p­Phenylenediamine antioxidants in PM2.5: New markers for traffic in positive matrix factorization source apportionment.

The extensive utilization of rubber-related products can lead to a substantial release of p-phenylenediamine (PPD) antioxidants into the environment. In recent years, studies mainly focus on the pollution characteristics and health risks of PM2.5-bound PPDs. This study presents long-time scale data of PPDs and N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) in PM2.5 and proposes the innovative use of PPDs as new markers for vehicular emissions in the Positive Matrix Factorization (PMF) source apportionment. The results indicate that PPDs and 6PPD-Q were detectable in 100 % of the winter PM2.5 samples, and the concentration ranges of PPDs and 6PPD-Q are 15.6-2.92 × 103 pg·m-3 and 3.90-27.4 pg·m-3, respectively, in which 6PPD and DNPD are the main compounds. Moreover, a competitive formation mechanism between sulfate, nitrate, ammonium (SNA) and 6PPD-Q was observed. The source apportionment results show that the incorporation of PPDs in PMF reduced the contribution of traffic source to PM2.5 from 13.5 % to 9.5 %. In the traffic source factor profiles, the load of IPPD, CPPD, DPPD, DNPD and 6PPD reaches 91.8 %, 91.6 %, 92.9 %, 80.6 % and 87.2 %, respectively. It`s amazing that traditional markers of traffic source, which often overlap with coal burning and industrial sources, over-estimated the contribution of vehicles by one third or more. The discovery of PPDs as specific markers for vehicular emissions holds significant utility, particularly considering the growing proportion of new energy vehicles in the future. The results may prove more accurate policy implications for pollution control. SYNOPSIS: PPDs are excellent indicators of vehicle emissions, and PMF without PPDs over-estimated the contribution of traffic source to PM2.5.

Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules.

Background: The prediction of the persistent pure ground-glass nodule (pGGN) growth is challenging and limited by subjective assessment and variation across radiologists. A chest computed tomography (CT) image-based deep learning classification model (DLCM) may provide a more accurate growth prediction. Methods: This retrospective study enrolled consecutive patients with pGGNs from January 2010 to December 2020 from two independent medical institutions. Four DLCM algorithms were built to predict the growth of pGGNs, which were extracted from the nodule areas of chest CT images annotated by two radiologists. All nodules were assigned to either the study, the inner validation, or the external validation cohort. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUROCs) were analyzed to evaluate our models. Results: A total of 286 patients were included, with 419 pGGN. In total, 197 (68.9%) of the patients were female and the average age was 59.5±12.0 years. The number of pGGN assigned to the study, the inner validation, and the external validation cohort were 193, 130, and 96, respectively. The follow-up time of stable pGGNs for the primary and external validation cohorts were 3.66 (range, 2.01-10.08) and 4.63 (range, 2.00-9.91) years, respectively. Growth of the pGGN occurred in 166 nodules [83 (43%), 39 (30%), and 44 (45%) in the study, inner and external validation cohorts respectively]. The best-performing DLCM algorithm was DenseNet_DR, which achieved AUROCs of 0.79 [95% confidence interval (CI): 0.70, 0.86] in predicting pGGN growth in the inner validation cohort and 0.70 (95% CI: 0.60, 0.79) in the external validation cohort. Conclusions: DLCM algorithms that use chest CT images can help predict the growth of pGGNs.

Associations between intimate partner violence with emotion regulation and selfefficacy among young students with love experience / 中国学校卫生

Objective@#To understand the current situation of intimate partner violence (IPV) among young students in Chengdu and its relationship with emotion regulation self-efficacy,and to provide a reference for conducting the education on close relationship.@*Methods@#Totally 1 041 young students with love experience in Chengdu were selected by by stratified cluster random sampling to explore potentional factors related to IPV.@*Results@#The incidence of IPV perpetration among young students with love experience was as high as 69.6% and the incidence of IPV victimization was 62.2%. Young students had committed(65.4%) or been subjected(64.0%) to more than three intimate partner violence. 59.92% young students were both perpetrators and victims of IPV. Multiple Logistic regression analysis showed that compared with young female students, young male students were not prone to commit violence in intimate relationships(OR=0.59), but may become victims of IPV(OR=1.91). More than half a year in love(OR=1.70), cohabitation(OR=2.47), bullying by peers (OR=1.54) and interference by parents (OR=1.63) were risk factors for IPV perpetration. Among them, more than half a year in love (OR=1.51) and cohabitants (OR=2.52) were positively associated with IPV victimization. The efficacy of managing negative emotions was a negatively associated with IPV perpetration (OR=0.96) and victimization(OR=0.97)(P<0.05).@*Conclusion@#The phenomenon of intimate partner violence among young students is more common, which is closely related to the rearing style of young students, peer relationship, love relationship and the ability to manage negative emotions, which should be paid attention to.