Temporal trends and outcomes in acute ischaemic stroke patients with a current or historical diagnosis of cancer.

BACKGROUND AND PURPOSE: The aim was to evaluate the temporal trends, characteristics and in-hospital outcomes of patients hospitalized with acute ischaemic stroke (AIS) between those with and without current or historical malignancies. METHODS: Adult hospitalizations with a primary diagnosis of AIS were identified from the National Inpatient Sample database 2007-2017. Logistic regression was used to compare the differences in the utilization of AIS interventions and in-hospital outcomes. For further analysis, subgroup analyses were performed stratified by cancer subtypes. RESULTS: There were 892,862 hospitalizations due to AIS, of which 108,357 (12.14%) had a concurrent diagnosis of current cancer (3.41%) or historical cancer (8.72%). After adjustment for confounders, patients with current malignancy were more likely to have worse clinical outcomes. The presence of historical cancers was not associated with an increase in poor clinical outcomes. Additionally, AIS patients with current malignancy were less likely to receive intravenous thrombolysis (adjusted odds ratio 0.66, 95% confidence interval 0.63-0.71). Amongst the subgroups of AIS patients treated with intravenous thrombolysis or mechanical thrombectomy, outcomes varied by cancer types. Notably, despite these acute stroke interventions, outcome remains poor in AIS patients with lung cancer. CONCLUSIONS: Although AIS patients with malignancy generally have worse in-hospital outcomes versus those without, there were considerable variations in these outcomes according to different cancer types and the use of AIS interventions. Finally, treatment of these AIS patients with a current or historical cancer diagnosis should be individualized.

Microplastic contamination and risk assessment in blue shark (Prionace glauca) from the eastern tropical Pacific Ocean.

We quantified the abundance and characteristics of microplastics in the blue shark, Prionace glauca, found in the eastern tropical Pacific Ocean and investigated the potential microplastic pollution risks. Microplastics (MPs) were detected in 39.1 % of specimens, up to 0.15 ± 0.38 items/g wet weight of the posterior part of the pylorus, and were sized 45.87 to 3220.12 µm. The majority were fibrous in shape (83.3 %) and blue in color (72.2 %). Both sexes of sharks had similar MP abundance and characteristics, except for polymers, with polyethylene terephthalate and polypropylene representing the dominant type in males and females, respectively. Most individuals experienced low pollution, but one male P. glauca exhibited a high ecological risk level owing to the high MP abundance and detection of polyvinyl chloride. This study provides an important baseline for the ingestion of microplastics by pelagic shark species and is a preliminary quantitative measure that could be used in future studies of the risk of MPs.

A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China.

China experiences frequent landslides, and therefore there is a need for landslide susceptibility maps (LSMs) to effectively analyze and predict regional landslides. However, the traditional methods of producing an LSM are unable to account for different spatial scales, resulting in spatial imbalances. In this study, Zigui-Badong in the Three Gorges Reservoir Area was used as a case study, and data was obtained from remote sensing images, digital elevation model, geological and topographic maps, and landslide surveys. A geographic weighted regression (GWR) was applied to segment the study area into different spatial scales, with three basic principles followed when the GWR model was applied for this propose. As a result, 58 environmental factors were extracted, and 18 factors were selected as LSM factors. Three of the most important factors (channel network basic level, elevation, and distance to river) were used as segmentation factors to segment the study area into 18 prediction regions. The particle swarm optimization (PSO) algorithm was used to optimize the parameters of a support vector machine (SVM) model for each prediction region. All of the prediction regions were merged to construct a GWR-PSO-SVM coupled model and finally, an LSM of the study area was produced. To verify the effectiveness of the proposed method, the outcomes of the GWR-PSO-SVM coupled model and the PSO-SVM coupled model were compared using three evaluation methods: specific category accuracy analysis, overall prediction accuracy analysis, and area under the curve analysis. The results for the GWR-PSO-SVM coupled model for these three evaluation methods were 85.75%, 87.86%, and 0.965, respectively, while the results for the traditional PSO-SVM coupled model were 68.35%, 84.44%, and 0.944, respectively. The method proposed in this study based on a spatial scale segmentation therefore acquired good results.