Lixisenatide in early parkinson's disease: efficacy, safety, and future directions: a correspondence.

Parkinson's disease (PD) presents as a complex neurodegenerative disorder characterized by motor and non-motor symptoms, resulting from dopaminergic neuron degeneration. Current treatment strategies primarily aim to alleviate symptoms through pharmacotherapy and supportive therapies. However, emerging research explores novel therapeutic avenues, including the repurposing of drugs like lixisenatide, a GLP-1 receptor agonist initially developed for type 2 diabetes. This correspondence summarizes a phase 2 clinical trial investigating lixisenatide's efficacy in early PD, demonstrating a potential for mitigating motor disability progression. Findings reveal a marginal improvement or stabilization in motor function among lixisenatide-treated individuals compared to placebo, emphasizing its therapeutic promise. Nonetheless, the emergence of gastrointestinal adverse events underscores the need for careful monitoring and management. Further extensive trials are warranted to delineate lixisenatide's efficacy and safety profile, fostering collaborative efforts towards precision treatments in PD.

Fine-Grained Crowd Counting.

Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many practical applications, the total number of people in an image is not as useful as the number of people in each sub-category. For example, knowing the number of people waiting inline or browsing can help retail stores; knowing the number of people standing/sitting can help restaurants/cafeterias; knowing the number of violent/non-violent people can help police in crowd management. In this article, we propose fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category. To enable research in this area, we construct a new dataset of four real-world fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting, waiting in line or not, and exhibiting violent behavior or not. Since the appearance features of different crowd categories are similar, the challenge of fine-grained crowd counting is to effectively utilize contextual information to distinguish between categories. We propose a two branch architecture, consisting of a density map estimation branch and a semantic segmentation branch. We propose two refinement strategies for improving the predictions of the two branches. First, to encode contextual information, we propose feature propagation guided by the density map prediction, which eliminates the effect of background features during propagation. Second, we propose a complementary attention model to share information between the two branches. Experiment results confirm the effectiveness of our method.