RESUMO
We aimed to investigate the efficacy of an objective method using AI-based retinal characteristic analysis to automatically differentiate between two traditional Chinese syndromes that are associated with ischemic stroke. Inpatient clinical and retinal data were retrospectively retrieved from the archive of our hospital. Patients diagnosed with cerebral infarction in the department of acupuncture and moxibustion between 2014 and 2018 were examined. Of these, the patients with Qi deficiency blood stasis syndrome (QDBS) and phlegm stasis in channels (PSIC) syndrome were selected. Those without retinal photos were excluded. To measure and analyze the patients' retinal vessel characteristics, we applied a patented AI-assisted automated retinal image analysis system developed by the Chinese University of Hong Kong. The demographic, clinical, and retinal information was compared between the QDBS and PSIC patients. The t-test and chi-squared test were used to analyze continuous data and categorical data, respectively. All the selected clinical information and retinal vessel measures were used to develop different discriminative models for QDBS and PSIC using logistic regression. Discriminative efficacy and model performances were evaluated by plotting a receiver operating characteristic curve. As compared to QDBS, the PSIC patients had a lower incidence of insomnia problems (46% versus 29% respectively, p=0.023) and a higher tortuosity index (0.45 ± 0.07 versus 0.47 ± 0.07, p=0.027). Moreover, the area under the curve of the logistic model showed that its discriminative efficacy based on both retinal and clinical characteristics was 86.7%, which was better than the model that employed retinal or clinical characteristics individually. Thus, the discriminative model using AI-assisted retinal characteristic analysis showed statistically significantly better performance in QDBS and PSIC syndrome differentiation among stroke patients. Therefore, we concluded that retinal characteristics added value to the clinical differentiation between QDBS and PSIC.
RESUMO
OBJECTIVE: Dysfunction of vascular endothelial cells was associated with diverse human diseases, including cardiovascular disease. Long noncoding RNAs (LncRNAs) were involved in the regulation of cell injury. We aimed to investigate the role of lncRNA testis-specific transcript, Y-linked 15 (TTTY15) in hypoxia-induced cell injury in human umbilical vein endothelial cells (HUVECs). MATERIALS AND METHODS: Cell counting kit-8 (CCK8) assay was used to check cell viability. Lactate dehydrogenase (LDH) assay kit and flow cytometry were utilized to evaluate the leakage rate of LDH and cell apoptosis, respectively. The protein levels of Cyclin D1 and B-cell lymphoma-2-Associated X (Bax) in hypoxia-induced HUVECs were measured by Western blot. Quantitative Real-time polymerase chain reaction (qRT-PCR) was conducted to detect the expression levels of TTTY15 and miR-186-5p in hypoxia-induced HUVECs. The starBase was utilized to predict the binding sites between TTTY15 and miR-186-5p and the dual-luciferase reporter assay was performed to verify the interaction. RESULTS: Hypoxia inhibited cell viability and promoted the release of LDH and cell apoptosis in HUVECs. Besides, hypoxia significantly decreased the protein level of Cyclin D1 and increased the protein level of Bax in HUVECs. In addition, the expression level of TTTY15 was obviously upregulated in hypoxia-induced HUVECs, opposite to the level of miR-186-5p. Meanwhile, knockdown of TTTY15 or upregulation of miR-186-5p mitigated hypoxia-induced cell injury in HUVECs. Further studies suggested that TTTY15 targeted miR-186-5p and regulated hypoxia-induced cell injury via interacting with miR-186-5p in HUVECs. CONCLUSIONS: Downregulation of TTTY15 ameliorated hypoxia-induced cell injury by targeting miR-186-5p in HUVECs.
