Distribution pattern and influencing factors of bacterial communities in different soil depths of Caragana jubata shurb in Luya Mountain, China.

Soil microorganisms are important components of terrestrial ecosystems, affecting soil formation and fertility, plant growth and stress tolerance, nutrient turnover and carbon storage. In this study, we collected soil samples (humus layer, 0-10 cm, 10-20 cm, 20-40 cm, and 40-80 cm) from Caragana jubata shrubland in Shanxi subalpine to explore the composition, diversity, and assembly of soil bacterial communities at different depths across the soil profile. The results showed that Actinomycota (19%-28%), Chloromycota (10%-36%) and Acidobacteria (15%-24%), and Proteobacteria (9%-25%) were the dominant bacterial phyla. α-diversity of soil bacterial community significantly decreased with the increases of soil depth. Soil bacterial ß-diversity varied across different soil depths. Soil pH, water content, and enzyme activity were the main ecological factors affecting the distribution of soil bacterial communities. Soil bacterial communities had more complex interactions in humus layer and 0-10 cm layer. On the whole, soil bacterial communities were dominated by coexistence in C. jubata shrubland, and the soil bacterial community assembly was driven by random process.

Sesamin alleviates lipid accumulation induced by oleic acid via PINK1/Parkin-mediated mitophagy in HepG2 cells.

Sesamin, a special compound present in sesame and sesame oil, has been reported a role in regulating lipid metabolism, while the underlying mechanisms remain unclear. Autophagy has been reported associated with lipid metabolism and regarded as a key modulator in liver steatosis. The present work aimed to investigate whether sesamin could exert its protective effects against lipid accumulation via modulating autophagy in HepG2 cells stimulated with oleic acid (OA). Cell viability was evaluated using the CCK-8 method, and triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein, cholesterol (LDL-C), alanine aminotransferase (ALT), along with aspartate aminotransferase (AST) were assessed by oil red O staining, transmission electron microscopy (TEM), and biochemical kits to investigate the lipid-lowering effects of sesamin. Differentially expressed genes were screened by RNA sequencing and validated using real-time quantitative PCR and Western blot. Autophagy and mitophagy related molecules were analyzed employing TEM, Western blot, and immunofluorescence. The data shows that in HepG2 cells stimulated by OA, sesamin reduces levels of TG, TC, LDL-C, ALT, and AST while elevating HDL-C, alleviates the lipid accumulation and improves fatty acid metabolism through modulating the levels of fat metabolism related genes including PCSK9, FABP1, CD36, and SOX4. Sesamin restores the suppressed autophagy in HepG2 cells caused by OA, which could be blocked by autophagy inhibitors. This indicates that sesamin improves fatty acid metabolism by enhancing autophagy levels, thereby mitigating the intracellular lipid accumulation. Furthermore, sesamin significantly enhances the mitophagy and improves mitochondrial homeostasis via activating the PINK/Parkin pathway. These data suggest that sesamin alleviates the excessive lipid accumulation in HepG2 caused by OA by restoring the impaired mitophagy via the PINK1/Parkin pathway, probably playing a preventive or therapeutic role in hepatic steatosis.

Effects of AMF inoculation on the eco-physiological characteristics of Imperata cylindrica under differing soil nitrogen conditions.

Arbuscular mycorrhizal fungi (AMF) play a key role in terrestrial ecosystems, while the ecological restoration application of AMF in mining areas has been progressively gaining attention. This study simulated a low nitrogen (N) environment in copper tailings mining soil to explore inoculative effects of four AMF species on the eco-physiological characteristics of Imperata cylindrica, and provided plant-microbial symbiote with excellent resistance to copper tailings. Results show that N, soil type, AMF species, and associated interactions significantly affected ammonium (NH4 +), nitrate nitrogen (NO3 -), and total nitrogen (TN) content and photosynthetic characteristics of I. cylindrica. Additionally, interactions between soil type and AMF species significantly affected the biomass, plant height, and tiller number of I. cylindrica. Rhizophagus irregularis and Glomus claroideun significantly increased TN and NH4 + content in the belowground components I. cylindrica in non-mineralized sand. Moreover, the inoculation of these two fungi species significantly increased belowground NH4 + content in mineralized sand. The net photosynthetic rate positively correlated to aboveground total carbon (TC) and TN content under the high N and non-mineralized sand treatment. Moreover, Glomus claroideun and Glomus etunicatum inoculation significantly increased both net photosynthetic and water utilization rates, while F. mosseae inoculation significantly increased the transpiration rate under the low N treatment. Additionally, aboveground total sulfur (TS) content positively correlated to the intercellular carbon dioxide (CO2) concentration, stomatal conductance, and the transpiration rate under the low N sand treatment. Furthermore, G. claroideun, G. etunicatum, and F. mosseae inoculation significantly increased aboveground NH4 + and belowground TC content of I. cylindrica, while G. etunicatum significantly increased belowground NH4 + content. Average membership function values of all physiological and ecological I. cylindrica indexes infected with AMF species were higher compared to the control group, while corresponding values of I. cylindrica inoculated with G. claroideun were highest overall. Finally, comprehensive evaluation coefficients were highest under both the low N and high N mineralized sand treatments. This study provides information on microbial resources and plant-microbe symbionts in a copper tailings area, while aiming to improve current nutrient-poor soil conditions and ecological restoration efficiency in copper tailings areas.

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma.

BACKGROUND: The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT). METHODS: Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients. RESULTS: A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05). CONCLUSION: The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. PURPOSE: To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. STUDY TYPE: Retrospective. POPULATION: A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging, contrast enhanced-T1 -weighted imaging using fast spin echo sequences at 1.5 T scanner. ASSESSMENT: Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic ), radiomics features (Modelradiomics ), and clinical factors + radiomics signatures using logistic (Modelcombined ), and BPNN (ModelBPNN ) methods were established, and model performances were compared. STATISTICAL TESTS: Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance. RESULTS: Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined , Modelradiomics , and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). CONCLUSION: A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.