Association between the onset of diabetic retinopathy and thickness changes in the retina and choroid of children with type 1 diabetes: A three-year longitudinal study.

Purpose: To explore the longitudinal changes in retinal and choroidal thickness and their relation with the onset of type 1 diabetes mellitus (T1DM) in children.

Methods: Thirty-eight children with T1DM and 71 healthy controls were included in this 3-year longitudinal study. Ophthalmic and systemic examinations were conducted on each participant.

Using the SEIQR model with epidemic amplifier effect to predict the final outbreak size of the COVID-19 in Dalian, Liaoning province, China.

Objectives: Early in the outbreak, to predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China, the finding can be used to provide a scientific reference for timely adjustment of prevention and control strategies.

Methods: Data from COVID-19 patients were collected from August 26 to September 14 2022. Early in the outbreak, a Susceptible-Exposed-Infectious-Quarantine-Recovered (SEIQR) dynamics model with an epidemic amplifier effect, based on the basic model, was developed to fit the data and predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China.

Evaluation of optical coherence tomography angiography metrics in children and adolescents with type 1 diabetes: 4-year longitudinal study.

Purpose: To evaluate longitudinal changes in optical coherence tomography angiography (OCTA) metrics in children and adolescents with type 1 diabetes (T1D).

Methods: This prospective observational cohort study included thirty-two eyes from thirty T1D children with no history of diabetic retinopathy (DR) who were followed up for 4 years. Participants underwent OCTA examinations at baseline and during follow-up.

Three-Year Follow-Up Study Investigating Changes in Retinal Vascular Morphology and the Relationship with Diabetic Retinopathy in Children with Type 1 Diabetes Mellitus.

Introduction: This study aimed to investigate changes in retinal microvascular morphology and associated factors, and their relationship with diabetic retinopathy (DR) in children with type 1 diabetes mellitus (T1DM).

Methods: Thirty-eight children enrolled in this 3-year follow-up study underwent complete ophthalmic examinations including fundus photography. Retinal vascular parameters were measured automatically and compared between baseline and follow-up.

Using time-dependent reproduction number to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China.

Objectives: To forecast the development trend of current outbreak in Dalian, mainly to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China, the results can be used to provide a scientific reference for timely adjustment of prevention and control strategies.

Methods: During the outbreak, Bayesian framework was used to calculated the time-dependent reproduction number ([Formula: see text]), and then above acquired [Formula: see text] and exponential trend equation were used to establish the prediction model, through the model, predict the [Formula: see text] value of following data and know when [Formula: see text] smaller than 1.

Results: From July 22 to August 5, 2020, and from March 14 to April 2, 2022, 92 and 632 confirmed cases and asymptomatic infected cases of COVID-19 were reported (324 males and 400 females) in Dalian.

Ocular surface microbiota dysbiosis contributes to the high prevalence of dry eye disease in diabetic patients.

People with diabetes mellitus (DM) are at an increased risk for developing dry eye disease (DED). However, the mechanisms underlying this phenomenon remain unclear. Recent studies have found that the ocular surface microbiota (OSM) differs significantly between patients with DED and healthy people, suggesting that OSM dysbiosis may contribute to the pathogenesis of DED.

Using the hybrid EMD-BPNN model to predict the incidence of HIV in Dalian, Liaoning Province, China, 2004-2018.

Background: Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS's development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model's performance.

Methods: The monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models.

Application of the Effect of Latitude on the Starting of Hand, Foot, and Mouth Disease Epidemic on Early Warning in Dalian, Liaoning Province, China.

Hand, foot, and mouth disease (HFMD) is a common infectious gastrointestinal disease in children under the age of 5. Many studies have documented that meteorological variables, especially temperature, are associated with HFMD. Since the general climate patterns occur as latitude increases, so latitude may indirectly influence the peak time of HFMD.

Using a Negative Binomial Regression Model for Early Warning at the Start of a Hand Foot Mouth Disease Epidemic in Dalian, Liaoning Province, China.

Background: The hand foot and mouth disease (HFMD) is a human syndrome caused by intestinal viruses like that coxsackie A virus 16, enterovirus 71 and easily developed into outbreak in kindergarten and school. Scientifically and accurately early detection of the start time of HFMD epidemic is a key principle in planning of control measures and minimizing the impact of HFMD. The objective of this study was to establish a reliable early detection model for start timing of hand foot mouth disease epidemic in Dalian and to evaluate the performance of model by analyzing the sensitivity in detectability.

PREDICTING CLINICALLY DIAGNOSED DYSENTERY INCIDENCE OBTAINED FROM MONTHLY CASE REPORTING BASED ON METEOROLOGICAL VARIABLES IN DALIAN, LIAONING PROVINCE, CHINA, 2005-2011 USING A DEVELOPED MODEL.

This study describes our development of a model to predict the incidence of clinically diagnosed dysentery in Dalian, Liaoning Province, China, using time series analysis. The model was developed using the seasonal autoregressive integrated moving average (SARIMA). Spearman correlation analysis was conducted to explore the relationship between meteorological variables and the incidence of clinically diagnosed dysentery.