Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004–2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m3), secondary inorganic aerosol (11.1 ± 5.0 μg/m3), and mineral dust (11.1 ± 7.9 μg/m3). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m3 over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 μg/m3) could be almost as large as from fossil fuel combustion sources (17 μg/m3). These estimates offer information about global population exposure to the chemical components and sources of PM2.5.

Satellite derived NO 2 column data have been used to study Chinese national fossil fuel consumption and pollutant emissions. Based on NO 2 retrievals from two satellites (GOME and SCIAMACHY) for 1996-2010, we analyzed the characteristics and evolution of regional pollution related to NO x emissions in China. Satellite observations indicated that the highly polluted regions were expanding. Anthropogenic emission dominated areas have expanded from the east to central and western China, and new highly polluted regions have formed throughout the nation. Bottom-up emission estimates suggested a 133% increase in anthropogenic NO x emissions in East Central China during 1996 to 2010, which was lower than the 184% increase of the NO 2 columns measured by the satellites. We found that growth rates of NO x emissions have slowed in Chinese megacities over recent years, in contrast to which, the NO x emissions were soaring in medium-sized cities, indicating that strict controls of NO x emissions from coal-fired facilities are required in China.

Background Lymph‐vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. Purpose To develop and validate an axial T 1 contrast‐enhanced (CE) MR‐based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. Study Type Retrospective. Population In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. Field Strength/Sequence T 1 CE MRI sequences at 1.5T. Assessment Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. Statistical Tests The Mann–Whitney U ‐test and the chi‐square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum‐redundancy/maximum‐relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. Results Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non‐LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. Data Conclusion T 1 CE MR‐based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. Level of Evidence : 4 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2019;49:1420–1426.

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The migration of CD4+ T cells plays an important role in arteriosclerosis obliterans (ASO). However, the molecular mechanisms involved in CD4+ T cell migration are still unclear. The current study is aimed to determine the expression change of miR-142-3p in CD4+ T cells from patients with ASO and investigate its role in CD4+ T cell migration as well the potential mechanisms involved. We identified by qRT-PCR and in situ hybridization that the expression of miR-142-3p in CD4+ T cells was significantly down-regulated in patients with ASO. Chemokine (C-X-C motif) ligand 12 (CXCL12), a common inflammatory chemokine under the ASO condition, was able to down-regulate the expression of miR-142-3p in cultured CD4+ T cells. Up-regulation of miR-142-3p by lentivirus-mediated gene transfer had a strong inhibitory effect on CD4+ T cell migration both in cultured human cells in vitro and in mouse aortas and spleens in vivo. RAC1 and ROCK2 were identified to be the direct target genes in human CD4+ T cells, which are further confirmed by dual luciferase assay. MiR-142-3p had strong regulatory effects on actin cytoskeleton as shown by the actin staining in CD4+ T cells. The results suggest that the expression of miR-142-3p is down-regulated in CD4+ T cells from patients with ASO. The down-regulation of miR-142-3p could increase the migration of CD4+ T cells to the vascular walls by regulation of actin cytoskeleton via its target genes, RAC1 and ROCK2.

Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

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Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis. Methods: A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. Results: In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. Conclusions: The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.