红外光谱数据融合对栽培滇重楼产地鉴别*

目的 多样的环境因素使得不同产地栽培滇重楼的化学成分也丰富多样，不同居群栽培滇重楼的甾体皂苷类成分具有很大的差异，多源数据融合分析能更全面的表征药材化学信息，建立一个高效而准确的产地鉴别模型，为其资源合理开发利用提供依据。方法 以来自云南和四川的8个产地（保山、楚雄、大理、红河、丽江、成都、文山、玉溪）共366份栽培滇重楼根茎为实验材料，采集其傅里叶变换近红外光谱（FT-NIR）和衰减全反射-傅里叶变换中红外光谱（ATR-FTMIR）数据。采用Kennard-Stone算法将不同产地的样品分为2/3的训练集和1/3的预测集，基于4种特征变量提取方法（CARS、VIP、SPA、SO-Covsel）结合2种数据融合策略（低级数据融合和中级数据融合），建立偏最小二乘产地判别分析模型。根据模型参数交叉验证均方根误差（RMSECV）和预测均方根误差（RMSEP）评估模型的稳定性，模型训练集和预测集准确率（ACC）评估模型分类性能。结果 近红外光谱和中红外光谱均能反应不同产地栽培滇重楼的化学成分差异，在中级数据融合中，基于VIP和SPA提取的特征变量建立的模型正确率均大于94%。相较于中级数据融合，低级数据融合模型得到了最为满意的结果，其预测集分类正确率达到100%。结论 根据近红外和中红外数据建立的低级数据融合PLS-DA模型，能够用于栽培滇重楼的产地鉴别分析。     

A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis

Evaluation of the ossification of the medial clavicular epiphysis plays a key role in forensic age estimation. The purpose of the present study was to assess a new numerical cut-off at the age of 18 years, taking into consideration Magnetic Resonance (MR) images of the medial clavicular epiphysis. We analyzed 163 MR scans of Italian subjects aged between 14 and 25 years. Using the data obtained we calculated two ratios: REM-1 (ratio between the length of the whole epiphysis and the length of the metaphysis) and REM-2 (ratio between the length of epiphyseal-metaphyseal fusion and the length of the metaphysis). In 68 out of 163 cases it was not possible to measure REM-2. The reproducibility was demonstrated using the Intraclass Correlation Coefficient (ICC) (Cronbach’s alpha > 0.80). REM-1 and REM-2 were compared in each category of age (adult and minor) by the Wilcoxon signed-rank test. The cut-off points for measurements of REM-1 and REM-2 were determined by logistic regression. For REM-1, the cut-off scores were 0.83 for all individuals (accuracy = 94.77%) and males (accuracy = 96.05%), and 0.86 for females (accuracy = 92.30%). For REM-2, the cut-off values were 0.40 for all individuals and males (accuracy = 100.00%), and 0.41 for females (accuracy = 100.00%). Finally, receiver operating characteristic (ROC) curves for age classification based on REM-1 and REM-2 were constructed, showing that REM-2 had the highest discriminative power. Thus, a new cut-off model for predicting the age of majority has been introduced, conducting a quantitative analysis thanks to the use of a high-resolution imaging tool.     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

