed high potent inhibition against ALR2 (IC50 = 33.19 nmol/L) and ∼16,109-fold selectivity for ALR2 over ALR1. Cytotoxicity assays ensured the primary biosafety of 15c. Further pharmacokinetic assay in rats indicated that 15c had a good pharmacokinetic feature (t1/2 = 5.60 h, area under the plasma concentration time curve [AUC(0-t)] = 598.57 ± 216.5 µg/mL * h), which was superior to epalrestat (t1/2 = 2.23 h, AUC[0-t] = 20.43 ± 3.7 µg/mL * h). Finally, in a streptozotocin-induced diabetic rat model, 15c significantly increased the nerve conduction velocities of impaired sensory and motor nerves, achieved potent inhibition of d-sorbitol production in the sciatic nerves, and significantly increased the paw withdrawal mechanical threshold. By combining the above investigations, we propose that 15c might represent a promising lead compound for the discovery of an antidiabetic peripheral neuropathy drug.

Machine Learning Based on Computed Tomography Pulmonary Angiography in Evaluating Pulmonary Artery Pressure in Patients with Pulmonary Hypertension.

BACKGROUND: Right heart catheterization is the gold standard for evaluating hemodynamic parameters of pulmonary circulation, especially pulmonary artery pressure (PAP) for diagnosis of pulmonary hypertension (PH). However, the invasive and costly nature of RHC limits its widespread application in daily practice. PURPOSE: To develop a fully automatic framework for PAP assessment via machine learning based on computed tomography pulmonary angiography (CTPA). MATERIALS AND METHODS: A machine learning model was developed to automatically extract morphological features of pulmonary artery and the heart on CTPA cases collected between June 2017 and July 2021 based on a single center experience. Patients with PH received CTPA and RHC examinations within 1 week. The eight substructures of pulmonary artery and heart were automatically segmented through our proposed segmentation framework. Eighty percent of patients were used for the training data set and twenty percent for the independent testing data set. PAP parameters, including mPAP, sPAP, dPAP, and TPR, were defined as ground-truth. A regression model was built to predict PAP parameters and a classification model to separate patients through mPAP and sPAP with cut-off values of 40 mm Hg and 55 mm Hg in PH patients, respectively. The performances of the regression model and the classification model were evaluated by analyzing the intraclass correlation coefficient (ICC) and the area under the receiver operating characteristic curve (AUC). RESULTS: Study participants included 55 patients with PH (men 13; age 47.75 ± 14.87 years). The average dice score for segmentation increased from 87.3% ± 2.9 to 88.2% ± 2.9 through proposed segmentation framework. After features extraction, some of the AI automatic extractions (AAd, RVd, LAd, and RPAd) achieved good consistency with the manual measurements. The differences between them were not statistically significant (t = 1.222, p = 0.227; t = -0.347, p = 0.730; t = 0.484, p = 0.630; t = -0.320, p = 0.750, respectively). The Spearman test was used to find key features which are highly correlated with PAP parameters. Correlations between pulmonary artery pressure and CTPA features show a high correlation between mPAP and LAd, LVd, LAa (r = 0.333, p = 0.012; r = -0.400, p = 0.002; r = -0.208, p = 0.123; r = -0.470, p = 0.000; respectively). The ICC between the output of the regression model and the ground-truth from RHC of mPAP, sPAP, and dPAP were 0.934, 0.903, and 0.981, respectively. The AUC of the receiver operating characteristic curve of the classification model of mPAP and sPAP were 0.911 and 0.833. CONCLUSIONS: The proposed machine learning framework on CTPA enables accurate segmentation of pulmonary artery and heart and automatic assessment of the PAP parameters and has the ability to accurately distinguish different PH patients with mPAP and sPAP. Results of this study may provide additional risk stratification indicators in the future with non-invasive CTPA data.