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☐ ☆ ✇ PLOS ONE Medicine&Health

Nonlinear effects of traffic statuses and road geometries on highway traffic accident severity: A machine learning approach

Por: Yao Liang · Hongxia Yuan · Zhenwu Wang · Zhongjin Wan · Tiantian Liu · Bing Wu · Shijie Chen · Xiaobo Tang — Noviembre 22nd 2024 at 15:00

by Yao Liang, Hongxia Yuan, Zhenwu Wang, Zhongjin Wan, Tiantian Liu, Bing Wu, Shijie Chen, Xiaobo Tang

The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualitatively as straight or curved, flat or slope, this study focused on the design elements of road geometry at accident locations. Additionally, this study considers the traffic conditions on the day of the accident, rather than the average annual traffic data as previous studies have done. To achieve this, we collected road design documents, traffic-related data, and 2023 accident data from the Suining section of the G42 Expressway in China. Using this dataset, we tested the classification performance of four machine learning models, including eXtreme Gradient Boosting, Gradient Boosted Decision Tree, Random Forest, and Light Gradient Boosting Machine. The optimal Random Forest model was employed to identify the key factors infulencing traffic accident severity, and the partial dependence plot was introduced to visualize the relationship between severity and various single and two-factor variables. The results indicate that the percentage of trucks, daily traffic volume, slope length, road grade, curvature, and curve length all exhibit significant nonlinear and threshold effects on accident severity. This reveals sepecific road and traffic features associated with varying levels of accident severity along the highway section examined in this study. The findings of this study will provide data-driven recommendations for highway design and daily safety management to reduce the severity of traffic accidents.
☐ ☆ ✇ PLOS ONE Medicine&Health

High-intensity focused ultrasound ablation combined with immunotherapy for treating liver metastases: A prospective non-randomized trial

Por: Xiyue Yang · Yao Liao · Lingli Fan · Binwei Lin · Jie Li · Danfeng Wu · Dongbiao Liao · Li Yuan · Jihui Liu · Feng Gao · Gang Feng · Xiaobo Du — Julio 5th 2024 at 16:00

by Xiyue Yang, Yao Liao, Lingli Fan, Binwei Lin, Jie Li, Danfeng Wu, Dongbiao Liao, Li Yuan, Jihui Liu, Feng Gao, Gang Feng, Xiaobo Du

Purpose

Given the unique features of the liver, it is necessary to combine immunotherapy with other therapies to improve its efficacy in patients of advanced cancer with liver metastases (LM). High-intensity focused ultrasound (HIFU) ablation is now widely used in clinical practice and can enhanced immune benefits. The study is intended to prospectively evaluate the safety and clinical feasibility of HIFU ablation in combination with systemic immunotherapy for patients with liver metastases.

Methods

The study enrolled 14 patients with LM who received ultrasound-guided HIFU ablation combined with immune checkpoint inhibitors (ICIs) such as anti-programmed cell death protein 1 (anti-PD-1 agents manufactured in China) at Mianyang Central Hospital. Patients were followed up for adverse events (AEs) during the trial, using the CommonTerminology Criteria for Adverse Events v5.0(CTCAE v5.0) as the standard. Tumour response after treatment was assessed using computerized tomography.

Results

The 14 patients (age range, 35–84 years) underwent HIFU ablation at 19 metastatic sites and systemic immunotherapy. The mean lesion volume was 179.9 cm3 (maximum: 733.1 cm3). Median follow-up for this trial was 9 months (range: 3–21) months. The study is clinically feasible and acceptable to patients.

Conclusion

This prospective study confirmed that HIFU combined with immunotherapy is clinically feasible and safe for treating liver metastases.

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