应用 σ度量对深圳市龙华区三家综合医院临床生化检验结果互认的评价研究

目的　探讨 σ度量在深圳市龙华区三家综合医院临床生化检验结果互认中的应用价值。方法　收集三家医院 2019年广东省临检中心常规化学，特殊蛋白前两次的室间质评（ EQA）样本，经混匀并分割成比对样本送至各评价实验室进行检测；以卫生部临检中心（ NCCL)标准和生物学变异导出的允许总误差（ TEa%）为质量规范，通过加权法计算出各项目 2019年 1～ 6月期间室内质控在控数据的不精密度，以 2018年参加卫生部室间质评（ EQA）数据作为偏倚（ bias%）来源，计算三家医院 29个检验项目的 σ度量值并进行性能评价，利用质量目标指数（ QGI)提出改善方法；将评价项目的 σ度量值与直接比对试验进行比较，通过统计分析确定 σ截断值（ cut-off）。结果　①三家医院 29个生化项目的性能并不一致，将项目中、高值的 σ值进行配对 t检验，其 t值分别为 2.28，3.01和 0.74，按 α =0.05水准判断得出医院 A与 B，医院 A与 C间的性能差异有统计学意义，医院 B与 C间的性能差异无统计学意义，医院 A项目性能要优于 B，C医院。评估的 87个项目中有 53项 σ平均值 <6，根据 QGI判断，3个项目需优先改善准确度，10个项目需同步改善准确度和精密度，另 40个项目需优先改善精密度。②对医院间项目比对通过率进行配对 χ2检验，其 χ2分别为 5.33，6.25和 2.5，按α=0.05水准判断， B与 A医院比对通过率低于 C与 A医院、 C与 B医院，差异有统计学意义； C与 A医院比对通过率跟 C与 B医院差异无统计学意义。③将 σ度量值与直接比对试验进行比较，经配对 χ2检验，当 σ cut-off值取≥ 2σ， ≥ 3σ，≥ 4σ，≥ 5σ和≥ 6σ时，其 χ2分别为 7.20，0.00，9.09，15.06和 16.06，按 χ20.05，1=3.84，α=0.05水准判断，当σcut-off值取≥ 3σ时，两种比对方法的差异无统计学意义。结论　σ度量值可以单独用于评价医院间生化检验结果的互认，在判断项目是否具有可比性时，可考虑将 σ cut-off值设为≥ 3σ。     

2016—2021年灌云县手足口病流行病学特征分析

目的 分析灌云县2016—2021年手足口病流行特征,为手足口病的防控提供参考依据。方法 采用描述性统计方法,分析灌云县手足口病的流行特征以及病原学变化等。结果 2016 —2021年灌云县手足口病 4 205例,呈现出“隔年高发”特征,年平均发病率为67.50/10万;4 —6月和10 —12月为高发期;男性占比明显高于女性;0～4岁散居儿童居多;伊山镇、侍庄街道和东王集镇发病率位居前三。2016 —2017年确诊患儿病原以EV71为主,2018—2021年以Cox A6为主。8例重症病例集中在1～5岁,确诊的重症患者以EV71感染为主。结论 灌云县手足口病疫情具有“隔年高发”和季节性“双峰流行”特征,以0～4岁散居儿童为高发人群和灌云县县城及周边镇(街)为高发区域。EV71灭活疫苗接种能有效预防EV71病毒引发的手足口病。     

p53基因突变与骨髓增殖性肿瘤临床特征及预后关系

探究p53 基因突变与骨髓增殖性肿瘤（MPN）临床特征及预后的关系。方法 选取2017年1月～2020年1月本院收治的MPN患者126例，二代测序法检测患者p53 基因突变情况。对患者进行随访，统计患者总生存（OS）时间和累计生存率；分析p53 基因突变对患者临床特征、预后的影响。结果 126例MPN患者中12例（9.52%）检出p53基因突变，突变主要位于4~8号外显子上，不同类型患者的p53 基因突变检出率比较差异无统计学意义（P＞0.05）。p53突变组患者年龄大于p53 非突变组，WBC水平低于p53 非突变组（P＜0.05），两组患者的染色体核型、IPSS预后分层比较差异无统计学意义（P＞0.05）；p53 非突变组患者的OS时间、累计生存率均明显高于p53突变组患者（P＜0.05）。结论 MPN患者p53 基因突变发生率较高，与患者年龄、WBC水平、异常核型及IPSS预后分层相关，p53 基因突变会影响患者的预后，可作为临床筛查预后不良高风险人群的客观指标。     

Neurofeedback and neuroplasticity of visual self-processing in depressed and healthy adolescents: A preliminary study

Adolescence is a neuroplastic period for self-processing and emotion regulation transformations, that if derailed, are linked to persistent depression. Neural mechanisms of adolescent self-processing and emotion regulation ought to be targeted via new treatments, given moderate effectiveness of current interventions. Thus, we implemented a novel neurofeedback protocol in adolescents to test the engagement of circuits sub-serving self-processing and emotion regulation.MethodsDepressed (n = 34) and healthy (n = 19) adolescents underwent neurofeedback training using a novel task. They saw their happy face as a cue to recall positive memories and increased displayed amygdala and hippocampus activity. The control condition was counting-backwards while viewing another happy face. A self vs. other face recognition task was administered before and after neurofeedback training.ResultsAdolescents showed higher frontotemporal activity during neurofeedback and higher amygdala and hippocampus and hippocampi activity in time series and region of interest analyses respectively. Before neurofeedback there was higher saliency network engagement for self-face recognition, but that network engagement was lower after neurofeedback. Depressed youth exhibited higher fusiform, inferior parietal lobule and cuneus activity during neurofeedback, but controls appeared to increase amygdala and hippocampus activity faster compared to depressed adolescents.ConclusionsNeurofeedback recruited frontotemporal cortices that support social cognition and emotion regulation. Amygdala and hippocampus engagement via neurofeedback appears to change limbic-frontotemporal networks during self-face recognition. A placebo group or condition and contrasting amygdala and hippocampus, hippocampi or right amygdala versus frontal loci of neurofeedback, e.g. dorsal anterior cingulate cortex, with longer duration of neurofeedback training will elucidate dosage and loci of neurofeedback in adolescents.     

