Bacterial and fungal communities regulated directly and indirectly by tobacco-rape rotation promote tobacco production.

Tobacco continuous cropping is prevalent in intensive tobacco agriculture but often leads to microbial community imbalance, soil nutrient deficiency, and decreased crop productivity. While the tobacco-rape rotation has demonstrated significant benefits in increasing tobacco yield. Microorganisms play a crucial role in soil nutrient cycling and crop productivity. However, the internal mechanism of tobacco-rape rotation affecting tobacco yield through microbe-soil interaction is still unclear. In this study, two treatments, tobacco continuous cropping (TC) and tobacco-rape rotation (TR) were used to investigate how planting systems affect soil microbial diversity and community structure, and whether these changes subsequently affect crop yields. The results showed that compared with TC, TR significantly increased the Shannon index, Chao1 index, ACE index of bacteria and fungi, indicating increased microbial α-diversity. On the one hand, TR may directly affect the bacterial and fungal community structure due to the specificity of root morphology and root exudates in rape. Compared with TC, TR significantly increased the proportion of beneficial bacterial and fungal taxa while significantly reduced soil-borne pathogens. Additionally, TR enhanced the scale and complexity of microbial co-occurrence networks, promoting potential synergies between bacterial OTUs. On the other hand, TR indirectly changed microbial community composition by improving soil chemical properties and changing microbial life history strategies. Compared with TC, TR significantly increased the relative abundance of copiotrophs while reduced oligotrophs. Notably, TR significantly increased tobacco yield by 39.6% compared with TC. The relationships among yield, microbial community and soil chemical properties indicated that planting systems had the greatest total effect on tobacco yield, and the microbial community, particularly bacteria, had the greatest direct effect on tobacco yield. Our findings highlighted the potential of tobacco-rape rotation to increase yield by both directly and indirectly optimizing microbial community structure.

NNT-AS1 in CAFs-derived exosomes promotes progression and glucose metabolism through miR-889-3p/HIF-1α in pancreatic adenocarcinoma.

It is metabolic and signaling crosstalk between stromal cells and tumors in the tumor microenvironment, which influences several aspects of tumor formation and drug resistance, including metabolic reprogramming. Despite considerable findings linking lncRNAs in HIF-1-related regulatory networks to cancer cell, little emphasis has been given to the role in communication between cancer-associated fibroblasts (CAFs) and tumor cells. Previously, we observed that NNT-AS1 was substantially expressed in CAFs cells and CAFs exosomes, and subsequently investigated the influence of CAFs exosomal NNT-AS1 on glucose metabolism, proliferation, and metastasis of pancreatic ductal adenocarcinoma (PDAC) cells. Transmission electron microscopy was used to examine exosomes secreted by PDAC patient-derived CAFs. qRT-PCR was used to evaluate the expression of NNT-AS1, miR-889-3p, and HIF-1. The role of CAFs-derived exosomal NNT-AS1 in PDAC cell progression and metabolism have been identified. Dual luciferase reporter assays examined the binding between NNT-AS1, miR-889-3p, and HIF-1. After PDAC cells co-culture exosomes secreted by CAFs, we found that they alter glucose metabolism, proliferation, and metastasis. In PDAC cells, CAF-derived exosomal lncRNA NNT-AS1 acted as a molecular sponge for miR-889-3p. Furthermore, HIF-1 could be targeted by miR-889-3p and was controlled by NNT-AS1. This study explores the mechanism by which NNT-AS1 influences the interaction of CAFs on glycolytic remodeling, proliferation, and metastasis of tumor cells through regulating miR-889-3p/HIF-1α, which also helps discover new clinical treatment targets for PDAC.

Causal relationship between gut microbiota and polycystic ovary syndrome: a literature review and Mendelian randomization study.

