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。     

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。     

Selecting patients for early interdisciplinary rehabilitation during neurointensive care after moderate to severe traumatic brain injury

Background

Early interdisciplinary rehabilitation (EIR) in neurointensive care is a limited resource reserved for patients with moderate to severe traumatic brain injury (TBI) believed to profit from treatment. We evaluated how key parameters related to injury severity and patient characteristics were predictive of receiving EIR, and whether these parameters changed over time.

Methods

Among 1003 adult patients with moderate to severe TBI admitted over 72 h to neurointensive care unit during four time periods between 2005 and 2020, EIR was given to 578 and standard care to 425 patients. Ten selection criteria thought to best represent injury severity and patient benefit were evaluated (Glasgow Coma Scale, Head Abbreviated Injury Scale, New-Injury-Severity-Scale, intracranial pressure monitoring, neurosurgery, age, employment, Charlson Comorbidity Index, severe psychiatric disease, and chronic substance abuse).

Results

In multivariate regression analysis, patients who were employed (adjOR 1.99 [95% CI 1.41, 2.80]), had no/mild comorbidity (adjOR 3.15 [95% CI 1.72, 5.79]), needed neurosurgery, had increasing injury severity and were admitted by increasing time period were more likely to receive EIR, whereas receiving EIR was less likely with increasing age (adjOR 0.97 [95% CI 0.96, 0.98]) and chronic substance abuse. Overall predictive ability of the model was 71%. Median age and comorbidity increased while employment decreased from 2005 to 2020, indicating patient selection became less restrictive with time.

Conclusion

Injury severity and need for neurosurgery remain important predictors for receiving EIR, but the importance of age, employment, and comorbidity have changed over time. Moderate prediction accuracy using current clinical criteria suggest unrecognized factors are important for patient selection.     

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.     

胸部CT指导冠脉造影（治疗）术导管选择的价值研究

目的探讨胸部CT 指导冠脉造影（治疗）术导管选择的价值。方法 选择2018 年1 月～2019 年1 月我院收治的300 例行冠状动脉造影（治疗）术的患者作为研究对象，采用单双号法分组，单号为对照组，双号为研究组，每组各150 例，对照组采用超声指导造影导管选择，研究组采用胸部CT 指导造影导管选择，对比两组患者导管选型、大小合适率、导管选择花费时间、手术总耗时、治疗总有效率及手术过程中动脉管壁损伤发生率。结果 研究组患者导管选型合适率（98.00%）、大小合适率（98.67%）均显著高于对照组（85.33%、86.67%），研究组患者导管选择花费时间为（13.76±1.73）min、手术总耗时为（37.18±2.72）min 均显著短于对照组的（21.23±2.15）min、（58.96±3.44）min，研究组患者治疗总有效率（96.67%）显著高于对照组（77.33%），研究组患者手术过程中动脉管壁损伤发生率（0.67%）显著低于对照组（15.33%），差异均有统计学意义（P＜0.05）。结论 在冠状动脉造影术中采用胸部CT指导临床患者选择造影导管，不仅能有效提高患者导管选型、大小合适率、缩短导管选择花费时间和降低其脉管壁损伤发生率，还能有效提高手术整体治疗效果，提高患者预后质量。     

运用彭静山教授“一点二穴三线四面取穴法”针刺治疗难治性面瘫

彭静山教授提出“一点二穴三线四面”八字取穴法。笔者运用此八字取穴法指导针灸临床取穴治疗难治性面瘫,临床效果显著。本文列举临床典型病例,详细阐述取穴和针刺治疗方法,并对治疗机理进行论述。对于难治性面瘫,单纯毫针针刺治疗效果有一定局限性,以“一点二穴三线四面”八字取穴法为基础,取穴点以透刺连接构成线、面,以芒针联合透刺法为主要治疗方法,可强化治疗效果、提高临床效应。     

基于复杂网络技术对电针治疗乳腺增生病处方分析

目的基于复杂网络技术分析电针治疗乳腺增生病的核心穴位及配伍穴相关性,并对电针波型进行探讨。方法检索中国期刊全文数据库(CNKI)、万方数据库、维普网数据库(VIP)中从1954年1月1日到2018年12月31日公开发表的电针治疗乳腺增生病的临床中文文献,利用Excel表格工具建立电针治疗乳腺增生病数据库,利用Matlab2014a软件进行节点中心性分析和聚类分析,利用Gephi0.9.1软件制作复杂网络示意图对分析结果进行描述和展示,利用Excel表格工具制作电针治疗乳腺增生病不同波形比例饼状图并分析其规律。结果选定43条电针处方:电针治疗乳腺增生病主穴核心度前三位依次为膻中穴、足三里、肩井穴;配伍穴位核心度前三位依次为太冲穴、太溪穴、脾俞穴;经络核心度前三位依次为足阳明胃经、任脉、足少阳胆经。配伍穴位相关性分析中,相关性频度前三位组合依次为太冲穴-太溪穴、太冲穴-肾俞穴、太冲穴-三阴交。电针波形中以连续波与疏密波常见,均具有较高的临床应用价值。结论电针治疗乳腺增生病处方以足阳明胃经为最多,其次为任脉;电针波型以连续波为主。     

基于神经网络和支持向量机的中医体质辨识模型研究＊

目的 提出基于舌象和形体特征的中医体质辨识模型，探讨中医以舌辨质客观化、规范化方法。方法 提取客观化的舌象特征（舌质与舌苔的色度和饱和度、舌苔纹理的平均亮度、平滑度以及齿痕特征）和形体特征，建立基于神经网络和支持向量机的客观化舌象和形体特征的辅助中医体质识别模型。结果 对平和质、气虚质、阴虚质和气淤质四种体质进行模型的训练和测试，结果表明融合舌象特征和形体特征的中医体质辨识模型能有效地辅助中医体质识别，且支持向量机对四种体质辨识效果总体上优于神经网络。结论 基于客观化的舌象特征和形体特征辅助中医体质辨识有利于提高中医体质辨识的客观化水平，合理选择机器学习算法可以提高中医体质辨识的准确性。     

苏州市初治菌阴肺结核患者就诊延迟现况及临床特征

目的 分析初治菌阴肺结核患者就诊延迟现况及临床特征,为制定有效措施提供科学依据。方法 应用横断面研究,收集苏州市5家结核病定点医院初治菌阴肺结核患者人口学、经济来源、生活方式、结核病知识、主要病史及进入结核病定点医院前发病和就诊等临床资料,采用Logistic回归方法进行统计分析。结果 379例初治菌阴肺结核患者就诊延迟186例,就诊延迟率为49.08%。老年人(OR=1.898,95% CI=1.157~3.112,P=0.011)、日工作时间大于8 h(OR=1.774,95% CI=1.014~3.102, P=0.044)、流动人口(OR=3.252; 95% CI=1.807~5.855, P<0.001)和发热(OR= 2.061,95% CI=1.021~4.160,P=0.043)是初治菌阴肺结核患者发生就诊延迟的危险特征,而咯血(OR= 0.356, 95% CI= 0.164~0.773,P=0.009)是就诊延迟的保护特征。结论 初治菌阴肺结核患者就诊延迟现象较严重,需对不同临床特征的患者采取综合干预措施,减少就诊延迟发生。