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1.
目的 探索贵州省男性主要少数民族骨量异常的影响因素,为骨量异常的防控提供参考依据。 方法 基于中国多民族队列,采用多阶段分层整群抽样方法调查贵州省苗族、布依族、侗族男性共5 727名。采用随机森林算法、非条件logistic回归和限制性立方样条回归探讨骨量异常的影响因素。结果 骨量异常重要的前5名的因素依次是职业、午睡时长、年龄、BMI、静态行为。布依族(OR=1.223,95%CI:1.065~1.405)、年龄≥50岁(OR=1.254,95%CI:1.038~1.515)、吸烟(OR=1.191,95%CI:1.060~1.338)、有关节炎(OR=1.259,95%CI:1.001~1.583)和有骨折史(OR=1.528,95%CI:1.227~1.902)可能是骨量异常的危险因素。而农林牧渔劳动者(OR=0.787,95%CI:0.626~0.990)、午睡时长≥90分钟(OR=0.725,95%CI:0.612~0.858)、中水平体力活动(OR=0.818,95%CI:0.708~0.946)和高水平体力活动(OR=0.824,95%CI:0.696~0.975)可能是贵州省主要男性少数民族骨量异常的保护因素。BMI与骨量异常的患病关联强度呈非线性关系,业余静态行为时长与骨量异常的患病关联强度呈线性关系。结论 影响贵州省男性主要少数民族骨量异常的因素包括多方面,包括非可控因素和可控因素,建议加强对可控因素的管理以预防骨量异常的发生。  相似文献   
2.
目的初步探索特布他林与硝苯地平用于产时胎儿宫内复苏(intrauterine fetal resuscitation,IUFR)的安全性及有效性。方法本研究采用随机对照方法,前瞻性将2021年1月至2021年4月在广州市妇女儿童医疗中心分娩中出现不可靠胎心监护图形(non-reassuring fetal heart rate tracing,NRFHT)的110例孕产妇随机分为特布他林组(硫酸特布他林0.25 mg皮下注射,n=55)和硝苯地平组(硝苯地平10 mg口服,n=55)。收集2组孕妇使用药物前及用药后5、15、30 min的血压、心率、血氧饱和度等血流动力学变化,以及IUFR的成功率、药物起效时间、产后出血率等指标,采用t检验、χ2检验、Fisher精确概率法及秩和检验对数据进行统计学分析。结果特布他林组和硝苯地平组孕妇用药前后平均动脉压及血氧饱和度比较差异均无统计学意义(P值均>0.05);硝苯地平组孕妇用药前后心率无明显变化(P>0.05),而特布他林组孕妇心率在用药后5、15、30 min均快于用药前[(97.0±20.2)、(99.2±13.8)、(91.8±12.6)与(81.7±11.3)次/min,P值均<0.001],但心率增快效应在30 min开始下降,与用药后15 min相比,心率下降了6.4次/min(95%CI:1.5~11.2,P<0.05)。所有孕产妇均未发生需要医疗干预的不良反应。特布他林组有78.2%(43/55)复苏成功,与硝苯地平组的70.9%(39/55)比较差异无统计学意义(χ2=0.77,P=0.381);特布他林组药物起效时间明显快于硝苯地平组[2 min(1~6 min)与6 min(1~10 min),U=2348.50,P<0.001]。2组孕产妇因NRFHT行剖宫产及阴道助产、1 h内再次使用宫缩抑制剂等方面比较差异均无统计学意义(P值均>0.05)。2组产后出血量、产后出血率、新生儿低Apgar评分(≤7分)、低脐动脉pH值(pH<7.2)、新生儿窒息率及新生儿重症监护病房入住率等方面差异均无统计学意义(P值均>0.05)。结论特布他林用于产时IUFR暂未发现明显不良反应;其起效速度快,可作为处理产时紧急IUFR的选择方案。  相似文献   
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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.  相似文献   
4.
AimSkin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research classification system (STAR classification) using digital images via machine learning. This was achieved by introducing shape features representing complicated shape of the skin tears.MethodsA skin tear image was separated into small segments, and features of each segment were estimated. The segments were then classified into different classes by machine learning algorithms, namely support vector machine and random forest. Their performance in classifying wound segments and STAR categories was evaluated with 31 images using the leave-one-out cross validation.ResultsSupport vector machine showed an accuracy of 74% and 69% in classifying wound segments and STAR categories, respectively. The corresponding accuracy using random forest were 71% and 63%.ConclusionMachine learning algorithms revealed capable of classifying categories of skin tears. This could offer the potential to aid nurses in their management of skin tears, even if they are not specialised in wound care.  相似文献   
5.
