目的 提高对囊性纤维化的认识。 方法 2011年11月收治1例自幼有临床表现的囊性纤维化患者，回顾其诊断及治疗经过，复习相关文献总结其临床特征、诊疗进展及预后评价。 结果 囊性纤维化起病年龄较早，患者自幼年起即反复出现肺、消化道、肝脏等多系统病变，最终导致多器官功能衰竭。 结论 应提高对囊性纤维化的识别度，对于发病年龄过早、反复发作的严重支气管扩张，伴随生长发育延迟、肝硬化等临床征象应注意对囊性纤维化的筛查。
ObjectivesTo systematically review the efficacy and safety of ciprofloxacin for non-cystic fibrosis bronchiectasis.MethodsDatabases including PubMed, EMbase, The Cochrane Library, CBM, VIP, CNKI and WanFang Data were electronically searched from inception to August 2018 to collect randomized controlled trials (RCTs) on ciprofloxacin in the treatment of non-cystic fibrosis bronchiectasis. Two reviewers independently screened literature, extracted data, and assessed risk of bias of included studies. Then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 9 RCTs involving 1 666 patients were included. The results of meta-analysis showed that: compared with control group, the ciprofloxacin more efficiently eradicate bacteria from sputum (RR=4.34, 95%CI 2.04 to 9.23, P=0.000 1), decrease risk of the exacerbations (RR=0.81, 95%CI 0.71 to 0.93, P=0.002) and the mean bacterial load (MD=–4.08, 95%CI –6.29 to –1.87, P=0.001). However, there were no significant differences between two groups in clinical efficiency and adverse events.ConclusionsThe current evidence shows that, ciprofloxacin can decrease the mean bacterial load and risk of the exacerbation, and more efficiently eradicate bacteria from sputum in non-cystic fibrosis bronchiectasis patients. Due to limited quality and quantity of the included studies, more studies are required to verify the conclusions.
ObjectiveTo explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). MethodsA systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and China Biomedical Literature Database up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. ResultsA total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of specific exhaled VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than exhaled VOCs, including forced expiratory flow at 75% lung capacity (FEF75) and modified Medical Research Council dyspnea scale score (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve (AUC) ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high bias risk, and the overall applicability was unclear. ConclusionThe exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.