Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Medical waste incineration fly ash-based magnesium potassium phosphate cement: Calcium-reinforced chlorine solidification/stabilization mechanism and optimized carbon reduction process strategy.

The traditional solidification/stabilization (S/S) technology, Ordinary Portland Cement (OPC), has been widely criticized due to its poor resistance to chloride and significant carbon emissions. Herein, a S/S strategy based on magnesium potassium phosphate cement (MKPC) was developed for the medical waste incineration fly ash (MFA) disposal, which harmonized the chlorine stabilization rate and potential carbon emissions. The in-situ XRD results indicated that the Cl- was efficiently immobilized in the MKPC system with coexisting Ca2+ by the formation of stable Ca5(PO4)3Cl through direct precipitation or intermediate transformation (the Cl- immobilization rate was up to 77.29%). Additionally, the MFA-based MKPC also demonstrated a compressive strength of up to 39.6 MPa, along with an immobilization rate exceeding 90% for heavy metals. Notably, despite the deterioration of the aforementioned S/S performances with increasing MFA incorporation, the potential carbon emissions associated with the entire S/S process were significantly reduced. According to the Life Cycle Assessment, the potential carbon emissions decreased to 8.35 × 102 kg CO2-eq when the MFA reached the blending equilibrium point (17.68 wt.%), while the Cl- immobilization rate still remained above 65%, achieving an acceptable equilibrium. This work proposes a low-carbon preparation strategy for MKPC that realizes chlorine stabilization, which is instructive for the design of S/S materials.

Preparation of magnesium potassium phosphate cement from municipal solid waste incineration fly ash and lead slag co-blended: Ca-induced crystal reconstruction process and Pb-Cl synergistic solidification mechanism.

Higher chlorine (Cl) content than lead (Pb) content in municipal solid waste incineration fly ash (MSWIFA) impeded the practical application of Pb5(PO4)3Cl-derived magnesium potassium phosphate cement (MKPC) preparation strategy. Herein, Pb/Ca-rich lead slag (LS) was co-blended with MSWIFA to prepare MKPC for the synergistic treatment of both two solid wastes and the Pb-Cl solidification. The results showed that the resulting 15-15 (15 wt% MSWIFA and 15 wt% LS incorporation) sample achieved 25.44 MPa compressive strength, and Pb and Cl leaching toxicity was reduced by 99.18 % and 92.80 %, respectively. The X-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses showed that Pb2+, Ca2+, phosphate and Cl- formed PbxCa5-x(PO4)3Cl in samples. The formation of PbxCa5-x(PO4)3Cl was also demonstrated by the high-angle annular dark field scanning transmission electron microscope (HAADF-STEM), while differences in the lattice characteristics of PbxCa5-x(PO4)3Cl and Pb5(PO4)3Cl were found. In-situ XRD indicated that Ca2+ accelerated the transformation of Pb2+ to Pb5(PO4)3Cl. After co-precipitating with Ca2+ to form PbxCa5-x(PO4)3Cl, Pb2+ continuously substituted Ca2+ to eventually transform to Pb5(PO4)3Cl. This work informs the synergistic treatment of MSWIFA and LS and offers new insights into the reaction mechanism between Pb2+, phosphate and Cl- under Ca2+ induction.

Study on the production of high 3HV content PHBV via an open fermentation with waste silkworm excrement as the carbon source by the haloarchaeon Haloferax mediterranei.

Silkworm excrement is hard to be degraded or bio-utilized by environmental microorganisms due to its high content of heavy metals and antimicrobial biomacromolecules in mulberry leaves. In traditional Chinese silk industry, the silkworm excrement results in environmental problems. In this study, the silkworm excrement after chlorophyll ethanol-extraction was researched. An open fermentation strategy was developed using the silkworm excrement as the sole or partial carbon source by haloarchaea to accumulate polyhydroxyalkanoates. As a haloarchaeon with strong carbon source utilization ability, Haloferax mediterranei was found to accumulate a certain amount of poly(3-hydroxybutyrate-co-3-hydroxyvalerate; PHBV) using waste silkworm excrement. The results showed that the addition of silkworm excrement into glucose based fermentation medium can significantly improve the production of PHBV. Using a mixture carbon source including the extract of silkworm excrement and glucose (with a 1:1 carbon content ratio), the yield of PHBV was 1.73 ± 0.12 g/l, which showed a 26% increase than that of fermentation without the silkworm excrement addition. When the NaCl content of medium was set to approximately 15%, fermentation without sterilization was performed using silkworm excrement as the carbon source. Moreover, the addition of the silkworm excrement extract could increase the 3-hydroxyvalerate (3 HV) content of PHBV regardless of the sterilization or non-sterilization fermentation conditions. When using silkworm excrement as the sole carbon source, the 3 HV content was as high as 16.37 ± 0.54 mol %. The real-time quantitative PCR results showed that the addition of the silkworm excrement could specifically enhance the expression of genes involved in the aspartate/2-ketobutyric acid pathway related to 3 HV synthesis in H. mediterranei, and further analysis of the amino acid of the silkworm excrement suggested that the high content of threonine in the silkworm excrement might be the reason for the increase of 3 HV content. Taken together, the success of non-sterile fermentation in hypersaline condition using haloarchaea implied a novel way to reuse the silkworm excrement, which not only reduces the production costs of PHBV, but also is conducive to environmental protection.

Research on a Weighted Gene Co-expression Network Analysis method for mining pathogenic genes in thyroid cancer.

Thyroid cancer (TC) is one of the most common thyroid malignancies occurring worldwide, and accounts for about 1% of all the malignant tumors. It is one of the fastest growing tumor and can occur at any age, but it is more common in women. It is important to find the pathogenesis and treatment targets of TC. In this pursuit, the present study was envisaged to investigate the effective carcinogenic biological macromolecules, so as to provide a better understanding of the occurrence and development of TC. The clinical and gene expression data were collected from The Cancer Genome Atlas (TCGA). We clustered mRNA and long non-coding RNA (lncRNA) into different modules by Weighted Gene Co-expression Network Analysis (WGCNA), and calculated the correlation coefficient between the genes and clinical phenotypes. Using WGCNA, we identified the module with the highest correlation coefficient. Subsequently, by using the differential genes expression analysis to screen the differential micro-RNA (miRNA), the univariate Cox proportional hazard regression was employed to screen the hub genes related to overall survival (OS), with P < 0.05 as the statistical significance threshold. Finally, we designed a hub competitive endogenous RNA(ceRNA) network of disease-associated lncRNAs, miRNAs, and mRNAs. From the results of enrichment analysis, the association of these genes could be related to the occurrence and development of TC, and these hub RNAs can be valuable prognostic markers and therapeutic targets in TC.

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

A Temporal Adaptive Access Mechanism for Data Fusion in an IoT Environment.

Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT.