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1.
Journal of Chinese Physician ; (12): 1452-1456, 2020.
Artigo em Chinês | WPRIM | ID: wpr-867418

RESUMO

Objective:To evaluate the clinical differences between smokers and non-smokers with chronic obstructive pulmonary disease (COPD).Methods:The clinical data of 5 183 COPD patients, including 3 688 smoking COPD patients and 1 495 non-smoking patients, were collected from the respiratory and critical medicine clinics of 12 Grade-A hospitals in Hunan Province and Guangxi Zhuang Autonomous Prefecture from December 2016 to December 2019. The general condition, clinical symptoms, acute exacerbation history and pulmonary function of the two groups were compared.Results:⑴ Non-smokers were more likely to be female, to be younger and had a lower educational level ( P<0.05). There was no difference of body mass index (BMI) and marriage statue between two groups ( P>0.05). ⑵ Clinical features: the incidence of chest distress in non-smoking COPD patients was higher than that in smoking COPD patients (86.8% and 81.4%, respectively), and the incidence of asthma was also higher than that of smoking COPD patients (86.0% and 83.1%, respectively), with statistically significant difference ( P<0.05). There was no significant difference in the incidence of cough and expectoration between the two groups ( P>0.05). The COPD assessment test (CAT) score of non-smoking COPD patients was significantly higher than that of smoking group (16.2±6.4) and (15.7± 6.5) ( P<0.05). There was no significant difference in the score of dyspnea modified medical research council dyspnea scale (mMRC) and the risk of acute exacerbation between the two groups ( P>0.05). The first second exertional volume as a percentage of predicted value (FEV 1%) and forced vital capacity (FVC) of non-smoking COPD patients were higher than those of smoking COPD patients ( P<0.05). Conclusions:Compared to smokers with COPD, non-smokers more are more likely to be female and have more severe clinical symptoms.

2.
Journal of Chinese Physician ; (12): 1445-1448, 2020.
Artigo em Chinês | WPRIM | ID: wpr-867416

RESUMO

Chronic obstructive pulmonary disease (COPD) complicated with pulmonary tuberculosis (PTB) is becoming more and more common. The coexistence of two diseases brings difficulty to the control of the disease. Smoking, biofuel exposure, vitamin D deficiency and diabetes may be common risk factors for both diseases. Complementary prevention and control strategies can be designed for the above risk factors. This article contains review with tuberculosis as a risk factor for COPD, COPD combined with tuberculosis, and the prevention strategies for both diseases.

3.
Journal of Chinese Physician ; (12): 1441-1444, 2020.
Artigo em Chinês | WPRIM | ID: wpr-867415

RESUMO

Chronic obstructive pulmonary disease (COPD) is a common, preventable and treatable disease that is characterized by persistent respiratory symptoms and airflow limitation due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases. In addition to smoking COPD caused by cigarette smoke, the phenotype of non-smoking COPD accounts for at least 1/4. Non-smoking COPD accounts for a large proportion, especially in developing countries. Non-smoking COPD and smoking COPD should be paid equal attention. More in-depth study on the prognosis of non-smoking COPD is needed.

4.
International Journal of Biomedical Engineering ; (6): 351-356,封3, 2013.
Artigo em Chinês | WPRIM | ID: wpr-553999

RESUMO

NanoString nCounter Analysis System is a newly developed gene expression detection platform that directly measures multiplexed mRNA levels through digital counting of individual mRNA transcripts.This technology uses as little as 100 ng of RNA and can obtain accurate gene quantitative data from up to 800 genes in one reaction.It requires no reverse transcription,enzymes and amplifications,and its sensitivity and accuracy are comparable to real time quantitative PCR.NanoString technology has been more and more extensively used in frontiers of biomedical research and clinics such as in validation of data from high-throughput platforms,gene expression profiling,gene regulatory network,molecular subtyping,diagnosis and prognosis of diseases.

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