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Taking the lower Yellow River basin as the study area, this study aims to construct ecological networks to mitigate the negative impacts of rapid urbanization on the ecosystem. Ecological sources were identified based on morphological spatial pattern analysis (MSPA), habitat quality and landscape connectivity. The ecological resistance surface was constructed and corrected by integrating natural and anthropogenic factors. The spatial range of ecological corridors and some of their key nodes were identified based on circuit theory. The ecological network (EN) was finally optimized using a similarity search and cost connectivity modules. The results show that the optimized ecological network structure is more stable than before. The EN includes 23 ecological sources with a total area of 5464.8 km 2 and 30 ecological corridor clusters with a total area of 2205.92 km 2 . Through the internal landscape heterogeneity of the corridor, 28 ecological node areas and 75 barrier areas were identified as key protection and restoration areas, with a total area of 78.44 km 2 and 372.79 km 2 , respectively. Through the construction and optimization of EN, this study identifies key areas for promoting ecological sustainability and provides a useful framework for coordinating regional ecological conservation and economic development.

Intense human activities have led to profound changes in landscape patterns and ecological processes, generating certain ecological risks that seriously threaten human wellbeing. Ecological risk assessment from a landscape perspective has become an important tool for macroecosystem landscape management. This research improves the framework and indices of the ecological risk assessment from a landscape perspective, evaluates the land use pattern and landscape ecological risk dynamics in the Yellow River Ecological Economic Belt (YREEB), analyzes the spatiotemporal variation, and identifies key areas for ecological risk management. The results indicate the following: The main land use types in the region are grassland and cropland, but the area of cropland and grassland decreased during the study period, and with the accelerated urbanization, urban land is the only land use type that continued to increase over the 20-year period. The ecological risk in the YREEB tended to decrease, the area of low ecological risk zones increased, while the area of high ecological risk zones gradually decreased. Most areas are at medium risk level, but the risk in central Qinghai and Gansu is obviously higher, and there is a dispersed distribution of local high- and low-risk zones. A total of 37.7% of the study area is identified as critical area for future risk management, and the potential for increased risk in these areas is high. These results can provide a basis for sustainable development and planning of the landscape and the construction of ecological civilization in ecologically fragile areas.

Pulmonary alveolar proteinosis (PAP) is a rare lung disorder in which defects in alveolar macrophage maturation or function lead to the accumulation of proteinaceous surfactant in alveolar space, resulting in impaired gas exchange and hypoxemia. PAP is categorized into three types: hereditary, autoimmune, and secondary. We report a case of secondary PAP in a 47-year-old man, whose risk factors include occupational exposure to inhaled toxins, especially aluminum dust, the use of anabolic steroids, and alcohol abuse, which in mice leads to alveolar macrophage dysfunction through a zinc-dependent mechanism that inhibits granulocyte macrophage-colony stimulating factor (GM-CSF) receptor signalling. Although the rarity and vague clinical presentation of PAP can pose diagnostic challenges, clinician awareness of PAP risk factors may facilitate the diagnostic process and lead to more prompt treatment.

Agriculture, as an important activity on which human beings depend for their livelihood, brings serious environmental problems while meeting the needs of human survival, among which agricultural non-point source (NPS) pollution is one of the most urgent environmental problems. This study quantitatively assessed the loading characteristics spatial and temporal evolution patterns of two agricultural NPS pollutants, chemical oxygen demand (COD) and ammonia nitrogen (NH3-N), from 2010 to 2020 in the Nansi Lake Basin as an example, and constructed a system dynamics (SD) simulation model to simulate and analyze agricultural NPS pollution under different development and treatment scenarios, based on an investigation of the regional prevention and control strategy of agricultural NPS pollution and the technological system. The results show that the current status of agricultural NPS pollution load in the Nansi Lake Basin is poor, and the level of pollution load is high, showing obvious geographical differences. In terms of temporal changes, the pollution loads of the two pollutants showed a decreasing trend from 2010 to 2020, among which the pollution load of NH3-N showed the largest change. Spatially, the spatial distribution of each type of pollutant has some similarities, with smaller pollution loads in Jining and Zaozhuang and relatively larger pollution loads in Heze and Ningyang. The main source of COD pollution in the Nansi Lake Basin is rural life, with an emission proportion of 52.85%, and the main sources of NH3-N pollution from agricultural NPS pollution in the area are rural life and livestock and poultry farming, with emission proportions of 47.55% and 35.36%, respectively. Under the status quo continuum scenario, the pollution load values for COD are consistently higher than those for NH3-N, so the relative impact of COD is greater. In this study, the principles and methods of SD in system science are adopted to deal with the agricultural NPS pollution of Nansi Lake Basin, and the evolution of its behavioral characteristics are simulated, forecasted, and predicted, and policy experiments are conducted, with a view to providing references for the prevention and control of agricultural NPS pollution in Nansi Lake Basin and further research.

