Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

BackgroundPancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.MethodsIn this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.ResultsOur analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.ConclusionOur study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease

Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis

BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies.MethodsComprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments.ResultsCompared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control.ConclusionThis study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression

The Impact of the Buffer Unit on the Performance in 802.11 Wireless LANs

International audienceno abstrac

Traffic profiling for modern enterprise networks: A case study

International audienceno abstrac

Cryogenic Soil—Product of Mineral Weathering Processes

Since the Quaternary, the alternate climate of dry and wet, cold and warm, and the emergence of glacial and interglacial periods have led to great changes in the global environment and climate. As an event closely related to cold climate, cryogenic soil has important reference significance for the study of climate change in a certain region and time period. The research on cryogenic soils mainly focuses on the following three aspects: particle size composition, surface morphology and mineral composition. Through the study of the relevant literature, we find that the correlation coefficient of particle size composition before and after freeze-thaw is used to determine the cause of cryogenic weathering. Due to the singleness of judgment conditions, the result is difficult to be convincing; It is difficult to prove the microscopic morphology of the cause of cryogenic weathering from a single mineral of quartz. Therefore, it is necessary to start with more types of primary minerals, and analyze the differences in the particle shape and microscopic surface morphology of different types of primary minerals during the cryogenic weathering process. And on this basis, the typical mineral morphology of the cause of cryogenic weathering is comprehensively judged; Freeze-thaw has little effect on the mineral composition of the soil, but has a greater impact on the size of the mineral particles, and this size change corresponds to the phenomenon of particles silt-fication. The mineral composition also controls the geochemical composition, and the insignificance of the mineral-chemical composition in the process of cryogenic silt-fication increases the difficulty of judging the cause of cryogenic weathering

Cryogenic Soil—Product of Mineral Weathering Processes

Since the Quaternary, the alternate climate of dry and wet, cold and warm, and the emergence of glacial and interglacial periods have led to great changes in the global environment and climate. As an event closely related to cold climate, cryogenic soil has important reference significance for the study of climate change in a certain region and time period. The research on cryogenic soils mainly focuses on the following three aspects: particle size composition, surface morphology and mineral composition. Through the study of the relevant literature, we find that the correlation coefficient of particle size composition before and after freeze-thaw is used to determine the cause of cryogenic weathering. Due to the singleness of judgment conditions, the result is difficult to be convincing; It is difficult to prove the microscopic morphology of the cause of cryogenic weathering from a single mineral of quartz. Therefore, it is necessary to start with more types of primary minerals, and analyze the differences in the particle shape and microscopic surface morphology of different types of primary minerals during the cryogenic weathering process. And on this basis, the typical mineral morphology of the cause of cryogenic weathering is comprehensively judged; Freeze-thaw has little effect on the mineral composition of the soil, but has a greater impact on the size of the mineral particles, and this size change corresponds to the phenomenon of particles silt-fication. The mineral composition also controls the geochemical composition, and the insignificance of the mineral-chemical composition in the process of cryogenic silt-fication increases the difficulty of judging the cause of cryogenic weathering

Study of hydrothermal processes in ice-layers subgrade under constant temperature and dynamic loading

Abstract The presence of ice-layers in the subgrade soils makes the hydrothermal state of road subgrade built in island permafrost regions more susceptible to external environmental influences. In order to deepen the study of the ice-layers subgrade, a hydrothermal study of subgrade under constant temperature and dynamic loading was carried out. It was found that dynamic loading can change the temperature, moisture and pore water pressure distribution. Under dynamic loading, the hydrothermal and pore water pressure state of the soil in the upper part of the ice layer changed significantly at the beginning of the test. The application of dynamic loads alters the spatial distribution of pore water pressure in the soil, resulting in pressure differences between different areas, which affects the migration of moisture and ultimately leads to the formation of areas with higher moisture content in the area below the load. However, the reduction in soil temperature will weaken the effect of the load, therefore, the temperature of the soil should be controlled for frozen subgrade with ice-layers to prevent the accumulation of moisture in the soil

Study on the effect of step ratio to temperature field of cut-fill transition in deep seasonal frozen soil region

The existing studies lack numerical simulation of the temperature field in the cut-fill transition of the deep seasonal frozen soil regions. The ratio effect of steps in the backfill area of cut-fill transitions has also been little studied. Therefore, this paper establishes a numerical model based on some geological and meteorological data of Yichun City, China. It simulates the temperature field process of the cut-fill transition for the next 10 years under three ratio step conditions: 1:1, 2:3, and 1:2. The results show that the temperature field of the subgrade corresponding to different working conditions varies relatively significantly in terms of maximum freezing depth and frozen layer morphology. The law shows that in the cut-fill transition section, the gentler the step gradient is, the deeper the maximum freezing depth is, and the larger the frozen layer retained area in the thaw period. The retention of frozen interlayers is likely to cause differences in subgrade stiffness and exacerbate the “thaw-basin” phenomenon. Therefore, this study recommends 1:1 ratio of steps as the optimal program for the construction of backfill area, and can be targeted according to the numerical results of this paper in the arrangement of drainage, insulation and other measures

Regulation of the composition of metakaolin-based geopolymer: Effect of zeolite crystal seeds

Recently, geopolymer have received more attention due to their high temperature stability and was considered to be a suitable material for preparing fireproof materials. However, dehydration of geopolymer gel phase products at high temperatures has been found to restrict the improvement of its fire resistance. And crystals such as zeolite and zeolite-like were confirmed to possess high-temperature resistance. Therefore, in this study, the preparation of zeolite was referenced and the crystal seeds induction method was used to modulate the composition of metakaolin-based geopolymer. Aiming to transform the gelatinous products to crystals (zeolites) and improve their fire resistance. Effects of zeolite on the composition of the geopolymer phase and its modulation mechanism were explored by XRD, SEM, TG, FT-IR, and 29Si NMR. Results suggested that zeolite promoted the transformation of the geopolymer gel phase to the zeolite crystal phase. New crystals were first generated on the surface and around the original crystals, and their crystal size was significantly reduced. Moreover, the content of zeolite seeds played a vital role in regulating the geopolymer phase composition. The induction effect was optimum, and about 6.19 % of the gel phase was transformed into zeolite when the zeolite content was 10 %, which is conducive to improving the fire resistance of geopolymer. This study provides a novel method for the preparation of high-performance fireproof geopolymer materials