北京市结防机构结核病患者耐药情况对比分析

Journal of Clinical Pulmonary Medicine（2021）

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Abstract

目的分析2009年2019年北京市结防机构结核病患者临床分离株耐药情况变化.方法对2009年2019年北京市结防机构分枝杆菌培养阳性临床分离株进行初步菌种鉴定及药物敏感性试验.结果 2009年1554株分枝杆菌阳性分离株中,结核分枝杆菌(MTB)1506株(96.9％),非结核分枝杆菌(NTM)48株(3.1％).2019年1241株分枝杆菌阳性分离株中,MTB1156株(93.2％),NTM85株(6.8％).2019年NTM分离率明显高于2009年,差异有统计学意义(χ2=21.530,P<0.001).2019年MTB感染者中60岁以上患者,外地户籍患者,初治患者比例较2009年有所增加,差异有统计学意义(χ2=36.952、7.054、19.719,P值均<0.05).2019年异烟肼、利福平、乙胺丁醇、卷曲霉素耐药率均低于2009年,差异均有统计学意义(χ2=7.526、4.165、45.212、10.178,P值均<0.05).2019年总耐药率为24.8％、耐多药率为5.3％,较2009年的31.6％、7.6％均有所下降,差异有统计学意义(χ2=14.702、5.598,P值均<0.05).结论北京市总耐药率与耐多药率均有下降,今后应在NTM与MTB的鉴别,流动人口防控,老年结核病防治,疗前药物敏感性检测等方面加以重视.

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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

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