BackgroundNative T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated.PurposeTo examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods.Study TypeRetrospective.Population128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17).Field Strength/Sequence3.0T; Balanced steady‐state free precession, phase‐sensitive inversion recovery (PSIR) and multislice native T1 mapping.AssessmentCompare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL‐myo), myocardial ring bounding box (DL‐box) and the surrounding tissue without myocardial ring (DL‐nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve.Statistical TestsAccuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann–Whitney U‐test and Chi‐square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant.ResultsDL‐myo, DL‐box, and DL‐nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702–0.959), 0.766 (0.617–0.915), 0.795 (0.654–0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352–0.738) and 0.800 (0.655–0.944) in the testing set.Data ConclusionThe DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode.Level of Evidence4Technical Efficacy Stage2

This a preprint and has not been peer reviewed. Data may be preliminary.

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Retinal pigment epithelium (RPE) serves critical functions in maintaining retinal homeostasis. An important function of RPE is to degrade the photoreceptor outer segment fragments daily to maintain photoreceptor function and longevity throughout life. An impairment of RPE functions such as metabolic regulation leads to the development of age-related macular degeneration (AMD) and inherited retinal degenerative diseases. As substrate recognition subunit of a ubiquitin ligase complex, suppressor of cytokine signaling 2 (SOCS2) specifically binds to the substrates for ubiquitination and negatively regulates growth hormone signaling. Herein, we explore the role of SOCS2 in the metabolic regulation of autophagy in the RPE cells. SOCS2 knockout mice exhibited the irregular morphological deposits between the RPE and Bruch’s membrane. Both in vivo and in vitro experiments showed that RPE cells lacking SOCS2 displayed impaired autophagy, which could be recovered by re-expressing SOCS2. SOCS2 recognizes the ubiquitylated proteins and participates in the formation of autolysosome by binding with autophagy receptors and lysosome-associated membrane protein2 (LAMP-2), thereby regulating the phosphorylation of glycogen synthase kinase 3β (GSK3β) and mammalian target of rapamycin (mTOR) during the autophagy process. Our results imply that SOCS2 participates in ubiquitin-autophagy-lysosomal pathway and enhances autophagy by regulating GSK3β and mTOR. This study provides a potential therapeutic target for AMD.