Case report: A rare case of bilateral Leber's idiopathic stellate neuroretinitis.

Background: Leber's idiopathic stellate neuroretinitis (LISN) is a rare disease characterized by disk edema, peripapillary and macular hard exudates, and often, the presence of vitreous cells. To enhance clinical understanding of the disease, a retrospective analysis was conducted on a patient diagnosed with LISN at our hospital, and discussions were held regarding its diagnosis and treatment. Methods: We reviewed the medical records of a 26-year-old male patient whose main complaint was a decrease in visual acuity of both eyes for 4 days, which had worsened over the last day. After systemic examination, fundus fluorescein angiography, and indocyanine green angiography, the patient was diagnosed with LISN in both eyes. After treatment with glucocorticoids, the patient's vision showed a significant improvement. Results: Upon admission, the visual acuity of both eyes was: VOD 0.05, VOS 0.25. After 5 days of treatment, the visual acuity of both eyes was: VOD 0.25, VOS 0.4. After 1 month of follow-up, the visual acuity of both eyes was: VOD 0.4, VOS 0.6. After 5 months of follow-up, the patient's vision improved to VOD 0.6, VOS 0.8. Conclusion: The cause of LISN remains unidentified. It is essential to rule out diseases exhibiting similar clinical signs but possessing a clear etiology. The primary treatment approach involves glucocorticoid-based anti-inflammatory therapy, potentially supplemented with antibiotics, antivirals, vasodilators, and traditional Chinese medicine. This disease is usually self-limiting and generally carries a favorable prognosis.

The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography.

BACKGROUND: Coronary Artery Disease (CAD) is a major cause of morbidity and mortality, yet it is frequently asymptomatic in the early stages and hence goes undetected. OBJECTIVE: We aimed to develop a novel artificial intelligence-based approach for early detection of CAD patients based solely on electrocardiogram (ECG). METHODS: This study included patients with suspected CAD who had standard 10-s resting 12-lead ECGs and coronary computed tomography angiography (cCTA) results within 4 weeks or less. The ECG and cCTA data from the same patient were matched based on their hospitalization or outpatient ID. All matched data pairs were then randomly divided into training, validation dataset for model development based on convolutional neural network (CNN) and test dataset for model evaluation. The accuracy (Acc), specificity (Spec), sensitivity (Sen), positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) of the model were calculated by using the test dataset. RESULTS: In the test dataset, the model for detecting CAD achieved an AUC of 0.75 (95% CI, 0.73 to 0.78) with an accuracy of 70.0%. Using the optimal cut-off point, the CAD detection model had sensitivity of 68.7%, specificity of 70.9%, positive predictive value (PPV) of 61.2%, and negative predictive value (NPV) of 77.2%. Our study demonstrates that a well-trained CNN model based solely on ECG could be considered an efficient, low-cost, and noninvasive method of assisting in CAD detection.

Measuring of the COVID-19 Based on Time-Geography.

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.