Introduction: Numerous studies have suggested an association between gut microbiota and polycystic ovarian syndrome (PCOS). However, the causal relationship between these two factors remains unclear. Methods: A review of observational studies was conducted to compare changes in gut microbiota between PCOS patients and controls. The analysis focused on four levels of classification, namely, phylum, family, genus, and species/genus subgroups. To further investigate the causal relationship, Mendelian randomization (MR) was employed using genome-wide association study (GWAS) data on gut microbiota from the MiBioGen consortium, as well as GWAS data from a large meta-analysis of PCOS. Additionally, a reverse MR was performed, and the results were verified through sensitivity analyses. Results: The present review included 18 observational studies that met the inclusion and exclusion criteria. The abundance of 64 gut microbiota taxa significantly differed between PCOS patients and controls. Using the MR method, eight bacteria were identified as causally associated with PCOS. The protective effects of the genus Sellimonas on PCOS remained significant after applying Bonferroni correction. No significant heterogeneity or horizontal pleiotropy was found in the instrumental variables (IVs). Reverse MR analyses did not reveal a significant causal effect of PCOS on gut microbiota. Conclusion: The differences in gut microbiota between PCOS patients and controls vary across observational studies. However, MR analyses identified specific gut microbiota taxa that are causally related to PCOS. Future studies should investigate the gut microbiota that showed significant results in the MR analyses, as well as the underlying mechanisms of this causal relationship and its potential clinical significance.

Diagnostic and prognostic risk factors analysis for distant metastasis in melanoma: a population-based study.

BACKGROUND: We aimed to develop tools that could predict the occurrence of distant metastases in melanoma and its prognosis based on clinical and pathological characteristics. MATERIALS AND METHODS: We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database of melanoma patients diagnosed between 2010 and 2019. Logistic analyses were performed to identify independent risk factors associated with distant metastasis. Additionally, multivariate Cox analyses were conducted to determine independent prognostic factors for patients with distant metastasis. Two nomograms were established and evaluated with the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Furthermore, we performed a retrospective analysis of melanoma with distant metastasis from our institute between March 2018 and June 2022. RESULTS: Of the total 19 396 melanoma patients, 352 (1.8%) had distant metastases at the time of diagnosis. The following clinical and pathological characteristics were identified as independent risk factors for distant metastasis in melanoma: N stage, tumor size, ulceration, mitosis, primary tumor site, and pathological subtype. Furthermore, tumor size, pathological subtype, and radiotherapy were identified as independent prognostic factors. The results of the training and validation cohorts' ROC curves, calibration, DCA, and Kaplan-Meier survival curves demonstrate the effectiveness of the two nomograms. The retrospective study results from our center supported the results from the SEER database. CONCLUSION: The clinical and pathological characteristics of melanoma can predict a patient's risk of metastasis and prognosis, and the two nomograms are expected to be effective tools to guide therapy decisions.

Identification of anoikis-related subtypes and immune landscape in kidney renal clear cell carcinoma.

Anoikis is a specific form of programmed cell death induced by the loss of cell contact with the extracellular matrix and other cells, and plays an important role in organism development, tissue homeostasis, disease development and tumor metastasis. We comprehensively investigated the expression patterns of anoikis-related genes (ARGs) in kidney renal clear cell carcinoma (KIRC) from public databases. Anoikis-related prognostic signatures were established based on four ARGs expression, in which KIRC patients were assigned different risk scores and divided into two different risk groups. In addition, four ARGs expression was validated by qRT-PCR. A better prognosis was observed in the low-risk group, but with lower immune activity (including immune cells and immune-related functions) in the tumor microenvironment. Combined with the relevant clinical characteristics, a nomogram for clinical application was established. Receiver operating characteristics (ROC) and calibration curves were constructed to demonstrate the predictive power of this risk signature. In addition, higher risk scores were significantly and positively correlated with higher gene expression of tumor mutation load (TMB), immune checkpoints (ICPs) and mismatch repair (MMR)-related proteins in general. The results also suggested that the high-risk group was more sensitive to immunotherapy and certain chemotherapeutic agents. Anoikis-related prognostic signatures may provide a better understanding of the roles of ARGs and offer new perspectives for clinical prognosis and individualized treatment.

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC.

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Relationship between food-derived antioxidant vitamin intake and breast cancer risk: a mendelian randomized study.