Diagnosing pulmonary embolism (PE) in the emergency department (ED) can be challenging because its signs and symptoms are non-specific. We compared the efficacy and safety of using age-adjusted D-dimer interpretation, clinical probability-adjusted D-dimer interpretation and standard D-dimer approach to exclude PE in ED patients. We performed a health records review at two emergency departments over a two-year period. We reviewed all cases where patients had a D-dimer ordered to test for PE or underwent CT or VQ scanning for PE. PE was considered to be present during the emergency department visit if PE was diagnosed on CT or VQ (subsegmental level or above), or if the patient was subsequently found to have PE or deep vein thrombosis during the next 30 days. We applied the three D-dimer approaches to the low and moderate probability patients. The primary outcome was exclusion of PE with each rule. Secondary objective was to estimate the negative predictive value (NPV) for each rule. 1163 emergency patients were tested for PE and 1075 patients were eligible for inclusion in our analysis. PE was excluded in 70.4% (95% CI 67.6–73.0%), 80.3% (95% CI 77.9–82.6%) and 68.9%; (95% CI 65.7–71.3%) with the age-adjusted, clinical probability-adjusted and standard D-dimer approach. The NPVs were 99.7% (95% CI 99.0–99.9%), 99.1% (95% CI 98.3–99.5%) and 100% (95% CI 99.4–100.0%) respectively. The clinical probability-adjusted rule appears to exclude PE in a greater proportion of patients, with a very small reduction in the negative predictive value.

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

Metastasis is a major cause of high recurrence and poor survival of patients with colorectal cancer (CRC), although the mechanisms associated with this process remain poorly understood. In this study, we report a novel mechanism by which SOX13 promotes CRC metastasis by transactivating SNAI2 and c-MET. SOX13 overexpression was significantly correlated with more aggressive clinicopathological features of CRC and indicated poor prognosis in two independent cohorts of CRC patients (cohort I, n = 363; cohort II, n = 390). Overexpression of SOX13-promoted CRC migration, invasion, and metastasis, whereas SOX13 downregulation caused the opposite effects. Further mechanistic investigation identified SNAI2 and MET as important target genes of SOX13 using serial deletion and site-directed mutagenesis luciferase reporter and chromatin immunoprecipitation (ChIP) assays, as well as functional complementation analyses. In addition, SOX13 was shown to be a direct target of HGF/STAT3 signaling, and the c-MET inhibitor crizotinib blocked the HGF/STAT3/SOX13/c-MET axis, significantly inhibiting SOX13-mediated CRC migration, invasion and metastasis. Moreover, in clinical CRC tissues, SOX13 expression was positively correlated with the expression of SNAI2, c-MET, and HGF. CRC patients with positive coexpression of SOX13/SNAI2, SOX13/c-MET, or HGF/SOX13 exhibited a worse prognosis. In summary, SOX13 is a promising prognostic biomarker in patients with CRC, and blocking the HGF/STAT3/SOX13/c-MET axis with crizotinib could be a new therapeutic strategy to prevent SOX13-mediated CRC metastasis.

Background The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8 + T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. Methods Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8 + TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan–Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts. Results The percentage of PD-1 + CD8 + TILs, TIM-3 + CD8 + TILs, PD-L1 + CD8 + TILs, and PD-L1 + CD8 + PBLs and the density of PD-L1 + CD8 + TILs were independent risk factors, while the percentage of TIM-3 + CD8 + PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318–8.043, P = 0.006), with a higher density of PD-L1 + CD8 + TILs (P < 0.001) and percentage of PD-1 + CD8 + TILs (P = 0.017) and PD-L1 + CD8 + TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1 + CD8 + PBLs (P = 0.042), LAG-3 + CD8 + PBLs (P < 0.001), TIM-3 + CD8 + PBLs (P = 0.003), PD-L1 + CD4 + PBLs (P = 0.001), and LAG-3 + CD4 + PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017–0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3 + CD8 + PBLs, PD-L1 + CD8 + TILs and PD-1 + CD8 + TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively. Conclusion We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.

