首发精神分裂症治疗前后甲状腺相关激素水平变化的研究
China Modern Doctor（2021）
Abstract
目的 观察首发精神分裂症治疗前后甲状腺相关激素水平变化.方法 选取109例首发精神分裂症患者(观察组)和113名健康体检者(对照组)作为研究对象.观察在接受抗精神病药物治疗前、接受药物治疗后1个月及第12月、18月的甲状腺相关激素水平.依据患者出院后有无连续服药,将观察组分为服药组(55例)和未服药组(54例),比较两组治疗前后的甲状腺相关激素水平.结果 一般资料比较:观察组、对照组性别构成,年龄,差异无统计学意义(P＞0.05).治疗前,观察组FT3、TT4、TSH水平高于对照组;治疗后,观察组FT4、TT3水平低于对照组;观察组治疗前后对照,FT3、TT4、TT3、TT4较前下降,差异均有统计学意义(P＜0.05).未服药组治疗前和12月时FT3、FT4、TT3、TT4、TSH水平比较,差异均无统计学意义(P＞0.05).服药组18月时FT4、TT3、TT4均低于治疗前水平;未服药组18月时仅FT4、TT4低于治疗前水平,差异有统计学意义(P＜0.05).结论 精神分裂症患者的FT3、TT4、TSH水平较正常人增高;FT3、TT4水平降低是疾病好转的信号;连续服用抗精神病药物会使患者的FT3、FT4、TT3、TT4水平下降;中断服药,甲状腺相关激素会恢复至未服药水平.
More求助PDF
上传PDF
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