Improved Birthweight Prediction With Feature-Wise Linear Modulation, GRU, and Attention Mechanism in Ultrasound Data.

Objectives: Birthweight prediction in fetal development presents a challenge in direct measurement and often depends on empirical formulas based on the clinician's experience. Existing methods suffer from low accuracy and high execution times, limiting their clinical effectiveness. This study aims to introduce a novel approach integrating feature-wise linear modulation (FiLM), gated recurrent unit (GRU), and Attention network to improve birthweight prediction using ultrasound data.

COVID-19-Associated Neurological Complications in Children.

Objectives: Neurological manifestations of Severe Acute Respiratory Syndrome coronavirus-2 have been well documented in adults during and after infection with the virus as well as after vaccination. The incidence of severe neurological symptoms among children is very low. This study aimed to analyze the varied neurological manifestations after COVID-19 infection among children and give a report on a single-center experience with these severe neurological symptoms.

The Science of Solid Lipid Nanoparticles: From Fundamentals to Applications.

Solid lipid nanoparticles (SLNs) play a crucial role in drug delivery, offering benefits such as enhanced bioavailability, targeted distribution, and reduced toxicity. This article provides a comprehensive overview of SLN formulation, development, and advancement in pharmaceutical research, examining their characteristics, classifications, and significance. The review also delves into the real-world applicability of various SLN formulations across different routes of administration, discussing their advantages, disadvantages, and challenges of scalability, along with strategies for efficient implementation.

Prevalence and influencing factors of metabolic syndrome among rural adult population in a district of South India.

Background: Metabolic syndrome has increased globally due to sedentary lifestyles, unhealthy diets and obesity, which is posing a substantial burden on healthcare systems. Understanding the determinants of metabolic syndrome like lifestyle factors, socioeconomic status and the environment are vital for devising effective prevention and management. Research into these determinants helps to identify high-risk populations and develop interventions to reduce its occurrence.

Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.

In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images.