ObjectiveTo develop a prediction model for survival of patients with coronary artery disease (CAD) using health conditions beyond cardiovascular risk factors, including maximal exercise capacity, through the application of machine learning (ML) techniques.MethodsAnalysis of data from a retrospective cohort linking clinical, administrative, and vital status databases from 1995 to 2016 was performed. Inclusion criteria were age 18 years or older, diagnosis of CAD, referral to a cardiac rehabilitation program, and available baseline exercise test results. Primary outcome was death from any cause. Feature selection was performed using supervised and unsupervised ML techniques. The final prognostic model used the survival tree (ST) algorithm.ResultsFrom the cohort of 13,362 patients (60±11 years; 2400 [18%] women), 1577 died during a median follow-up of 8 years (interquartile range, 4 to 13 years), with an estimated survival of 67% up to 21 years. Feature selection revealed age and peak metabolic equivalents (METs) as the features with the greatest importance for mortality prediction. Using these 2 features, the ST generated a long-term prediction with a C-index of 0.729 by splitting patients in 8 clusters with different survival probabilities (P<.001). The ST root node was split by peak METs of 6.15 or less or more than 6.15, and each patient’s subgroup was further split by age or other peak METs cut points.ConclusionApplying ML techniques, age and maximal exercise capacity accurately predict mortality in patients with CAD and outperform variables commonly used for decision-making in clinical practice. A novel and simple prognostic model was established, and maximal exercise capacity was further suggested to be one of the most powerful predictors of mortality in CAD.  相似文献   
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Background and aimsWhile low-density lipoprotein cholesterol (LDL-C) is a good predictor of atherosclerotic cardiovascular disease, apolipoprotein B (ApoB) is superior when the two markers are discordant. We aimed to determine the impact of adiposity, diet and inflammation upon ApoB and LDL-C discordance.Methods and resultsMachine learning (ML) and structural equation models (SEMs) were applied to the National Health and Nutrition Examination Survey to investigate cardiometabolic and dietary factors when LDL-C and ApoB are concordant/discordant. Mendelian randomisation (MR) determined whether adiposity and inflammation exposures were causal of elevated/decreased LDL-C and/or ApoB. ML showed body mass index (BMI), dietary saturated fatty acids (SFA), dietary fibre, serum C-reactive protein (CRP) and uric acid were the most strongly associated variables (R2 = 0.70) in those with low LDL-C and high ApoB. SEMs revealed that fibre (b = ?0.42, p = 0.001) and SFA (b = 0.28, p = 0.014) had a significant association with our outcome (joined effect of ApoB and LDL-C). BMI (b = 0.65, p = 0.001), fibre (b = ?0.24, p = 0.014) and SFA (b = 0.26, p = 0.032) had significant associations with CRP. MR analysis showed genetically higher body fat percentage had a significant causal effect on ApoB (Inverse variance weighted (IVW) = Beta: 0.172, p = 0.0001) but not LDL-C (IVW = Beta: 0.006, p = 0.845).ConclusionOur data show increased discordance between ApoB and LDL-C is associated with cardiometabolic, clinical and dietary abnormalities and that body fat percentage is causal of elevated ApoB.  相似文献   
7.
背景 新生儿出生体质量与个体健康息息相关,低出生体质量是早期新生儿死亡的高危因素,而巨大儿的发生不仅可增加母婴产时并发症发生风险,还可增加个体成年后罹患各种慢性病的风险。因此,寻找新生儿出生体质量的影响因素十分重要。 目的 探讨孕妇肠道菌群对新生儿出生体质量的影响。 方法 以2017年1—9月在广州市妇女儿童医疗中心出生的516例新生儿及其孕母为研究对象,根据新生儿出生体质量将其分为低出生体质量儿组(LW组,n=24)、正常体质量儿组(NW组,n=479)、巨大儿组(OW组,n=13)。采集孕母的肠道菌群参数及临床实验室检测指标,采用QIIME软件进行孕期肠道菌群组成分析和多样性分析;采用LEfSe分析,分别对三组孕妇肠道菌群属水平上的相对丰度进行两两比较,识别组间具有明显差异的菌群;通过线性模型MaAsLin进行多元分析,以捕获各实验室检测指标与微生物属之间的相关性;通过Boruta随机森林分类器模型分别基于实验室检测指标和肠道菌群分类操作单元(OTUs)构建新生儿出生体质量分类预测模型,探究孕妇肠道菌群对新生儿体质量的影响。 结果 三组孕母的肠道菌群组成分析发现,门水平中厚壁菌门(Firmicutes)物种丰富度最高,属水平里普拉梭菌(Faecalibacterium)明显富集,三组间门水平的香农指数和辛普森指数比较,差异无统计学意义(P>0.05)。三组间的LEfSe分析发现:与LW组比较,NW组链球菌(Streptococcus)和罗氏菌(Roseburia)明显富集(P<0.05),而芽孢杆菌(Bacillaceae)、萝卜属菌(Raphanus)、甲烷球形菌(Methanosphaera)、巴氏杆菌(Barnesiellaceae)、普雷沃氏菌(Paraprevotella)丰度明显降低(P<0.05);与NW组比较,OW组属巨单胞菌(Megamonas)、属粪球菌(Coprococcus)、韦荣氏菌(Veillonellaceae)、cc-115、梭菌(Closrtidiaceae)、另枝杆菌(Alistipes)明显富集(P<0.05),而布劳特氏菌(Blautia)和伊格尔兹氏菌(Eggerthella)丰度明显降低(P<0.05);与LW组比较,OW组Closrtidiaceae、Alistipes菌群明显富集(P<0.05),而Barnesiellaceae丰度明显降低(P<0.05)。基于实验室检测指标分类器模型、肠道菌群OTUs分类器模型,区分NW组与LW组的受试者工作特征曲线下面积(AUC)分别为0.62、0.77,区分NW组与OW组的AUC分别为0.65、0.78。 结论 不同出生体质量新生儿对应孕母的肠道菌群存在差异,孕母肠道菌群OTUs模型可区分新生儿出生体质量的大小,孕妇肠道菌群可能是预测新生儿体质量的一个良好指标。  相似文献   
8.
<正>骨质疏松症(osteoporosis,OP)是一种以骨量低,骨组织微结构损坏,导致骨脆性增加,易发生骨折为特征的全身性骨病~([1])。骨质疏松症最大的危害是容易引起骨折。骨质疏松导致骨折的发生率为7.31%~12.2%,而将近一半的脆性骨折发生在骨量减少者中~([2])。由于骨量丢失通常在无症状的情况下发生~([3]),常  相似文献   
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