Investigation into pedestrian exposure to traffic PM around grade separations: a case study in Xi’an,China

To investigate the influence of grade separations on the concentration dynamics of particulate matter (PM) and pedestrian PM exposure rate, PM mass concentrations (PMCs) at and around a typical grade separation were measured. Using a statistical fitting method, the horizontal variation and frequency distributions for PMCs at the entrance and exit of a grade separation were investigated. The pedestrian exposure around the grade separation was quantified in terms of respiratory deposition dose (RDD) rates. The results of the case study showed that the average mass concentrations of PM10, PM2.5, and PM1 at the exit of the underpass were found to be about 19, 9, and 4% higher than those at the entrance, and the concentrations of PM10, PM2.5, and PM1 in the underpass were higher compared with those on the overpass. The average mass concentrations of PM10 (136 μg m^?3), PM2.5 (67 μg m^?3), and PM1 (44 μg m^?3) in the underpass exceeded those on the overpass by about 14, 8, and 7%, respectively. Further analysis of the underpass revealed that all three types of PMCs decayed exponentially with increasing distance from the exit. However, for the entrance, as the distance from the entrance increased, PMCs began to drop slightly, and then rose to the maximum near the ramp. Based on the frequency histograms and corresponding fitted curves at the entrance and exit of the underpass, the rate of violation of ambient PM concentrations was assessed. The total respiratory deposition dose (RDD) rates of PM10, PM2.5, and PM1 around the grade separation were roughly 3.4, 2.8, and 2.5 times higher than those of the background, respectively. The findings of this study are a step toward understanding more environmentally friendly grade-separation design strategies.     

Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups

Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit.     

Quantitative Assessment of Skin Stiffness Using Ultrasound Shear Wave Elastography in Systemic Sclerosis

This study was aimed at investigating the performance of ultrasound shear wave elastography (US-SWE) in the assessment of skin (the dermis) stiffness in patients with systemic sclerosis (SSc). The thickness and elastic modulus of the skin were measured using US-SWE at 6 sites in 60 SSc patients and 60 healthy volunteers: the bilateral middle fingers and forearms and the anterior chest and abdomen. To evaluate clinical scores, the measurements were also extended to 17 skin sites in 30 patients. The diagnostic performance of US-SWE in the differentiation of SSc from healthy skin was determined by receiver operating characteristic (ROC) curve analysis, and the reliability of the measurement was evaluated with intra- and inter-class correlation coefficients. The results of US-SWE were compared with modified Rodnan skin thickness scores. Our results indicated that (i) the elastic modulus values were significantly higher in SSc patients than in controls, with or without normalization by skin thickness; (ii) receiver operating characteristic analysis revealed normalized US-SWE cutoff values with a very high accuracy for right and left fingers (areas under the curve?=?0.974 and 0.949), followed by left forearm (0.841), anterior abdomen (0.797), right forearm (0.772) and anterior chest (0.726); (iii) the reliability of US-SWE measurements was good for all examined sites with intra-observer correlation coefficients of 0.845–0.996 and inter-observer correlation coefficients of 0.824–0.985; and (iv) total scores of skin involvement determined at 17 sites (modified Rodnan skin thickness scores) correlated with skin stiffness (r?=?0.832) and thickness (r?=?0.736). In conclusion, US-SWE is a quantitative method with high specificity, sensitivity and reliability in the detection of SSc involvement. This non-invasive, real-time and operator-independent imaging technique could be an ideal tool for the assessment of SSc disease.     

原发性甲减并发髋关节侵蚀性骨关节炎1例

正患者,王某,女,46岁,半年前无明显诱因出现双髋部疼痛,右侧重,跛行明显,偶夜间痛醒,无晨僵,无肌痛和肌无力。期间经抗炎治疗后症状减轻,此后反复间歇性发作,因疼痛进行性加重3月收入院治疗。体格检查发现头发、眉毛稍稀疏,反应迟钝,语速及对答反应较慢,体型偏胖偏矮,全身未见皮疹,颜面、双下肢轻度非凹陷性水肿,指甲无脱落。双髋关节内、外旋受限,屈曲、外展受限,双侧4字试验(+)。