A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer.

Background: To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model. Methods: The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods. Results: We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types. Conclusion: We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.

Research on the kinetics and degradation pathways of gaseous acetic acid ester organics.

ABSTRACTDesigned to meet the specific needs of the printing industry exhaust gas emissions, this paper proposes a method for the degradation of gaseous acetic acid ester organics that is environmentally friendly, safe, and simple to use: micro-nano cavitation technology. In the process of using micro-nano cavitation technology to degrade acetic acid ester organics, the products in the degradation process were analyzed by gas chromatography-mass (GC-MS) spectrometry, and the degradation pathways of acetic acid ester organics were identified. Under high temperatures and high pressure caused by cavitation collapse, the C-C bond and C-O bond on the main chain of organic matter are cleaved to form low molecular products. Low-molecular intermediate products are continuously produced as the reaction advances, and these intermediate products are further oxidized and decomposed into carbon dioxide and water. Besides, the factors that influence the degradation rate of acetic acid ester organics were investigated. Based on the experimental data, acetic acid esters can degrade with the greatest efficiency when their initial concentration is 200 ± 50 mg/m3 and their treatment time is 20∼30 min. Moreover, the experiment was optimized using the response surface method. The results suggested that for an initial concentration of 155.544 mg/m3 and a reaction time of 21.961 min, the best degradation rate was 0.251 min-1. Micro-nano cavitation technology is a novel and promising technology for the degradation of volatile organic compounds, with a wide range of practical applications.

Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China.

Exploring key impact factors and their effects on urban residents' transport carbon dioxide (CO2) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi'an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters' strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.

Influencing factors and kinetics study on the degradation of gaseous ethyl acetate by micro-nano bubbles.

As an eco-friendly technology, micro-nano bubbles have gained extensive attention due to their excellent properties. We carried out the experiments to investigate the degradation performance of micro-nano bubbles on ethyl acetate at ambient temperature and pressure. The effects were deeply analyzed by studying the treatment time, initial concentration, and mixed components on ethyl acetate. Treatment time at 30 min had the best results, with a removal efficiency of 86.07 % and a degradation rate of 0.340 ± 0.021 min-1. With the increase of the initial ethyl acetate concentration, the degradation extent first increased and then decreased. The best efficiency of 94.61% and the maximum reaction rate of 8.79×10-3 min-1 were achieved at an initial concentration of 265.6 mg/m3. In addition, ethyl acetate degradation was inhibited by the presence of butyl acetate, and removal efficiency of mixed components was lower than that of single components. The GC-MS results showed that possible intermediates, such as ethanol and acetone, were produced during the decomposition process, which was expected to eventually decompose into CO2 and H2O as the reaction progresses. This work presents a new method for the degradation of ethyl acetate and provides valuable information for the degradation of organic matter by micro-nano bubbles.

Clinical Significance of Hepsin and Underlying Signaling Pathways in Prostate Cancer.

The hepsin gene encodes a type II transmembrane serine protease. Previous studies have shown the overexpression of hepsin in prostate cancer, and the dysregulation of hepsin promotes cancer cell proliferation, migration, and metastasis in vitro and in vivo. The review incorporated with our work showed that hepsin expression levels were specifically increased in prostate cancer, and higher expression in metastatic tumors than in primary tumors was also observed. Moreover, increased expression was associated with poor outcomes for patients with prostate cancer. Using in silico protein-protein interaction prediction, mechanistic analysis showed that hepsin interacted with eight other oncogenic proteins, whose expression was significantly correlated with hepsin expression in prostate cancer. The oncogenic functions of hepsin are mainly linked to proteolytic activities that disrupt epithelial integrity and regulatorily interact with other genes to influence cell-proliferation, EMT/metastasis, inflammatory, and tyrosine-kinase-signaling pathways. Moreover, genomic amplifications of hepsin, not deletions or other alterations, were significantly associated with prostate cancer metastasis. Targeting hepsin using a specific inhibitor or antibodies significantly attenuates its oncogenic behaviors. Therefore, hepsin could be a novel biomarker and therapeutic target for prostate cancer.

IRF1 Inhibits Autophagy-Mediated Proliferation of Colorectal Cancer via Targeting ATG13.

IRF1 is a nuclear transcription factor that mediates interferon effects and appears to have anti-tumor activity. To determine the roles of IRF1 in colorectal cancer (CRC), we investigated the effects of IRF1 in CRC cells. We found that IRF1 inhibit cell proliferation and tumor growth. Under starvation conditions, IRF1 enhanced apoptosis and reduced autophagic flux. ATG13, an important factor of autophagy complex, was confirmed as a target of IRF1. These findings indicated that IRF1 function as a tumor suppressor in CRC and inhibit autophagy through ATG13, targeting this pathway may provide new insights into the molecular mechanisms of CRC progression.

Thermal Layout Analysis and Design of Direct Methanol Fuel Cells on PCB Based on Novel Particle Swarm Optimization.

As a new energy technology, the fuel cell has developed rapidly, and its performance has been continuously improved. Fuel cell stacks composed of multiple single cells are gradually being used in portable electronic products. Since the performance of fuel cells cannot be optimal at room temperature, it is critical to research cell temperature characteristics and heat distributions in applications. In this paper, the effects of temperature and charge transfer coefficient and the relationship between exchange current density and output voltage were analyzed by the mathematical model of direct methanol fuel cells. Moreover, to optimize the thermal layout of the fuel cell stack in the printed circuit board (PCB) substrate, the idea of a fuel cell as a device was proposed innovatively, and the corresponding thermal optimization strategy was analyzed. A novel particle swarm optimization algorithm was used to detect the optimal layout of fuel cells of different specifications on the same substrate. The three-dimensional thermal simulation model was used to obtain the temperature data and verify the optimization results.

The Self-Adaptive Fuel Supply Mechanism in Micro DMFC Based on the Microvalve.

To achieve a self-adaptive fuel supply mechanism for the micro direct methanol fuel cell (µDMFC), we designed and developed a thermal control microvalve channel structure, where we considered the relationship between the temperature characteristics, viscosity, and velocity of the methanol solution. Both the single channel model and three-dimensional cell model for the microvalve were established using the COMSOL Multiphysics program. The results demonstrated that in the microvalve channel, the viscosity of the solution decreased, and the flow rate at the microvalve outlet increased with the increasing temperature. Meanwhile, the geometry structure of the microvalve single channel was optimized, so that the effect of the control speed of the microvalve under temperature changes became more prominent. In the full-cell model analysis, a low-velocity methanol solution at the low current density can significantly inhibit methanol crossover. At the high current densities, an increase in the methanol solution flow rate was beneficial to an increase in the cell reaction output. The µDMFC was fabricated and the experiment was conducted, where the results showed that the power density of the self-adaptive cell reached a maximum value of 16.56 mW/cm2 in 2 M methanol solution, which was up to 7% better than conventional cell performance. The proposed microvalve structure can effectively improve the output power of the µDMFC during the whole reaction process, and it may improve the stability of the cell operation.