Experimental Validation Research Articles

Deciphering Carotenoid and Flowering Pathway Gene Variations in Eastern and Western Carrots (Daucus carota L.)

Background/Objectives: Carrot is a major root vegetable in the Apiaceae owing to its abundant carotenoids, antioxidants, vitamins, and minerals. The modern dark orange western carrot was derived from sequential domestication events from the white-rooted wild form to the pale orange-, purple-, or yellow-rooted eastern carrot. Genetic and molecular studies between eastern and western carrots are meager despite their evolutionary relatedness. Methods: Twelve RNA seq libraries obtained from distinct eastern and western cultivars at vegetative and reproductive developmental stages were utilized to identify differentially expressed genes (DEGs) to decode the key molecular genetic changes in carotenoid and flowering pathways. Results: In the carotenoid pathway, an upregulation of the PSY, CRTISO, and LCYE genes was observed in the western cultivar, while the eastern cultivar exhibited a higher abundance of downstream enzymes, particularly CCD and NCED1. These later enzymes are crucial in linking apocarotenoids and xanthin-mediated ABA signaling. In the flowering pathway, we noted a greater expression of DEGs associated with the photoperiod and vernalization pathways in the western cultivar. In contrast, the eastern cultivar displayed a dominance of genes from the autonomous pathway (FLD, LD, FLK, and PEBP) that function to repress FLC. The experimental validation of 12 key genes through quantitative real-time PCR further confirms their functional role in carrots. Conclusions: The identified key regulatory genes in these major pathways are valuable for designing breeding strategies for manipulating carotenoid content and flowering time while developing climate-specific carrots. The knowledge of carotenoid and flowering pathways is advantageous in producing nutritionally improved roots and seeds in carrots across diverse climates.

Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling

With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve labeling efficiency in distributed data middleware scenarios is the main difficulty in enhancing the quality of labeled data at present. In this paper, we proposed an asynchronous federated learning optimization method based on the combination of LLM and digital twin technology. By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. The experimental validation results show that our proposed method has good performance compared with other algorithms in the process of intelligent labeling both in terms of accuracy and running solves the consistency and accuracy problems of intelligent labeling in a distributed data center.

Fourier p- element simulation and experimental validation of frequencies for rotating blade in flexure

Fourier p- element simulation and experimental validation of frequencies for rotating blade in flexure

ARLSSMM: an effective fault diagnosis method for roller bearing under strong noise

ABSTRACT Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance.

Precision Orthodontic Force Simulation Using Nodal Displacement-Based Archwire Loading Approach.

Precision in force simulationis critical for forecasting tooth movement and optimizing orthodontic treatment strategies. While traditional techniques have provided valuable insights, there remains a need for improved methodologies that can seamlessly integrate with fixed orthodontic practices. This study aims to refine orthodontic force simulation techniques by integrating a nodal displacement approach within finite element analysis, specifically designed to enhance prediction accuracy in tooth movement and optimize orthodontic treatment planning. Three-dimensional patient-specific models of the Tooth, Periodontal Ligament, and Bone Complex (TPBC) of five volunteers were created, along with models of brackets and wires. The simulation involved an initial step of estimating node displacements to align the archwire with the brackets, followed by a subsequent step to attain the required tooth movement and determine the orthodontic force. Experimental validation of the simulation results was performed using an orthodontic force tester (OFT). Utilizing the nodal displacement approach, the simulation successfully positioned the archwire onto the brackets. When benchmarked against the OFT, 80% of the simulated force directions exhibited angular discrepancies of less than 5°. Additionally, the absolute differences in force magnitude reached 20.06 cN, and in moments, up to 71.76 cN mm. The relative differences were as high as 9.55% for force and 13.83% for moments. These findings represent an improvement of up to 10.45% in force accuracy and 8.87% in moment accuracy compared to median values reported in most recent literature. In this research, a nodal displacement methodology was employed to simulate orthodontic forces with precision across the dental arch. The results demonstrate the approache's potential to enhance the accuracy of force prediction in orthodontic treatment planning, thereby advancing our understanding of orthodontic biomechanics.

A Systematic Identification of RNA-Binding Proteins (RBPs) Driving Aberrant Splicing in Cancer