Background Immunohistochemistry (IHC) for programmed cell death ligand 1 (PD-L1) displays staining diversity. We compared IHC staining of PD-L1 in gastric cancer (GC) by using three commercially available antibody clones, and analyzed the correlation with the prognosis. Methods IHC using PD-L1 antibodies (clones SP142, 28–8 and E1L3N) in 315 formalin-fixed paraffin-embedded samples was qualitatively compared at the 1, 5 and 10% cut-off by two pathologists on total, tumor and immune/stromal cells. We used computer – assisted scoring to quantitatively analyze and compare the “H-score” of PD-L1 expression in 66 samples on total cells. The antibody clone SP142 was selected to investigate the infiltration of PD-L1 + CD8 + T cells using automated quantitative immunofluorescence analyses ( n  = 50) and the prognostic significance. The prognoses were assessed by log-rank test. Results PD-L1 clones SP142 and 28–8 displayed great concordance by qualitative (κ = 0.816, 0.810 for total cells and tumor cells at the 5% cut-off) and quantitative analyses (R 2  = 0.7991, 0.8187 for positive percentage and “H-score”). PD-L1 clone SP142 showed the highest positivity in immune/stromal cells staining (18.41%) compared to 28–8 (7.62%), while clone E1L3N showed poor staining in both tumor and immune/stromal cells. Clone SP142, but not 28–8 and E1L3N, predicted a worse prognosis at the 5% cut-off ( p  = 0.0243). Both the clone SP142 and 28–8 had high inter-pathologist correlation for tumor staining (R 2  = 0.9805 and R 2  = 0.9853), but a moderate correlation for stromal/immune cell staining (R 2  = 0.5653 and R 2  = 0.5745). Furthermore, a higher density of PD-L1 + CD8 + T cells was correlated with a shorter survival time (R 2  = 0.0909, p  = 0.0352). Conclusions PD-L1 antibody clone SP142 was superior in cell staining, particularly in immune/stromal cell and prognosis. These findings are important for selection of PD-L1 antibody clones in the future diagnostic test.

Acute myeloid leukemia (AML) is an aggressive and heterogeneous disease, associated with significant morbidity and mortality rates. Tryptophan metabolism has been implicated in the development of several tumors. The immune landscape within the tumor microenvironment plays a pivotal role in both leukemogenesis and the determination of patient prognosis. Nonetheless, the influence of tryptophan metabolic patterns and corresponding immune signatures in AML remains largely unclear. Transcriptomic, genomic, and clinical data from TCGA were analyzed, and GSE71014 was used for external validation. Molecular subtypes were identified via consensus clustering of tryptophan metabolism-related genes (TRPRGs). Immune infiltration was quantified using ESTIMATE. A tryptophan-related prognostic risk score (TRPRS) was constructed using LASSO-Cox regression and evaluated for prognostic performance. We characterized alterations in 39 TRPRGs across AML cohorts and delineated the clinical and tumor microenvironmental features of two molecular subtypes. First, a TRPRG-based scoring system was established, identifying seven candidate genes significantly associated with patient outcomes. After LASSO-Cox regression selection, six genes were incorporated into the final prognostic model, stratify overall survival risk. The TRPRS effectively stratified overall survival in both the TCGA and GEO cohorts and remained an independent prognostic factor after multivariate adjustment. High-TRPRS patients exhibited distinct immune characteristics and differential drug sensitivity patterns. Functional experiments demonstrated that HADH and ECHS1 promote AML cell proliferation and survival. Our integrative analysis identified key tryptophan-metabolism-related genes in AML and developed a six-gene TRPRS capable of accurately distinguishing survival risk. This model not only provides mechanistic insights into AML progression but also offers a framework for individualized risk stratification and therapeutic guidance.