BACKGROUND: In previous observational studies, food-derived antioxidant vitamins have been suggested to be associated with breast cancer. However, the findings were inconsistent and the causal relationship could not be clearly elucidated. To confirm the potential causal relationship between food-derived antioxidants (retinol, carotene, vitamin C and vitamin E) and the risk of breast cancer, we conducted a two-sample Mendelian randomization (MR) study. METHODS: The instrumental variables (IVs) as proxies of genetic liability to food-derived antioxidant vitamins were obtained from the UK Biobank Database. We extracted breast cancer data (122,977 cases and 105,974 controls) from the Breast Cancer Consortium (BCAC). In addition, we studied estrogen expression status categorically, including estrogen receptor positive (ER+) breast cancer (69,501 cases and 105,974 controls) and versus estrogen receptor (ER-) negative breast cancer (21,468 cases and 105,974 controls). We performed two-sample Mendelian randomization study, and inverse variance-weighted (IVW) test was regarded as main analysis. Sensitivity analyses were further conducted to assess heterogeneity and horizontal pleiotropy. RESULTS: The results of IVW showed that among the four food-derived antioxidants, only vitamin E had protective effect on the risk of overall breast cancer (OR = 0.837, 95% CI 0.757-0.926, P = 0.001) and ER+ breast cancer (OR = 0.823, 95% CI 0.693-0.977, P = 0.026). However, we found no association between food-derived vitamin E and ER- breast cancer. CONCLUSIONS: Our study suggested food-derived vitamin E can decrease the risk of breast cancer overall and ER+ breast cancer, and the robustness of our results was confirmed by sensitivity analyses.

Lightweight and High Impact Toughness PP/PET/POE Composite Foams Fabricated by In Situ Nanofibrillation and Microcellular Injection Molding.

Polypropylene (PP) has become the most promising and candidate material for fabricating lightweight products. Microcellular injection molding (MIM) is a cost-effective technology for manufacturing porous plastic products. However, it is still challenging to fabricate high-performance PP microcellular components. Herein, we reported an efficient strategy to produce lightweight and high impact toughness foamed PP/polyethylene terephthalate (PET)/polyolefin-based elastomer (POE) components by combining in situ fibrillation (INF) and MIM technologies. First, the INF composite was prepared by integrating twin-screw compounding with melt spinning. SEM analysis showed PET nanofibrils with a diameter of 258 nm were achieved and distributed uniformly in the PP due to the POE's inducing elaboration effect. Rheological and DSC analysis demonstrated PET nanofibrils pronouncedly improved PP's viscoelasticity and crystal nucleation rate, respectively. Compared with PP foam, INF composite foam showed more stretched cells in the skin layer and refined spherical cells in the core layer. Due to the synergistic toughening effect of PET nanofibrils and POE elastic particles, the impact strength of INF composite foams was 295.3% higher than that of PP foam and 191.2% higher than that of melt-blended PP/PET foam. The results gathered in this study reveal potential applications for PP based INF composite foams in the manufacturing of lightweight automotive products with enhanced impact properties.

Comparison of hexaminolevulinate (HAL) -guided versus white light transurethral resection for NMIBC: A systematic review and meta-analysis of randomized controlled trials.

PURPOSE: We systematically reviewed the effectiveness of hexaminolevulinic acid (HAL) after traditional light cystoscopy vs. only white light cystoscopy (WLC) on nonmuscle-invasive bladder cancer (NMIBC) clinical outcomes. METHODS: Systematic literature searches of PubMed, Embase, Web of Science, and the Cochrane database and reference lists were performed. A total of 12 randomized controlled trials (RCTs) of HAL fluorescent cystoscopy (FC) and WLC vs. white light cystoscopy alone for the diagnosis of initial or recurrent bladder cancer that reported bladder cancer recurrence, progression, recurrence-free survival (RFS), and other effects were selected for review. RESULTS: Our results included 2,775 patients identified for analysis and showed that the HAL group had a lower recurrence rate than the white light cystoscopy group with a statistically significant difference (RR=0.77, 95% CI 0.69-0.85. P < 0.05), and this advantage still existed for patients receiving intravesical chemotherapy. There was also a statistically significant difference in favour of fluorescent cystoscopy in recurrence-free survival and progression rate (HR=0.79, 95% CI 0.67-0.92. P < 0.05, RR = 0.63, 95% CI 0.43-0.94. P < 0.05, respectively). The time to first recurrence was not significantly different from that in the WLC group (SMD=0.73, 95% CI, -0.39-1.85. P = 0.2). And the HAL group did not have a significantly reduced residual tumor rate (RR=0.59, 95% CI 0.23-1.51. P = 0.27). CONCLUSIONS: Fluorescent cystoscopy was associated with a reduced risk of bladder cancer recurrence and reduced progression rate; it also has advantages for RFS. However, there was no significant difference in the rate of residual tumor and the time of first recurrence. More studies are needed to better understand the effects of the photosensitizer used on NMIBC patients.

