BioRED: A Comprehensive Biomedical Relation Extraction Dataset

Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles. Further, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a comprehensive dataset can successfully facilitate the development of more accurate, efficient, and robust RE systems for biomedicine

The effect of fog on the probability density distribution of the ranging data of imaging laser radar

This paper outlines theoretically investigations of the probability density distribution (PDD) of ranging data for the imaging laser radar (ILR) system operating at a wavelength of 905 nm under the fog condition. Based on the physical model of the reflected laser pulses from a standard Lambertian target, a theoretical approximate model of PDD of the ranging data is developed under different fog concentrations, which offer improved precision target ranging and imaging. An experimental test bed for the ILR system is developed and its performance is evaluated using a dedicated indoor atmospheric chamber under homogeneously controlled fog conditions. We show that the measured results are in good agreement with both the accurate and approximate models within a given margin of error of less than 1%

The role of basic health insurance on depression: an epidemiological cohort study of a randomized community sample in Northwest China

BACKGROUND: Little research has focused on the relationship between health insurance and mental health in the community. The objective of this study is to determine how the basic health insurance system influences depression in Northwest China. METHODS: Participants were selected from 32 communities in two northwestern Chinese cities through a three-stage random sampling. Three waves of interviews were completed in April 2006, December 2006, and January 2008. The baseline survey was completed by 4,079 participants. Subsequently, 2,220 participants completed the first follow-up, and 1,888 completed the second follow-up. Depression symptoms were measured by the Center for Epidemiologic Studies Depression Scale (CES-D). RESULTS: A total of 40.0% of participants had at least one form of health insurance. The percentages of participants with severe depressive symptoms in the three waves were 21.7%, 22.0%, and 17.6%. Depressive symptoms were found to be more severe among participants without health insurance in the follow-up surveys. After adjusting for confounders, participants without health insurance were found to experience a higher risk of developing severe depressive symptoms than participants with health insurance (7 months: OR, 1.40; 95% CI, 1.09-1.82; p = 0.01; 20 months: OR, 1.89; 95% CI, 1.37-2.61; p < 0.001). CONCLUSION: A lack of basic health insurance can dramatically increase the risk of depression based on northwestern Chinese community samples

Tumor-associated macrophages regulate gastric cancer cell invasion and metastasis through TGF beta 2/NF-kappa B/Kindlin-2 axis

Objective: Recent studies have shown that tumor-associated macrophages (TAMs) play an important role in cancer invasion and metastasis. Our previous studies have reported that TAMs promote the invasion and metastasis of gastric cancer (GC) cells through the Kindlin-2 pathway. However, the mechanism needs to be clarified. Methods: THP-1 monocytes were induced by PMA/interleukin (IL)-4/IL-13 to establish an efficient TAM model in vitro and M2 macrophages were isolated via flow cytometry. A dual luciferase reporter system and chromatin immunoprecipitation (ChIP) assay were used to investigate the mechanism of transforming growth factor beta 2 (TGF beta 2) regulating Kindlin-2 expression. Immunohistochemistry was used to study the relationships among TAM infiltration in human GC tissues, Kindlin-2 protein expression, clinicopathological parameters and prognosis in human GC tissues. A nude mouse oncogenesis model was used to verify the invasion and metastasis mechanisms in vivo. Results: We found that Kindlin-2 expression was upregulated at both mRNA and protein levels in GC cells cocultured with TAMs, associated with higher invasion rate. Kindlin-2 knockdown reduced the invasion rate of GC cells under coculture condition. TGF beta 2 secreted by TAMs regulated the expression of Kindlin-2 through the transcription factor NF-kappa B. TAMs thus participated in the progression of GC through the TGF beta 2/NF-kappa B/Kindlin-2 axis. Kindlin-2 expression and TAM infiltration were significantly positively correlated with TNM stage, and patients with high Kindlin-2 expression had significantly poorer overall survival than patients with low Kindlin-2 expression. Furthermore, Kindlin-2 promoted the invasion of GC cells in vivo. Conclusions: This study elucidates the mechanism of TAMs participating in GC cell invasion and metastasis through the TGF beta 2/NF-kappa B/Kindlin-2 axis, providing a possibility for new treatment options and approaches.Peer reviewe

Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized the biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this first-of-its-kind survey can provide a comprehensive overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health

The temporal trend of disease burden attributable to metabolic risk factors in China, 1990–2019 : An analysis of the Global Burden of Disease study

Background and aims: The disease burden attributable to metabolic risk factors is rapidly increasing in China, especially in older people. The objective of this study was to (i) estimate the pattern and trend of six metabolic risk factors and attributable causes in China from 1990 to 2019, (ii) ascertain its association with societal development, and (iii) compare the disease burden among the Group of 20 (G20) countries. Methods: The main outcome measures were disability-adjusted life-years (DALYs) and mortality (deaths) attributable to high fasting plasma glucose (HFPG), high systolic blood pressure (HSBP), high low-density lipoprotein (HLDL) cholesterol, high body-mass index (HBMI), kidney dysfunction (KDF), and low bone mineral density (LBMD). The average annual percent change (AAPC) between 1990 and 2019 was analyzed using Joinpoint regression. Results: For all six metabolic risk factors, the rate of DALYs and death increased with age, accelerating for individuals older than 60 and 70 for DALYs and death, respectively. The AAPC value in rate of DALYs and death were higher in male patients than in female patients across 20 age groups. A double-peak pattern was observed for AAPC in the rate of DALYs and death, peaking at age 20–49 and at age 70–95 plus. The age-standardized rate of DALYs increased for HBMI and LBMD, decreased for HFPG, HSBP, KDF, and remained stable for HLDL from 1990 to 2019. In terms of age-standardized rate of DALYs, there was an increasing trend of neoplasms and neurological disorders attributable to HFPG; diabetes and kidney diseases, neurological disorders, sense organ diseases, musculoskeletal disorders, neoplasms, cardiovascular diseases, digestive diseases to HBMI; unintentional injuries to LBMD; and musculoskeletal disorders to KDF. Among 19 countries of Group 20, in 2019, the age-standardized rate of DALYs and death were ranked fourth to sixth for HFPG, HSBP, and HLDL, but ranked 10th to 15th for LBMD, KDF, and HBMI, despite the number of DALYs and death ranked first to second for six metabolic risk factors. Conclusions: Population aging continuously accelerates the metabolic risk factor driven disease burden in China. Comprehensive and tight control of metabolic risk factors before 20 and 70 may help to mitigate the increasing disease burden and achieve healthy aging, respectively