Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas.

The poor prognosis of gliomas necessitates the search for biomarkers for predicting clinical outcomes. Recent studies have shown that PANoptosis play an important role in tumor progression. However, the role of PANoptosis in in gliomas has not been fully clarified.Low-grade gliomas (LGGs) from TCGA and CGGA database were classified into two PANoptosis patterns based on the expression of PANoptosis related genes (PRGs) using consensus clustering method, followed which the differentially expressed genes (DEGs) between two PANoptosis patterns were defined as PANoptosis related gene signature. Subsequently, LGGs were separated into two PANoptosis related gene clusters with distinct prognosis based on PANoptosis related gene signature. Univariate and multivariate cox regression analysis confirmed the prognostic values of PANoptosis related gene cluster, based on which a nomogram model was constructed to predict the prognosis in LGGs. ESTIMATE algorithm, MCP counter and CIBERSORT algorithm were utilized to explore the distinct characteristics of tumor microenvironment (TME) between two PANoptosis related gene clusters. Furthermore, an artificial neural network (ANN) model based on machine learning methods was developed to discriminate distinct PANoptosis related gene clusters. Two external datasets were used to verify the performance of the ANN model. The Human Protein Atlas website and western blotting were utilized to confirm the expression of the featured genes involved the ANN model. We developed a machine learning based ANN model for discriminating PANoptosis related subgroups with drawing implications in predicting prognosis in gliomas.

[High expression of activated CD4+ memory T cells and CD8+ T cells and low expression of M0 macrophage are associated with better clinical prognosis in bladder cancer patients].

Objective To study the infiltration pattern of bladder cancer immune cells and explore the relationship between immune cells and tumor prognosis. Methods The bladder cancer transcript data and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA). CIBERSORT software deconvolution method was used to calculate the proportions of 22 kinds of immune cells. R software was used to analyze the immune cell infiltration pattern in each clinical stage. The relationship between each immune cell and prognosis was analyzed by Kaplan-Meier survival. Results A total of 433 cases of gene transcript data were downloaded from the TCGA database, including 414 cases of bladder cancer tissues and 19 normal tissues. After the data were corrected, the proportions of immune cells were calculated using CIBERSORT software. Screening was performed, and 146 cases of bladder cancer tissue and 4 cases of normal bladder tissue were obtained. Activated CD4+ memory T cells and CD8+ T cells had the lowest expression and M0 macrophages had the highest expression in clinical stage IV of bladder cancer. The bladder cancer patients with high expression of activated CD4+ memory T cells, CD8+ T cells and low expression of M0 macrophages had beneficial prognosis. Conclusion The bladder cancer patients with high expression of activated CD4+ memory T cells, CD8+ T cells and low expression of M0 macrophages might have better clinical prognosis.

Particles and porous tablets based on Fe0/ZSM-5 composites prepared by ball-milling for heavy metals removal: Dissolved Fe2+, pH, and mechanism.

Novel, low-cost Fe0/ZSM-5-based particles and porous tablets were prepared by a ball-milling method and used for the removal of Pb2+ in solution. Solid-phase characterization by scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) and transmission electron microscopy (TEM) revealed that the Fe0 microparticles were evenly loaded and tightly immobilized on the surface of ZSM-5 because of the extrusion/welding impact during ball-milling. For different Pb2+ concentrations, batch experiments indicated that the removal of Pb2+ increased with the decline of dissolved Fe2+ and pH value in the solution for particles; opposite results were obtained for the tablets. The differences in the contact between both materials and Pb2+ were the main factor controlling Pb2+ removal in the solution. Investigation into the effect of initial pH value revealed that high pH reduced the number of electrons released from Fe corrosion. Consequently, low levels of removed Pb2+ and dissolved Fe2+ were synchronously observed. Also, simulated electroplating wastewater was treated using the prepared particles and porous tablets, and the removal order of Pb2+>Cr6+>Cu2+≈Cd2+ was observed. The Fe0/ZSM-5 particles and tablets prepared through ball-milling show potential as materials for treatment of Pb2+ and other toxic metals.

[Leaching Toxicity and Bioaccessibility of Heavy Metals in MSWI Fly Ash with Various Particle Sizes].

Size fractionation and chemical composition of municipal solid waste incinerator (MSWI) fly ash were investigated. And the leaching efficiency of heavy metals was contrastively studied using toxicity characteristic leaching procedure (TCLP), European standard protocol (EN12457-2), sequential extraction procedure (SEP) and physiologically based extraction test (PBET) for different size-fractionated fly ash. The results indicated that the main compositing elements of fly ash were Ca, Si, Al, Mg, Fe, Na, K. 58.7% of fly ash particles were in the range of 38-106 µm and the amount in the<150 µm particle-size fraction accounted for more than 90% of in total, Cu, Pb and Cd mainly existed in the residual form with a minor amount of exchange fraction for any size-fractioned fly ash particles. TCLP result showed that leaching amounts of Zn, Cu and Pb, except for As and V, firstly increased and then decreased with the reduction of particle size. The leaching amount of Zn reached the highest value of 547 mg·kg-1 in the range of 38-106 µm, whereas the leaching amount of heavy metals exhibited a lower level for all size-fractioned fly ash particles. The extraction concentrations of Zn, Pb, Cu and As reached 3270, 339, 335 and 16.8 mg·kg-1 for the PBET tests, respectively, which were significantly higher than those of the TCLP. Therefore, heavy metals in MSWI fly ash had the potential bioavailability to human body.