Stature estimation for Saudi men based on different combinations of upper limb part dimensions

Estimating stature based on body/limb parts can help define the characteristics of unidentified bodies. The most studied upper limb part is the hand, although few studies have examined whether stature can be estimated using fingers plus other hand dimensions. Moreover, there is paucity in anthropometric studies that determined whether bilateral whole limb parts (e.g., arms, forearms, and hands) are related to stature among the living subjects.This prospective cross-sectional study aimed to evaluate the relationship between different upper limb measurements and the stature of Saudi men. Furthermore, I assessed whether upper limb asymmetry was present, and developed regression models to estimate stature based on different available measurements. Stature and 13 upper limb parameters were measured for 100 right-handed Saudi men who were 18 to 24 years old.All measurements were positively correlated with stature (P < .001), and the best single predictor was the bilateral ulnar length. Asymmetry was more pronounced in the hand measurements. A multiparameter model provided reasonable predictive accuracy (±3.77–5.68 cm) and was more accurate than single-parameter models. Inclusion of the right-side fingers improved the model''s accuracy.This study developed potential models for estimating stature during the identification of bodies of Saudi men.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

2009-2019年西安市肺结核流行特征及治疗转归分析

目的 了解2009-2019年西安市肺结核的流行特征及治疗转归情况，为完善西安市肺结核防治策略提供依据。方法 收集2009-2019年《传染病信息管理系统》中登记的肺结核患者病案信息数据，通过描述性研究方法对肺结核流行病学特征和治疗转归情况进行分析。结果 2009-2019年间西安市肺结核年平均发病率为49.06/10万。郊县、郊区、城区肺结核年平均发病率分别为54.13/10万、47.46/10万和46.19/10万；男性发病率是女性的1.85倍。职业分布排在前5位的为农民（47.48%），家政、家务及待业（14.65%），离退人员（9.55%），学生（8.63%）和工人（5.23%）。利福平敏感或耐药性未知患者的成功治疗率平均为98.03%，利福平耐药患者治疗成功率为51.47%。结论 西安市近年来肺结核发病率和治疗率呈增长趋势，利福平耐药患者治疗成功率较低，要加强对重点人群的健康促进工作，加大肺结核发现力度及患者治疗管理工作。     

新生儿重症监护病房中重症肺炎多种菌感染相关因素分析

目的分析新生儿重症监护病房(NICU)中重症肺炎新生儿多种菌感染危险因素。方法回顾分析2014年1月至2019年5月收入NICU的1 057例重症细菌肺炎新生儿的临床资料,分析其多种菌感染的危险因素。结果单因素分析显示,胎龄(37周)、出生体质量(2 500 g)、发病日龄(7 d)、羊水污染(Ⅱ、Ⅲ度)、感染类型(医院感染)、住院时间(≥14天)、机械通气、Apagar评分(7分)、胎膜早破、抗菌药物使用天数(≥10天)、更换抗菌药物(≥3种)、联合使用抗菌药物(≥3种)12个因素是NICU中重症肺炎新生儿多种菌感染的危险因素,差异有统计学意义(P均0.05)。多因素分析显示,更换抗菌药物(≥3种)、Apagar评分(7分)、感染类型(医院感染)、机械通气、羊水污染(Ⅱ、Ⅲ度)是NICU中重症肺炎新生儿多种菌感染的独立危险因素(P0.05)。结论临床应针对主要危险因素采取综合防控措施,减少NICU新生儿重症肺炎多种菌感染。     

Differentiating benign from malignant cardiac tumors with cardiac magnetic resonance imaging

Background

The purpose of this analysis is to describe the differences in cardiac magnetic resonance characteristics between benign and malignant tumors, which would be helpful for surgical planning.

