[Landscape Pattern Vulnerability and Its Driving Forces in Different Geomorphological Divisions in the Middle Yellow River].

The ecological environment of the middle Yellow River is highly vulnerable. Conducting a scientific assessment of landscape pattern vulnerability holds great significance, as it serves as the basis for the rational construction of the ecological environment in this area. Based on five periods of land use data from the middle Yellow River from 1990 to 2018, the landscape pattern vulnerability index was employed to analyze the spatio-temporal evolution of the landscape pattern vulnerability. Furthermore, the influencing factors for landscape pattern vulnerability in different natural geomorphological divisions were explored using an optimal parameters-based geographical detector model. The results showed that:① From 1990 to 2018, cultivated land (which accounted for 36.96 % to 39.97 % of the area) remained the predominant landscape in the middle Yellow River. Among all landscape types, cultivated land and construction land exhibited the most significant changes. The area of cultivated land decreased by 10 185.00 km2, whereas the area of construction land increased by 7 678.46 km2. ② From 1990 to 2018, the landscape pattern was dominated by low and medium vulnerability and accounted for 70 %-80 % of the total area. The high and higher vulnerability areas were concentrated in the loess hilly and gully region, whereas the lower vulnerability area was concentrated in the valley plain and the earth-rock mountain regions. During this period, landscape pattern vulnerability underwent an incipient decrease, followed by a subsequent increase. From 1990 to 2000 and from 2000 to 2005, the changes in the level of landscape pattern vulnerability were dominated by a "reduction in the degree of vulnerability". However, from 2005 to 2010 and from 2010 to 2018, it was mainly an "increase in the degree of vulnerability". ③ Annual precipitation and NDVI were the main factors influencing the vulnerability of landscape patterns, whereas the influencing factors varied across different natural geomorphological divisions:the loess hilly and gully region and the earth-rock mountain region were dominated by natural factors, with annual precipitation and DEM being the dominant factors, respectively; the loess plateau tableland-gully region, valley plain region, and sandy land and desert region were dominated by human factors, with population density, degree of land use, and distance from roads being the dominant factors, respectively. The interaction results of any two influencing factors were manifested as two-factor enhancement or nonlinear enhancement. Risk detection revealed that high vulnerability areas of landscape patterns in different natural geomorphological divisions were distributed over distinct ranges of their corresponding dominant factors. Therefore, in the practices of ecological management in the middle Yellow River, appropriate management strategies should be implemented based on the vulnerability characteristics of different natural landforms, to further improve the ecological management level of the watershed.

Dynamic change in the peritoneal cancer index based on CT after chemotherapy in the overall survival prediction of gastric cancer patients with peritoneal metastasis.

PURPOSE: The purpose of this research was to investigate the efficacy of the CT-based peritoneal cancer index (PCI) to predict the overall survival of patients with peritoneal metastasis in gastric cancer (GCPM) after two cycles of chemotherapy. METHODS: This retrospective study registered 112 individuals with peritoneal metastasis in gastric cancer in our hospital. Abdominal and pelvic enhanced CT before and after chemotherapy was independently analyzed by two radiologists. The PCI of peritoneal metastasis in gastric cancer was evaluated according to the Sugarbaker classification, considering the size and distribution of the lesions using CT. Then we evaluated the prognostic performance of PCI based on CT, clinical characteristics, and imaging findings for survival analysis using multivariate Cox proportional hazard regression. RESULTS: The PCI change ratio based on CT after treatment (ΔPCI), therapy lines, and change in grade of ascites were independent factors that were associated with overall survival (OS). The area under the curve (AUC) value of ΔPCI for predicting OS with 0.773 was higher than that of RECIST 1.1 with 0.661 (P < 0.05). Patients with ΔPCI less than -15% had significantly longer OS. CONCLUSION: CT analysis after chemotherapy could predict OS in patients with GCPM. The CT-PCI change ratio could contribute to the determination of an appropriate strategy for gastric cancer patients with peritoneal metastasis.