HBV X Protein Induces Degradation of UBXN7, a Novel Negative Regulator of NF-κB Signaling, to Promote HBV Replication.

Chronic hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma. However, the function and mechanism of the effect of HBV on host protein ubiquitination remain largely unknown. We aimed at characterizing whether and how HBV promotes self-replication by affecting host protein ubiquitination. In this study, we identified UBXN7, a novel inhibitor for nuclear factor kappa B (NF-κB) signaling, was degraded via interaction with HBV X protein (HBx) to activate NF-κB signaling and autophagy, thereby affecting HBV replication. The expression of UBXN7 was analyzed by Western blot and quantitative reverse transcription polymerase chain reaction in HBV-transfected hepatoma cells and HBV-infected primary human hepatocytes (PHHs). The effects of UBXN7 on HBV replication were analyzed by using in vitro and in vivo assays, including stable isotope labeling by amino acids in cell culture (SILAC) analysis. Changes in HBV replication and the associated molecular mechanisms were analyzed in hepatoma cell lines. SILAC analyses showed that the ubiquitination of UBXN7 was significantly increased in HepG2.2.15 cells compared with control cells. After HBV infection, HBx protein interacted with UBXN7 to promote K48-linked ubiquitination of UBXN7 at K99, leading to UBXN7 degradation. On the other hand, UBXN7 interacted with the ULK domain of IκB kinase ß through its ubiquitin-associating domain to facilitate its degradation. This in turn reduced NF-κB signaling, leading to reduced autophagy and consequently decreased HBV replication.

The association between circulating docosahexaenoic acid and lung cancer: A Mendelian randomization study.

BACKGROUND: Lung cancer is a malignant tumor with a high incidence, it is vital to identify modifiable and avoidable risk factors for primary prevention, which can significantly lower the risk of cancer by preventing exposure to hazards and altering risky behavior. Some observational studies suggest that an increase in docosahexaenoic acid (DHA) consumption can reduce lung cancer risk. However, interpretation of these observational findings is difficult due to residual confounding or reverse causality. To evaluate the link between DHA and lung cancer, we have undertaken this analysis to examine the causal association between DHA and the risk of lung cancer using a two-sample Mendelian randomization (MR) framework. METHODS: We performed a two-sample MR analysis to evaluate the causal effect of plasma DHA levels on lung cancer risk. For the exposure data, we extracted genetic variants as instrumental variables (IVs) that are strongly associated with DHA from a large-scale genome-wide association study (GWAS). We obtained the corresponding effect estimates for IVs on the risk of lung cancer with 11,348 cases and 15,861 controls. Finally, we applied Mendelian randomization analysis to obtain preliminary MR results and performed sensitivity analyses to verify the robustness of our results. RESULTS: According to the primary MR estimates and further sensitivity analyses, a higher serum DHA level was associated with a higher risk of lung cancer [OR = 1.159, 95% CI (1.04-1.30), P = 0.01]. For lung adenocarcinoma, the results also showed a close correlation between the DHA level and lung adenocarcinoma [OR = 1.277, 95% CI (1.09-1.50), P = 0.003], but it was not statistically significant for squamous cell carcinoma [OR = 1.071, 95% CI (0.89-1.29), P = 0.467]. CONCLUSIONS: Our study revealed that plasma DHA is positively associated with the risk of lung cancer overall, especially for lung adenocarcinoma. This study provides new information to develop dietary guidelines for primary lung cancer prevention.

Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer.

Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.

