社区获得性肺炎甲状腺激素和降钙素原水平变化特点及临床价值

Continuing Medical Education（2015）

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Abstract

目的探讨血清甲状腺激素和降钙素原(PCT)水平在社区获得性肺炎(CAP)诊治中的意义及对疾病评估的价值.方法选取我院住院的42例CAP患者,根据治疗效果将其分为好转组及无好转组,治疗前进行血清甲状腺激素和PCT含量测定,并监测治疗后1周血清 PCT水平,再分别比较两组甲状腺激素和治疗前后机体血清PCT的水平;另外选择20例健康体检者作为对照组,比较CAP组与对照组甲状腺激素和治疗前后血清PCT水平.结果 CAP组患者血清T3、FT3含量均低于对照组(P<0.05);其中,无好转组与好转组比较,血清T3、FT3含量更低,两组差异有统计学意义(P<0.05).治疗前,CAP组血清PCT含量与对照组相比明显升高(P<0.05),但无好转组与好转组差异无显著性差异(P>0.05);治疗后1周,与好转组相比,无好转组血清PCT水平较高,两组有显著性差异(P<0.01).结论 CAP患者血清T3、FT3水平下降,且与感染严重程度正相关,T3、FT3水平越低,病情越严重;在CAP治疗过程中,可以将血清PCT水平作为评价疗效的参考依据.

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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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