125 research outputs found

    Relationship between hyperlipidemia and the risk of death in aneurysm: a cohort study on patients of different ages, genders, and aneurysm locations

    Get PDF
    Aims: The study aimed to assess the association of hyperlipidemia and the risk of death in the aneurysm population, focusing on age, gender, and aneurysm location differences.Methods: All patients’ data on this retrospective cohort study were obtained from the Medical Information Mart for Intensive Care (MIMIC-III) database, and the baseline characteristics and laboratory parameters of all patients were collected. The COX regression model was established to explore the association of hyperlipidemia and the risk of death for patients with aneurysms. More importantly, subgroup analyses based on the age, gender, and aneurysm location differences were performed.Results: A total of 1,645 eligible patients were enrolled in this study. These patients were divided into the survival group (n = 1,098) and the death group (n = 547), with a total mortality rate of approximately 33.25%. The result displayed that hyperlipidemia was associated with a decreased death risk in aneurysm patients. In addition, we also found that hyperlipidemia was associated with a lower death risk of abdominal aortic aneurysm and thoracic aortic arch aneurysm among aneurysm patients aged ≥60 years; hyperlipidemia was only a protective factor for the death risk of male patients diagnosed with abdominal aortic aneurysm. For female patients diagnosed with abdominal aortic aneurysm and thoracic aortic arch aneurysm, hyperlipidemia was associated with a decreased death risk.Conclusion: The relationship of hyperlipidemia, hypercholesterolemia, and the risk of death for patients diagnosed with aneurysms was significantly associated with age, gender, and aneurysm location

    Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes

    Get PDF
    Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN). The first stage preliminarily locate the kidney and cut off the irrelevant background to reduce class imbalance and computation cost. Then the second stage precisely segment the kidney and tumor on the cropped patch. The proposed method achieves 98.05% and 83.70% of Dice score on the validation set of MICCAI 2019 KiTS Challenge

    Colorimetric Assay for Determination of Lead (II) Based on Its Incorporation into Gold Nanoparticles during Their Synthesis

    Get PDF
    In this report, we present a new method for visual detection of Pb2+. Gold nanoparticles (Au-NPs) were synthesized in one step at room temperature, using gallic acid (GA) as reducer and stabilizer. Pb2+ is added during the gold nanoparticle formation. Analysis of Pb2+ is conducted by a dual strategy, namely, colorimetry and spectrometry. During Au-NPs synthesis, addition of Pb2+ would lead to formation of Pb-GA complex, which can induce the aggregation of newly-formed small unstable gold nanoclusters. Consequently, colorimetric detection of trace Pb2+ can be realized. As the Pb2+ concentration increases, the color turns from red-wine to purple, and finally blue. This method offers a sensitive linear correlation between the shift of the absorption band (Δλ) and logarithm of Pb2+ concentration ranging from 5.0 × 10−8 to 1.0 × 10−6 M with a linear fit coefficient of 0.998, and a high selectivity for Pb2+ detection with a low detection limit down to 2.5 × 10−8 M

    Baichuan 2: Open Large-scale Language Models

    Full text link
    Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan
    corecore