A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Objective: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings.

Design, synthesis and in vitro and in vivo biological evaluation of matrine derivatives as efficient anticancer agents with the characteristics of endoplasmic reticulum stress induction and apoptosis activation.

Natural products have made significant contributions to the prevention and treatment of malignant tumors. However, natural products often suffer from low efficacy and potential toxicity. Therefore, modifying and optimizing lead compounds derived from natural products is a crucial strategy in drug development.

VaxBot-HPV: a GPT-based chatbot for answering HPV vaccine-related questions.

Objective: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

Objectives: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources.

Single-metal atom sandwiched by graphdiyne and BN-doped graphdiyne sheets as an electrocatalyst for nitrogen reduction: a first-principles study.

The exploration of efficient single-atom catalysts provides a prospective pattern for the sustainable development of electrocatalytic nitrogen fixation. We systematically researched the nitrogen reduction properties of catalysts with a single transition metal (TM) atom sandwiched between BN-doped graphdiyne and graphdiyne (labeled BN-TM-G) by first-principles calculations. The TM atom in the novel sandwich structure provides electrons to the adjacent B atom, which acts as the active site, thus driving the fixation and reduction of N.

Neurological complications of COVID-19 in children and the associated immunological responses.

The high spread rate, severe symptoms, psychological and neurological problems, and unavailability of effective medicines are the major factors making Coronavirus disease 2019 (COVID-19) a massive threat to the world. It is thought that COVID-19 causes mild symptoms or mild infectious illness in children. However, we cannot rule out the possibility of serious complications such as the multisystem inflammatory syndrome.

Multiscale dual attention mechanism for fluid segmentation of optical coherence tomography images.

Optical coherence tomography (OCT) technology can obtain a clear retinal structure map, which is greatly beneficial for the diagnosis of retinopathy. Ophthalmologists can use OCT technology to analyze information about the retina's internal structure and changes in retinal thickness. Therefore, segmentation of retinal layers in images and screening for retinal diseases have become important goals in OCT scanning.

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China.

Urban areas consume more than 66% of the world's energy and generate more than 70% of global greenhouse gas (GHG) emissions. With the world's population expected to reach 10 billion by 2100, and with nearly 90% of people living in urban areas, a critical question for planetary sustainability is how the size of cities affects energy use and carbon dioxide (CO) emissions. Are urban agglomerations more energy and emission efficient than smaller cities? Does urban agglomeration exhibit gains from economies of scale concerning emissions? Here, we examine the relationship between urban agglomeration and CO emissions for urban agglomeration in the Yangtze River Delta in China using a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model considering the spatial effects.