痛敏穴的探查方法概况
Hunan Journal of Traditional Chinese Medicine(2019)
Abstract
敏化穴,即发生敏化现象的穴区或部位[1].大量的临床[2-3]及基础研究[4]都证实了敏化穴能加强穴位的主治效应,产生“小刺激,大反应”,而敏化穴的精准定位是保证疗效的重要因素之一.痛敏穴是反映疾病最常见的体现方式,表现为患者主观上疼痛敏感性(特殊痛感或快然感)显著增强或客观上出现痛阈值降低,又称阿是穴或反应点[1,5].痛敏穴的探查方法多样,论述较为分散,而在操作上又存在着诸多干扰因素,影响其精准定位.本文通过检索Pubmed、Springer、中国知网(CNKI)、维普中文科技期刊数据库(VIP)、万方数据(WANFANG DATA)等数据库对涉及痛敏穴探查的文献进行梳理,归纳其常用的探查方法,并分析影响其准确性的干扰因素,以期为今后痛敏穴的探查提供一定的指导,进一步提高临床疗效及研究结果的可靠性和可重复性.
More求助PDF
上传PDF
View via Publisher
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