WeChat Mini Program
Old Version Features

    • 反复玻璃体腔注射康柏西普或雷珠单抗对眼底血管性疾病患者角膜的影响

    林杜生,张少斌,黄晓琼,周昭涛,肖亮伊, 罗晓红

    Journal of Shantou University Medical College（2021）

    Cited 0|Views1
    Abstract
    目的:探讨反复玻璃体腔注射康柏西普或雷珠单抗对眼底血管性疾病患者角膜的影响.方法:回顾性研究.选取2018年2月—2020年11月在汕头博德眼科医院接受治疗的眼底血管性疾病患者101例(101眼),其中男性61例,女性40例.按治疗药物不同分成康柏西普组(51例51眼)和雷珠单抗组(50例50眼).康柏西普组患者每月玻璃体腔注射康柏西普(0.5 mg/0.05 mL),雷珠单抗组患者每月玻璃体腔注射雷珠单抗(0.5 mg/0.05 mL),连续治疗3个月.分别于玻璃体腔注射前和注射后1、3、5个月检查患者的中央角膜厚度、角膜内皮细胞密度、六角形细胞比例、细胞面积变异系数等指标.结果:康柏西普组和雷珠单抗组患者的性别、年龄、疾病类型之间差异均没有统计学意义(P>0.05).两组患者接受注射治疗后1、3、5个月的术眼中央角膜厚度、角膜内皮细胞密度、六角形细胞比例、细胞面积变异系数等指标与治疗前比较,差异均没有统计学意义(P>0.05).结论:反复玻璃体腔注射康柏西普或雷珠单抗5个月内对眼底血管性疾病患者中央角膜厚度及角膜内皮细胞没有明显的影响.
    More
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined