Enhancing LGMD-based model for collision prediction via binocular structure

IntroductionLobular giant motion detector (LGMD) neurons, renowned for their distinctive response to looming stimuli, inspire the development of visual neural network models for collision prediction. However, the existing LGMD-based models could not yet incorporate the invaluable feature of depth distance and still suffer from the following two primary drawbacks. Firstly, they struggle to effectively distinguish the three fundamental motion patterns of approaching, receding, and translating, in contrast to the natural abilities of LGMD neurons. Secondly, due to their reliance on a general determination process employing an activation function and fixed threshold for output, these models exhibit dramatic fluctuations in prediction effectiveness across different scenarios.MethodsTo address these issues, we propose a novel LGMD-based model with a binocular structure (Bi-LGMD). The depth distance of the moving object is extracted by calculating the binocular disparity facilitating a clear differentiation of the motion patterns, after obtaining the moving object's contour through the basic components of the LGMD network. In addition, we introduce a self-adaptive warning depth-distance, enhancing the model's robustness in various motion scenarios.ResultsThe effectiveness of the proposed model is verified using computer-simulated and real-world videos.DiscussionFurthermore, the experimental results demonstrate that the proposed model is robust to contrast and noise

Evaluating the role of serum uric acid in the risk stratification and therapeutic response of patients with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD)

Background: Pulmonary arterial hypertension (PAH) is a malignant pulmonary vascular disease that negatively impacts quality of life, exercise capacity, and mortality. This study sought to investigate the relationship between serum uric acid (UA) level and the disease severity and treatment response of patients with PAH and congenital heart disease (PAH-CHD).Methods: This study included 225 CHD patients and 40 healthy subjects. Serum UA was measured in all patients, and UA levels and haemodynamic parameters were re-evaluated in 20 patients who had received PAH-specific drug treatment for at least 7 ± 1 month.Results: Serum UA levels were significantly higher in PAH-CHD patients than in CHD patients with a normal pulmonary artery pressure and normal subjects (347.7 ± 105.7 μmol/L vs. 278.3 ± 84.6 μmol/L; 347.7 ± 105.7 μmol/L vs. 255.7 ± 44.5 μmol/L, p < 0.05). UA levels in the intermediate and high risk groups were significantly higher than those in the low-risk group (365.6 ± 107.8 μmol/L vs. 311.2 ± 82.8 μmol/L; 451.6 ± 117.6 μmol/L vs. 311.2 ± 82.8 μmol/L, p < 0.05). Serum UA levels positively correlated with mean pulmonary arterial pressure, WHO functional class, pulmonary vascular resistance, and NT-proBNP (r = 0.343, 0.357, 0.406, 0.398; p < 0.001), and negatively with mixed venous oxygen saturation (SvO2) and arterial oxygen saturation (SaO2) (r = −0.293, −0.329; p < 0.001). UA significantly decreased from 352.7 ± 97.5 to 294.4 ± 56.8 μmol/L (p = 0.001) after PAH-specific drug treatment for at least 6 months, along with significant decreases in mean pulmonary arterial pressure and pulmonary vascular resistance and increases in cardiac index and mixed SvO2.Conclusion: Serum UA can be used as a practical and economic biomarker for risk stratification and the evaluation of PAH-specific drug treatment effects for patients with PAH-CHD