Survival Risk Prediction of Esophageal Squamous Cell Carcinoma Based on BES-LSSVM.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. In this study, ten indicators related to the survival of patients with ESCC are founded using genetic algorithm feature selection. The prognostic index (PI) for ESCC is established using the binary logistic regression. PI is divided into four stages, and each stage can reasonably reflect the survival status of different patients. By plotting the ROC curve, the critical threshold of patients' age could be found, and patients are divided into the high-age groups and the low-age groups. PI and ten survival-related indicators are used as independent variables, based on the bald eagle search (BES) and least-squares support vector machine (LSSVM), and a survival prediction model for patients with ESCC is established. The results show that five-year survival rates of patients are well predicted by the bald eagle search-least-squares support vector machine (BES-LSSVM). BES-LSSVM has higher prediction accuracy than the existing particle swarm optimization-least-squares support vector machine (PSO-LSSVM), grasshopper optimization algorithm-least-squares support vector machine (GOA-LSSVM), differential evolution-least-squares support vector machine (DE-LSSVM), sparrow search algorithm-least-squares support vector machine (SSA-LSSVM), bald eagle search-back propagation neural network (BES-BPNN), and bald eagle search-extreme learning machine (BES-ELM).

Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm.

Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients' survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size ß of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC.

Upregulation of miR-520c-3p via hepatitis B virus drives hepatocellular migration and invasion by the PTEN/AKT/NF-κB axis.

Hepatitis B virus (HBV) is a major risk factor for the development and progression of hepatocellular carcinoma (HCC). It has been reported that viral infection can interfere with the expression of cellular microRNA (miRNA) to affect oncogenesis. In this study, we showed that miR-520c-3p was upregulated in liver tumor specimens, and we revealed that HBV infection enhanced the expression of miR-520c-3p through the interaction of viral protein HBV X protein (HBx) with transcription factor CREB1. We further showed that miR-520c-3p induced by HBV transfection/infection caused epithelial-mesenchymal transition (EMT). Using the miRNA target prediction database miRBase and luciferase reporter assays, we identified PTEN as a novel target gene of miR-520c-3p and miR-520c-3p directly targeted PTEN's 3'-untranslated region. Moreover, we discovered that HBV promoted EMT via the miR-520c-3p-PTEN to activate AKT-NFκB signaling pathway, leading to increased HCC migration and invasion. Importantly, miR-520c-3p antagomir significantly represses invasiveness in HBx-induced hepatocellular xenograft models. Our findings indicate that miR-520c-3p is a novel regulator of HBV and plays an important role in HCC progression. It may serve as a new biomarker and molecular therapeutic target for HBV patients.

Antimicrobial Terpenes Suppressed the Infection Process of Phytophthora in Fennel-Pepper Intercropping System.

The interactions between non-host roots and pathogens may be key to the inhibition of soilborne pathogens in intercropping systems. Fennel (Foeniculum vulgare) can be intercropped with a wide range of other plants to inhibit soilborne pathogens in biodiversity cultivation. However, the key compounds of fennel root exudates involved in the interactions between fennel roots and pathogens are still unknown. Here, a greenhouse experiment confirmed that intercropping with fennel suppressed pepper (Capsicum annuum) blight disease caused by Phytophthora capsici. Experimentally, the roots and root exudates of fennel can effectively interfere with the infection process of P. capsici at rhizosphere soil concentrations by attracting zoospores and inhibiting the motility of the zoospores and germination of the cystospores. Five terpene compounds (D-limonene, estragole, anethole, gamma-terpenes, and beta-myrcene) that were identified in the fennel rhizosphere soil and root exudates were found to interfere with P. capsica infection. D-limonene was associated with positive chemotaxis with zoospores, and a mixture of the five terpene compounds showed a strong synergistic effect on the infection process of P. capsici, especially for zoospore rupture. Furthermore, the five terpene compounds can induce the accumulation of reactive oxygen species (ROS), especially anethole, in hyphae. ROS accumulation may be one of the antimicrobial mechanisms of terpene compounds. Above all, we proposed that terpene compounds secreted from fennel root play a key role in Phytophthora disease suppression in this intercropping system.

