孙丽平教授运用对药辨治小儿咳嗽临证荟萃

阐述了孙丽平教授治疗小儿咳嗽临床常用的8组对药,即前胡与白前、紫菀与款冬花、苦杏仁与桃仁、橘红与桔梗、瓜蒌与冬瓜子、地龙与僵蚕、川贝母与浙贝母、沙参与麦冬的配伍特点及临床应用心得,并列举其临证经典医案,结合中药学理论和现代药理学研究,总结分析孙教授运用对药治疗小儿咳嗽的学术经验及用药思路。孙教授认为小儿体属稚阳,其咳多起肺虚实之热,主以清热护肺,次以运脾滋阴,主次同疗,则阴阳平矣,强调小儿咳嗽用药,应以辨别个体体质差异为基础,提倡对药组方"肺脾同治、气血通调、阴阳平补"的用药理念。附案例1则,以资验证。     

儿童1型糖尿病骨密度变化及影响因素分析

目的 测定儿童1型糖尿病(T1DM)患者骨密度(BMD),分析探讨骨密度变化的影响因素。方法 选取于2018年1月—2021年6月于我院收治的儿童1型糖尿病患者76例,收集性别、年龄、发病年龄、身高、体重、BMI、病程等基本资料,检测空腹血糖、空腹C肽、糖化血红蛋白(HbA1c)、血碳酸氢根(HCO3-)、血清碱性磷酸酶(ALP),应用双能X线吸收测定法测定骨密度,获取Z值。结果 76例儿童1型糖尿病患者骨密度Z值为-0.93±2.14。HbA1c、病程与骨密度Z值呈负相关,差异具有统计学意义(分别B=-0.334,P<0.001;B=-0.191,P=0.017)。结论 儿童1型糖尿病患者骨密度低于健康儿童,血糖控制不良、病程长是1型糖尿病儿童骨密度减低的危险因素。     

经鼻高流量氧气湿化治疗老年慢性阻塞性肺疾病急性加重合并呼吸衰竭的可行性研究

ObjectiveTo investigate the feasibility of transnasal heated humidified high flow nasal cannula oxygen therapy (HFNC) in the treatment of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) with respiratory failure in elderly patients. MethodsA total of 176 elderly patients with AECOPD complicated with respiratory failure who were hospitalized at Peking University Shougang Hospital from December 2016 to January 2022 were enrolled, including 82 patients in an HFNC group and 94 patients in an NPPV group. After treatment, pulse oxygen saturation (SPO₂), arterial partial pressure of carbon dioxide (PaCO₂), oxygenation index (OI), respiratory rate (RR), heart rate (HR), mean arterial pressure (MAP), comfort score, discharge rate, rate of endotracheal intubation, rate of transfer to intensive care unit (ICU), and mortality were compared between the two groups. The independent sample t-test was used for comparison between the two groups. Statistical data are expressed in percentage or number of cases and the χ² test was used for their comparisons. ResultsThe SPO₂ values at 30 min, 1 h, and 6 h were significantly higher in the HFNC group than in the NPPV group (t=-2.049,-2.618, and -3.314, P=0.043, 0.010, and 0.001, respectively). SPO₂ before discharge was significantly lower than that of the NPPV group (t=2.162, P=0.033), but OI at each time point and before discharge had no statistical significance (P＞0.05). MAP at 6 h was significantly higher in the HFNC group than in the NPPV group (t=-2.209, P=0.029), but within the normal range. HRs at 2 h and 3 h in the HFNC group were significantly higher than those of the NPPV group (t=-2.199 and -2.336, P=0.030 and 0.021, respectively). There were no significant differences in RR, HR, or MAP between the two groups at other time points and before discharge (P＞0.05). There was no significant difference in PaCO2 between the two groups (P＞0.05). Comfort score in the HFNC group was significantly higher than that of the NPPV group (t=-46.807, P＜0.001). There were no significant differences in discharge rate, ICU transfer rate, endotracheal intubation rate, and mortality between the two groups (P＞0.05). ConclusionHFNC is as effective as NPPV in treating elderly patients with AECOPD complicated with type Ⅰ or mild type Ⅱ respiratory failure, and HFNC is more comfortable than NPPV.     

Imaging in neonatal respiratory disease

The purpose of this review is to describe the current state of the art in clinical imaging for NICU patients, divided into major areas that correspond to likely phenotypes of neonatal respiratory disease: airway abnormalities, parenchymal disease, and pulmonary vascular disease. All common imaging modalities (ultrasound, X-ray, CT, and MRI) are discussed, with an emphasis on modalities that are most relevant to the individual underlying aspects of disease. Some promising aspects of dynamic and functional imaging are included, where there may be future clinical applicability.     

基于代谢组学分析的18-三体妊娠羊水差异代谢物

摘要:目的通过 非靶向代谢组学方法分析18-三体( trisomy 18,T18) 妊娠母体羊水样本,探索其差异代谢物。方法采用病 例-对照研究,以8例18-三体妊娠母体羊水样本为病例组,40例正常胎儿母体羊水样本为对照组,采用气相色谱飞行时间质 谱技术( GC-T0F/MS)检测两组羊水样本。采用主成分分析(PCA)和正交偏最小二乘法判别分析(OPIS-DA)模型分析代谢谱 差异,通过单维统计分析寻找差异代谢物。结果PCA 和OPLS-DA模型分析均显示病例组与对照组之间无明显分离趋势。 通过单维分析在两组间共发现5种差异代谢物,分别为甘油醛、葡萄糖酸、硫酸吲哚酚.磷酸盐和泛酸(P<0.05)。结论羊水 代谢组学证实18-三体妊娠存在多种代谢物水平的差异,为疾病的发生机制的探索提供了更多研究思路。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Outcomes of coronary artery bypass grafting in patients with heart failure with a midrange ejection fraction

BackgroundCoronary artery bypass grafting (CABG) improves survival in patients with heart failure and severely reduced left ventricular systolic function (LVEF). Limited data exist regarding adverse cardiovascular event rates after CABG in patients with heart failure with midrange ejection fraction (HFmrEF; LVEF > 40% and < 55%).MethodsWe analyzed data on isolated CABG patients from the Veterans Affairs national database (2010-2019). We stratified patients into control (normal LVEF and no heart failure), HFmrEF, and heart failure with reduced LVEF (HFrEF) groups. We compared all-cause mortality and heart failure hospitalization rates between groups with a Cox model and recurrent events analysis, respectively.ResultsIn 6533 veterans, HFmrEF and HFrEF was present in 1715 (26.3%) and 566 (8.6%) respectively; the control group had 4252 (65.1%) patients. HFrEF patients were more likely to have diabetes mellitus (59%), insulin therapy (36%), and previous myocardial infarction (31%). Anemia was more prevalent in patients with HFrEF (49%) as was a lower serum albumin (mean, 3.6 mg/dL). Compared with the control group, a higher risk of death was observed in the HFmrEF (hazard ratio [HR], 1.3 [1.2-1.5)] and HFrEF (HR, 1.5 [1.2-1.7]) groups. HFmrEF patients had the higher risk of myocardial infarction (subdistribution HR, 1.2 [1-1.6]; P = .04). Risk of heart failure hospitalization was higher in patients with HFmrEF (HR, 4.1 [3.5-4.7]) and patients with HFrEF (HR, 7.2 [6.2-8.5]).ConclusionsHeart failure with midrange ejection fraction negatively affects survival after CABG. These patients also experience higher rates myocardial infarction and heart failure hospitalization.