Geographical Origin and Level Identification of Frankincense Based on Hyperspectral Image

Background: As the demand for traditional Chinese medicinal materials increases in China and even the world, there is an urgent need for an effective and simple identification technology to identify the origin and quality of the latter and ensure the safety of clinical medication. Mineral element analysis and isotope finger-printing are the two commonly used techniques in traditional origin identification. Both of these techniques require the use of stoichiometric methods in the identification process. Although they have high accuracy and sensitivity, they are expensive and inefficient. In addition, near-infrared spectroscopy is a fast, nondestructive, and widely used identification technique developed in recent years, but its identification results are susceptible to samples’ states and environmental conditions, and its sensitivity is low. Hyperspectral imaging combines the advantages of imaging technology and optical technology, which can simultaneously access the image information and spectral information which reflect the external characteristics, internal physical structure, and chemical composition of the samples. Hyperspectral imaging is widely applied to agricultural product inspection, but research into its application in origin and quality identification of TCM materials is rare. Methods: In this study, the algorithm framework discriminative marginalized least squares regression (DMLSR) was used for feature extraction of frankincense hyperspectral data. The DMLSR with intraclass compactness graph and manifold regularization can efficiently learn the projective samples with higher separability and less redundant information than the original samples. Then, the discriminative collaborative representation with Tikhonov regularization (DCRT) was applied for classifying the geographical origin and level of frankincense. DCRT introducesthe discriminant regularization term and incorporates SID, which is more sensitive to the spectrum as the measurement method and is more suitable for the frankincense spectral data compared with SVM. Results: For the origin classification task, samples of all levels from each origin were, respectively, selected for three?way classification. We used 10-fold cross-validation to select a model parameter in the experiment. When obtaining the optimal parameters, we randomly selected the training set and testing set, where the training set accounts for 70% and the training set for 30%. After repeating this random process 10 times, we obtained the final average classification accuracy, which is higher than 90%, and the standard deviation fluctuation is usually small. For the level classification task, samples of each level from three origins were separately selected for multiclassification. We randomly selected the training set and testing set from each origin. The level classification results of the three origins are good on D4350 data, and the classification accuracy of each level is basically above 80%. Conclusion: Experiments and analysis show that our algorithm framework has excellent classification performance, which is stable in origin classification and has potential for generalization. In addition, the experiments show that in our algorithm framework, different classification tasks need to combine different data sources to achieve better classification and recognition, as the origin classification task uses frankincense’s D3000 data, and level classification task uses frankincense’s D4350 data.     

影响重型肝炎预后的因素分析

目的研究重型肝炎的各种预后因素的重要性。方法选择2000年1月至2004年12月我院收治的重型肝炎患者358例,分为好转治愈组和恶化死亡组,将两组的临床资料进行单因素和多因素分析。结果两组之间在凝血酶原活动度、肝性脑病、总胆红素和天冬氨酸氨基转移酶/丙氨酸氨基转移酶等方面有显著性差异(P<0.05)。结论凝血酶原活动度、肝性脑病、总胆红素和天冬氨酸氨基转移酶/丙氨酸氨基转移酶比值等可以作为判断重型肝炎预后的指标,对指导临床有一定价值。     

Comparison of diagnostic usefulness of predictive models in preliminary differentiation of adnexal masses

The purpose of this study was to compare prognostic models evaluating the probability of an ovarian cancer occurrence based on a number of clinical and ultrasonographic data in women with adnexal masses. A total of 686 women with adnexal masses underwent the examinations between 1994 and 2002. The recorded parameters included: age, menopausal status, body mass index, the grayscale and Doppler ultrasonographic examination, and selected markers concentration levels. In order to find the best combination of features, which significantly influences the probability of malignancy, stepwise logistic regression analysis, as well as artificial neural network, was used. The diagnostic efficiency of received models was estimated and compared using receiver-operating characteristics (ROC) curve. The results indicate that 431 and 255 patients had a benign and malignant ovarian tumor, respectively. Application of stepwise logistic regression analysis revealed statistically significant importance of eight features. The sensitivity and specificity for the received model were 65.71% and 77.59%, respectively. Three-layer perceptron network shows 13 features as significant predictors of malignancy. The network gave a sensitivity of 85.7% and specificity of 93.1%. Comparison of area under ROC curve for received models was 0.9679 vs 0.9716. Prognostic values of the analyzed neural model are not optimal but seem to surpass logistic regression model in terms of the predictive possibilities.