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Purpose: The influence of myopia and ocular biometry parameters on diabetic retinopathy (DR) needs further clarification. We aimed to investigate the association between ocular biometrical parameters and DR in Chinese people with diabetes mellitus (DM) without any ocular intervention. Methods: This cross‐sectional study recruited type 2 DM patients with no history of ocular treatment in Guangzhou, China. The ocular biometrical parameters were obtained by Lenstar (LS900, Haag‐Streit AG, Koeniz, Switzerland), including corneal diameter, central corneal thickness (CCT), corneal curvature (CC), anterior chamber depth (ACD), lens thickness (LT) and axial length (AL). The lens power and axial length‐to‐cornea radius ratio (AL/CR ratio) were calculated. Spherical equivalent (SE) was determined by auto‐refraction after pupil dilation. Multivariate logistic regression analyses were performed to explore the associations of ocular biometry with any DR and vision threatening DR (VTDR). Results: A total of 1838 patients were included in the final analysis, involving 1455 (79.2%) patients without DR and 383(20.8%) patients with DR. After adjusting confounding factors, any DR was independently associated with AL (odds ratio (OR) 0.84 per 1 mm increase, 95% confidence interval (CI): 0.74, 0.94) and AL/CR ratio (OR 0.26 per 1 increase, 95%CI: 0.10, 0.70). Similarly, the presence of VTDR was independently related to AL (OR 0.67 per 1 mm increase, 95%CI: 0.54, 0.85) and AL/CR ratio (OR 0.04 per 1 increase, 95%CI: 0.01, 0.25). The lens power may not be significantly correlated with presence of any DR or VTDR. The CC, corneal diameter and refractive status were not significantly correlated with presence of DR or VTDR. Conclusion: Longer AL and higher AL/CR ratio may be protective factors against the occurrence and progression of DR. Further longitudinal studies are warranted to verify if refractive status and AL‐associated parameters contribute to the occurrence and progression of DR in type 2 DM. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the associations of the estimated glomerular filtration rate (eGFR) with peripapillary choroidal thickness (pCT) and retinal nerve fiber layer (pRNFL) thickness in diabetic patients by using swept-source optical coherence tomography (SS-OCT). Methods: Ocular treatment-naïve patients with type 2 diabetes mellitus registered in the community health system in Guangzhou, China, were recruited to participate in this prospective cross-sectional study. The eGFR was determined using the Xiangya formula, and the renal function was categorized into non-chronic kidney disease (non-CKD), mild CKD, and moderate to severe CKD (MS-CKD) according to the guidelines. The pCT and pRNFL thicknesses at 12 o'clock were obtained using an SS-OCT by a circular scan with a diameter of 3.4 mm centering on the optic nerve head, and the data from only one eye in each patient were used. Results: This study included 1,408 diabetic patients, with a mean age of 64.4 ± 7.8 years. The average pCT decreased with renal function deterioration, with 126.0 ± 58.0 μm for non-CKD, 112.0 ± 51.2 μm for mild CKD and 71.0 ± 22.9 μm for MS-CKD, respectively (P <.001). The pCT was found to be significantly thinner in CKD patients in all quadrantes (P <.05 in all regions) with the exception of the inferior quadrant, and the average pCT was positively correlated with eGFR (β = 0.3, 95%CI = 0.0 to 0.6, P =.021) after adjusting for other factors. The pRNFL thickness in the nasal quadrant was significantly reduced in patients with CKD, and pRNFL thickness was positively correlated with eGFR (β = 0.1, 95%CI = 0.0–0.2, P =.009) after adjusting for other factors. Conclusion: Impaired renal function was associated with a reduction of pCT and pRNFL thickness in patients with type 2 diabetes. The measurement of pCT and pRNFL may provide additional information for predicting renal impairment. [ABSTRACT FROM AUTHOR]

Background: Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public. Results: In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting. Conclusion: We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting. [ABSTRACT FROM AUTHOR]