Maresin 1 intervention reverses experimental pulmonary arterial hypertension in mice.

BACKGROUND AND PURPOSE: Pulmonary arterial hypertension (PAH) is a pulmonary vasculature obstructive disease that leads to right heart failure and death. Maresin 1 is an endogenous lipid mediator known to promote inflammation resolution. However, the effect of Maresin 1 on PAH remains unclear. EXPERIMENTAL APPROACH: The serum Maresin 1 concentration was assessed using UPLC. A mouse model of PAH was established by combining the Sugen 5416 injection and hypoxia exposure. After treatment with Maresin 1, the right ventricular systolic pressure (RVSP) and right ventricular function were measured by haemodynamic measurement and echocardiography, respectively. Vascular remodelling was evaluated by histological staining. Confocal microscopy and western blot were used to test related protein expression. In vitro cell migration, proliferation and apoptosis assays were performed in primary rat pulmonary artery smooth muscle cells (PASMCs). Western blotting and siRNA transfection were used to clarify the mechanism of Maresin 1. KEY RESULTS: Endogenous serum Maresin 1 was decreased in PAH patients and mice. Maresin 1 treatment decreased RVSP and attenuated right ventricular dysfunction (RVD) in the murine PAH model. Maresin 1 reversed abnormal changes in pulmonary vascular remodelling, attenuating endothelial to mesenchymal transformation and enhancing apoptosis of α-SMA positive cells. Furthermore, Maresin 1 inhibited PASMC proliferation and promoted apoptosis by inhibiting STAT, AKT, ERK, and FoxO1 phosphorylation via LGR6. CONCLUSION AND IMPLICATIONS: Maresin 1 improved abnormal pulmonary vascular remodelling and right ventricular dysfunction in PAH mice, targeting aberrant PASMC proliferation. This suggests Maresin 1 may have a potent therapeutic effect in vascular disease.

FGF21 alleviates pulmonary hypertension by inhibiting mTORC1/EIF4EBP1 pathway via H19.

Long non-coding RNAs (lncRNAs) play a significant role in pulmonary hypertension (PH). Our preliminary data showed that hypoxia-induced PH is attenuated by fibroblast growth factor 21 (FGF21) administration. Therefore, we further investigated the regulatory role of long non-coding RNAs in PH treated with FGF21. RNA sequencing analysis and real-time PCR identified a significantly up-regulation of the H19 after FGF21 administration. Moreover, gain- and loss-of-function assays demonstrated that FGF21 suppressed hypoxia-induced proliferation of pulmonary artery smooth muscle cells partially through upregulation of H19. In addition, FGF21 deficiency markedly exacerbated hypoxia-induced increases of pulmonary artery pressure and pulmonary vascular remodelling. In addition, AAV-mediated H19 overexpression reversed the malignant phenotype of FGF21 knockout mice under hypoxia expose. Further investigation uncovered that H19 also acted as an orchestra conductor that inhibited the function of mechanistic target of rapamycin complex 1 (mTORC1) by disrupting the interaction of mTORC1 with eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1). Our work highlights the important role of H19 in PH treated with FGF21 and suggests a mechanism involving mTORC1/EIF4EBP1 inhibition, which may provide a fundamental for clinical application of FGF21 in PH.

Survival risk prediction model for ESCC based on relief feature selection and CNN.

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive system with poor prognosis and high mortality. It is of great significance to predict the prognosis risk of patients with cancer by using medical pathology information. To take full advantage of the clinic pathological information of ESCC patients and improve the accuracy of postoperative survival risk prediction, this paper proposes an ESCC survival risk prediction model based on Relief feature selection and convolutional neural network (CNN). Firstly, statistical analysis methods and relief feature selection algorithm are used to extract the important risk factors related to the survival risk of patients. Then, One-dimensional convolutional neural network (1D-CNN) is used to establish the survival risk prediction model of patients with esophageal cancer. Finally, the data of patients with esophageal cancer provided by the First Affiliated Hospital of Zhengzhou University is used to assess the performance of the model. The results show that the model proposed in this paper has a high accuracy rate, which can effectively predict the postoperative survival risk of the patient through the clinical phenotypic index of the patient.