耳穴贴压联合涌泉穴按摩对老年原发性高血压患者血压变异性的影响

目的:探讨耳穴贴压联合涌泉穴按摩治疗老年原发性高血压的效果及对患者血压变异性(BPV)的影响。方法:将94例老年原发性高血压患者随机分为对照组(n=47)和观察组(n=47)。常规西医治疗基础上,观察组给予王不留行籽耳穴贴压联合涌泉穴按摩治疗。比较两组治疗前后收缩压(SBP)、舒张压(DBP)水平、SBP/DBP下降幅度并评估降压疗效,BPV根据24 h动态血压监测结果评估,计算两组治疗前后24h SBP标准差(24h SBP)、24h DBP标准差(24h DBP)、白天SBP标准差(dSSD)、白天DBP标准差(dDSD)、夜间SBP标准差(nSSD)和夜间DBP标准差(nDSD),记录两组治疗期间不良反应情况。结果:观察组治疗后SBP、DBP水平和下降幅度均优于对照组(P<0.05); 观察组降压总有效率97.87%明显高于对照组82.98%,差异有统计学意义(P<0.05); 对照组治疗后24h SBP、24h DSD、dSSD、dDSD较治疗前有明显下降(P<0.05),观察组治疗后24h SBP、24h DSD、dSSD、dDSD、nSSD显著低于治疗前(P<0.05),观察组治疗后24h SBP、24h DSD、dSSD均显著低于对照组,差异有统计学意义(P<0.05); 两组不良反应以头晕、头痛、恶心和眩晕为主,症状程度均较轻,患者耐受性好,组间不良反应率比较差异均无统计学意义(P<0.05)。结论:常规西医治疗基础上给予中医耳穴贴压联合涌泉穴按摩,不仅能有效降低老年原发性高血压患者血压水平和增益降压疗效,而且能显著降低BPV。     

基于化学模式识别技术提升参威骨痹片的质量标准并监测其生产中质控指标的转移率变化

目的:提升参威骨痹片的质量标准,初步探索其质量控制指标成分在批间含量差异较大的原因。方法:采用HPLC建立参威骨痹片的指纹图谱,以Diamonsil C18(4. 6 mm×250 mm,5μm)为色谱柱,流动相乙腈(A)-0. 1%磷酸水溶液(B)梯度洗脱(0～5 min,10%A;5～15 min,10%～12%A;15～30 min,12%～26%A;30～43 min,26%～31%A,43～50 min,31%～40%A,50～70 min,40%～55%A;70～84 min,55%～72. 5%A),检测波长230 nm。以共有峰为自变量绘制正交偏最小二乘法-判别分析-变量重要性投影(OPLS-DA-VIP)图,将共有峰对该制剂各批次间指纹图谱差异的贡献度量化,寻找差异较大的色谱峰,结合相关文献,筛选出与参威骨痹片临床适应症相关的成分并进行其含量测定的专属性试验,最终选定质控指标。通过HPLC-二极管阵列检测器(DAD)同时对本品及其生产过程中间体中质控指标进行测定,检测波长236,276,230,322 nm,其他条件同HPLC指纹图谱检测方法。结果:HPLC指纹图谱共标定了26个共有峰,各批次样品指纹图谱与对照指纹图谱的相似度均≥0. 950。优选出马钱苷酸、龙胆苦苷、芍药苷、蛇床子素为参威骨痹片的质控指标,四者的平均质量分数分别为161. 02,401. 80,255. 54,80. 68μg·g-1。结论:所建立的指纹图谱及多指标定量分析方法稳定、可靠,可用于参威骨痹片的质量控制。原料药批间质控指标成分含量差异和生产过程中间体的质控方法不够完善是引起该制剂批间质控指标成分含量差异较大的主要原因。