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Background: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. Methods: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017–2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018–2021) and another from center 2 (n = 43, 2020–2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Results: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. Conclusion: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications. [ABSTRACT FROM AUTHOR]

Background: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. Methods: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. Results: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. Conclusions: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China. [ABSTRACT FROM AUTHOR]

Amatoxins and phallotoxins are toxic cyclopeptides found in the genus Amanita and are among the predominant causes of foodborne sickness and poisoning-related fatalities in China. This study introduces and validates a simple, rapid and cost-effective ultra-performance liquid chromatography–mass spectrometry method for the simultaneous determination and quantification of α-amanitin, β-amanitin, γ-amanitin, phallisacin, phallacidin and phalloidin in human blood and urine. Quick therapeutic decision-making is supported by a 9 min chromatographic separation performed on a Waters Acquity UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 µm) using a gradient of high-performance liquid chromatography (HPLC)-grade water and methanol:0.005% formic acid. The analyte limit of quantification was 1–3 ng/mL in blood and 0.5–2 ng/mL in urine. Calibrations curves, prepared by spiking drug-free blood and urine, demonstrated acceptable linearity with mean correlation coefficients (r) greater than 0.99 for all phallotoxins and amatoxins. Acceptable intraday and interday precision (relative standard deviation <15%) and accuracy (bias, −4.8% to 13.0% for blood and—9.0% to 14.7% for urine) were achieved. The validated method was successfully applied to analyze 9 blood samples and 2 urine samples testing positive for amatoxins and/or phallotoxins. Amatoxins and/or phallotoxins were identified in each whole blood sample at a range of 1.12–5.63 ng/mL and in two urine samples from 1.01–9.27 ng/mL. The method has the benefits of simple sample preparation (protein precipitation) and wide analyte coverage, making it suitable for emergency quantitative surveillance toxicological analysis in clinics and forensic poisoning practice. [ABSTRACT FROM AUTHOR]

• A set of indices are constructed to measure electric vehicle (EV) market resilience. • A conceptual framework is proposed to explore contributors to EV market resilience. • China's provincial EV market resilience and underlying contributors are investigated. • China's EV market resilience presents regional heterogeneity and spatial agglomeration. • Key contributors to China's provincial EV market resilience are identified. This study assesses electric vehicle (EV) market resilience and explores its underlying contributors. We develop a set of indices to measure the resistance, recoverability, and comprehensive resilience of the EV market. A four-dimensional conceptual framework, encompassing market foundation, industrial foundation, policy support and usage environment, is proposed to investigate the potential contributors to the EV market resilience. The models are applied to China's provincial EV markets, and the geographical detector model is used for empirical investigation. We identify an imbalanced distribution of regional EV market resilience, characterized by a spatial clustering of high-performing regions. Notably, a majority of regions encounter challenges in simultaneously achieving high performance in both resistance and recoverability. It is found that contributors across the conceptual framework exhibit varying degrees of influence on resilience, with resistance shaped by inherent factors and recoverability by agency-based factors. However, diverse initiatives may counteract each other, due to interactions among contributors. [ABSTRACT FROM AUTHOR]

While many studies examine the correlates of car ownership, only a few have quantified the relative contributions of individuals' socio-economic characteristics, alternative transportation choices, and built environment attributes to car ownership in developing cities. By employing gradient boosting decision trees to the 2020 regional household travel survey data from Wuhan, a megacity in China, we estimated the respective impacts of these factors on car ownership. The results of this study highlight household wealth as the dominant factor in predicting car ownership. Additionally, the ownership of Ebikes, an alternative travel option, emerges as an important mitigator of car ownership. However, most transit and built environment variables contribute marginally to the prediction of car ownership – though an exception is to direct future development into inner city neighborhoods. The findings suggest that Ebikes not only serve as a crucial alternative to cars but also complement limited transit services. Therefore, in developing cities with growing economies, banning Ebikes would not be conducive to curbing car growth. [ABSTRACT FROM AUTHOR]

Schematic diagram of the dual-gas sensor system, which includes a gas pretreatment part, an optical part and an electrical part. [Display omitted] • A SPGC and a MPGC were developed for achieving wide measurement ranges. • The developed sensor system is intrinsically safe and explosion-proof. • The developed gas pretreatment module can remove moisture and aerosols. • Use temperature compensation algorithm to adapt to changes in ambient temperature. • The sensor system can operate normally in the laboratory and field deployment. To effectively monitor gas explosion and coal spontaneous combustion in coal mine, an intrinsically safe and explosion-proof dual-gas sensor system was developed for methane (CH 4) and carbon monoxide (CO) measurement. A distributed feedback (DFB) laser was used to scan two adjacent absorption lines (CH 4 : 4284.51 cm−1, CO: 4285.01 cm−1). Two measurement ranges of 0–500 parts per million by volume (ppmv) and 0–300,000 ppmv for CH 4 and 0–100 ppmv and 0–4000 ppmv for CO were realized using a ∼25 cm single-pass gas cell (SPGC) and a ∼25 m multi-pass gas cell (MPGC), respectively. A gas pre-treatment module was used to remove moisture and aerosols from gas samples. A temperature compensation algorithm was adopted to deal with ambient temperature change. Based on the Allan deviation analysis, the limit of detection (LoD) of CH 4 is 0.19 ppmv and the LoD of CO is 0.12 ppmv, respectively, at an averaging time of 0.5 s. A continuous two-day CH 4 measurement was performed to demonstrate the long-term stability and environmental monitoring capability. The sensor system was successfully deployed in coal mine gas analysis at Longjiapu, Changchun City, China. [ABSTRACT FROM AUTHOR]