基于方剂构成信息的知识发现和关联网络构建挖掘海洋中药海马历代用药经验＊

目的 通过对含海马方剂构成信息进行数据挖掘和知识发现，构建关联网络，分析海马的历代应用情况，挖掘海马用药配伍经验，发现海马潜在功效和主治病症，为海马的临床应用和开发提供依据。方法 以《中医方剂大辞典》《中国药典》《中华医典》等多部著作为方源，收集含有海马的相关方剂，并进行频数分析、关联规则挖掘和关联网络的构建。结果 共筛选有文献记载的含海马方剂154首。从剂型看，大多为散剂，从方剂分布年代来看，海马的应用虽然有很长历史，但是古代应用并不广泛，现代应用较多。通过对配伍药物进行分析发现，与海马配伍频率较高的是“鹿茸”、“熟地黄”、“肉苁蓉”等补益药物。从功效主治方面看，大多数方剂具有“补肾”、“温阳”、“益精”等传统功效外，还具有健脑等功效；主治病症中，除“阳痿”、“癥瘕”、“疔疮”等《中国药典》所记载的疾病之外，还可治疗“虚劳”、“健忘”、“瘿病”、“不寐”、“痹证”、“小便淋沥或不通”等。对其进行关联关系分析发现，海马配伍补肾温阳的效果较为明显，多用于治疗阳痿，且腰痛、虚劳、遗精、健忘、不育等疾病的治疗与补肾关系密切；在治疗癥瘕时，多与醋、樗鸡、干漆、硇砂、斑蝥、水蛭、没药、当归配伍。结论 该研究不仅发现了含海马方剂的常用剂型、分布年代和药物配伍规律，发现了历代医家应用海马的临床经验，还探索了海马的潜在应用价值。海马作为一味海洋中药，具有温、补、托、消等特性，除常用于治疗阳痿外，还可用于治疗腰痛、健忘、瘿病、不寐、痹证等病症，这些研究结果可为拓展海马的临床应用提供理论基础及依据。     

《针灸大成》关于气海穴临床应用的文献研究

目的:通过检索《针灸大成》中与气海穴治疗作用相关的文献条文,总结气海穴在治疗各系统疾病中运用频次较高的疾病及其配穴规律,为临床针灸对气海穴的使用提供理论支持。方法:以《中华医典》(第五版)中《针灸大成》作为文献检索来源,将气海穴及气海穴的别称“脖胦”“下肓”“丹田”“肓之原”“肓原”“下言”和“气泽”为检索词,用计算机检索工具及人工检索相结合的方法检索符合要求的文献条文,通过建立本研究的数据库,频次分析、条形统计图比较分析等方法,总结出气海穴在治疗各系统疾病中的运用频次及其配穴规律。结果:在《针灸大成》所涉及的条文中,气海穴尤善治疗内科疾病,在治疗内科疾病中排名前3位的是脾胃系病症、气血津液疾病、肾系病症和妇科疾病,气海穴配穴习惯为上下配穴法,同名经配穴法,以及前后配穴法,其中主要为前后配穴法和同名经配穴法。结论:气海穴《针灸大成》中单穴应用占比最高,而在气海穴众多配穴中,运用了本经配穴法、上下配穴法、前后配穴法,配穴归经主要来自任脉和足太阳膀胱经。同名经配穴法,同气相求,可增加疗效;与气海穴配伍较多的足太阳膀胱经以背腧穴为主,此为前后配穴法,亦称腹背阴阳配穴法,腹部为阴,腰背为阳,前后配穴法可起到“从阳引阴”亦可“从阴引阳”的作用,以达到调节阴阳,调和脏法,调畅经络的目的。     

基于临床观察文本解析痛经辨治规律研究

目的:研究痛经的中医药辨治规律。方法:检索国家知识基础设施数据库(CNKI)1988—2019年应用中药治疗痛经的相关文献,采用Medcase Ver38诊籍中医师工作室-名老中医经验传承辅助平台,运用文本解析对临床医案进行研究。结果:研究文献131篇,医案104则,涉及病例128次,332诊次,涉及症状92条,病机33条,舌象27条,脉象17条,药物249种。结论:中医药治疗痛经的特异症状为“经行腹痛”,可见症为“夹血块、月经色暗、手足冰冷、经期腰酸”,相关舌脉为“舌质暗红,苔薄白”“脉沉细弦”,核心病机为“瘀血阻滞”,常用治法为“行气化瘀,活血止痛”,常用药物为“当归、白芍、甘草、延胡索、川芎”。     

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.     

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.     

Penalized variable selection for accelerated failure time models with random effects

Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.     

针刺咽部腧穴治疗脑梗死后真性延髓麻痹:随机对照研究

目的:比较针刺患侧与针刺双侧咽部腧穴治疗脑梗死后真性延髓麻痹的临床疗效。方法:将66例脑梗死后真性延髓麻痹患者随机分为单侧组和双侧组,每组33例,最后每组各有1例脱落。两组患者在常规药物和电项针治疗的基础上,单侧组针刺患侧的吞咽、发音和治反流穴,双侧组针刺双侧吞咽、发音和治反流穴。每天1次,每周连续针刺6 d,休息1 d,治疗21 d后评定临床疗效。采用吞咽困难分级量表、洼田饮水试验和GRBAS分级评价两组患者治疗前后的吞咽和构音情况。结果:治疗21 d后,两组患者吞咽困难分级量表评分均提高(均P<0.01),且单侧组改善程度大于双侧组(P<0.01);两组患者GRBAS分级、洼田饮水试验情况均改善(均P<0.01),且单侧组改善程度大于双侧组(P<0.05,P<0.01)。结论:针刺患侧咽部腧穴治疗脑梗死后真性延髓麻痹较针刺双侧咽部腧穴疗效更优。     

大承气汤足浴和足底穴位按摩护理干预改善肿瘤切除术后化疗引起便秘临床观察

目的观察大承气汤足浴和足底穴位按摩护理干预对改善肿瘤切除术引起便秘的效果。方法选择2017年1月—2018年1月收治的60例住院患者,进行肿瘤切除术后随机均分为观察组和对照组,对照组给予一般护理干预,观察组在对照组的基础上使用大承气汤足浴和足底穴位按摩辅助治疗,观察患者术后便秘情况的发生率和治疗效果,对数据进行统计分析。结果对照组和观察组患者的便秘发生率有显著差异(P<0.05),对照组和观察组患者的粪便性状有显著差异(P<0.05)。结论应用大承气汤足浴和足底穴位按摩护理干预可改善肿瘤切除术后引起的便秘情况,值得推广使用。