Background: Alternative Splicing (AS) is a post-transcriptional process that allows a single RNA to produce different mRNA variants and, in some cases, multiple proteins. Various processes, many yet to be discovered, regulate AS. This study focuses on regulation by RNA-binding proteins (RBPs), which are not only crucial for splicing regulation but also linked to cancer prognosis and are emerging as therapeutic targets for cancer treatment. CLIP-seq experiments help identify where RBPs bind on nascent transcripts, potentially revealing changes in splicing status that suggest causal relationships. Selecting specific RBPs for CLIP-seq experiments is often driven by a priori hypotheses. Results: We developed an algorithm to detect RBPs likely related to splicing changes between conditions by integrating several CLIP-seq databases and a differential splicing detection algorithm. This work refines a previous study by improving splicing event prediction, testing different enrichment statistics, and performing additional validation experiments. The new method provides more accurate predictions and is included in the Bioconductor package EventPointer 3.14. We tested the algorithm in four experiments involving knockdowns of seven different RBPs. The algorithm accurately assessed the statistical significance of these RBPs using only splicing alterations. Additionally, we applied the algorithm to study sixteen cancer types from The Cancer Genome Atlas (TCGA) and three from TARGET. We identified relationships between RBPs and various cancer types, including alterations in CREBBP and MBNL2 in adenocarcinomas of the lung, liver, prostate, rectum, stomach, and colon. Some of these findings are validated in the literature, while others are novel. Conclusions: The developed algorithm enhances the ability to predict and understand RBP-related splicing changes, offering more accurate predictions and novel insights into cancer-related splicing alterations. This work highlights the potential of RBPs as therapeutic targets and contributes to the broader understanding of their roles in cancer biology.

7-hydroxycoumarin ameliorates ulcerative colitis in mice by inhibiting the MAPK pathway and alleviating gut microbiota dysbiosis.

This research aimed to delineate the pharmacological mechanisms of 7-Hydroxycoumarin (7-HC) on ulcerative colitis (UC) employing network pharmacology and experimental validation. To investigate the therapeutic effects of 7-HC on UC, a UC mouse model was established through the unrestricted intake of 3.0% dextran sulfate sodium (DSS) in their drinking water. Subsequently, we predicted the core targets and signaling pathways of 7-HC for the treatment of UC using the network pharmacology approach. Finally, the insights gained from network pharmacology were further validated by molecular docking, molecular dynamics simulation as well as in vivo experiments. Administering 7-HC orally to mice with UC led to a marked improvement in colitis indicators. Furthermore, 7-HC significantly lowered the levels of inflammatory cytokines (TNF-α, IL-1β) in the colon and modulated oxidative stress markers (MPO, SOD). Subsequent studies identified 2 core targets (AKT1 and EGFR) in the colon of UC mice that were inhibited by 7-HC. Network pharmacology and experimental validation showed that 7-HC can reduce the expression of MAPK pathway markers P38, JNK, ERK, and their phosphorylation; 7-HC can also ameliorate UC by regulating the gut microbiome. 7-HC demonstrates considerable efficacy in alleviating UC in mice, primarily through substantial diminution of tissue inflammation and oxidative stress. This is the first time that 7-HC has been found to treat UC by inhibiting the MAPK pathway and modulating the gut microbiota, providing a fresh perspective on the pharmacological mechanisms through which 7-HC operates in the management of UC.

In Silico Identification of Promising PDE5 Inhibitors Against Hepatocellular Carcinoma Among Natural Derivatives: A Study Involving Docking and ADMET Analysis.

Hepatocellular carcinoma (HCC) represents a significant worldwide health challenge due to its high mortality rate, underscoring the need for advanced therapeutic strategies. This study employs a computer-based method to identify potential phosphodiesterase 5 (PDE5) inhibitors from a library of approved IBS_Scaff 532 natural compounds. PDE5 inhibitors have gained attention for their potential anti-tumor effects. Using molecular docking simulations, the researchers assessed how well these compounds bind to the PDE5 enzyme, which regulates cellular cGMP pathways. Additionally, ADMET profiling predicted the pharmacological and safety properties of candidate inhibitors. Notably, compounds like IBS_NC-0322 and IBS_NC-0320 exhibited favorable ADMET properties and strong binding affinities. These findings suggest their potential as therapeutic agents for treating HCC. While in silico methods serve as valuable screening tools, subsequent experimental validation and clinical trials are essential for confirmation.

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response

Background/Objectives: Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of breast cancer patients warrants further study. Methods: The anoikis-related prognostic signature (ANRS) was generated using a variety of machine learning methods, and the correlation between the ANRS and the tumor microenvironment (TME), drug sensitivity, and immunotherapy was investigated. Moreover, single-cell analysis and spatial transcriptome studies were conducted to investigate the expression of prognostic ANRGs across various cell types. Finally, the expression of ANRGs was verified by RT-PCR and Western blot analysis (WB), and the expression level of PLK1 in the blood was measured by the enzyme-linked immunosorbent assay (ELISA). Results: The ANRS, consisting of five ANRGs, was established. BC patients within the high-ANRS group exhibited poorer prognoses, characterized by elevated levels of immune suppression and stromal scores. The low-ANRS group had a better response to chemotherapy and immunotherapy. Single-cell analysis and spatial transcriptomics revealed variations in ANRGs across cells. The results of RT-PCR and WB were consistent with the differential expression analyses from databases. NU.1025 and imatinib were identified as potential inhibitors for SPIB and PLK1, respectively. Additionally, findings from ELISA demonstrated increased expression levels of PLK1 in the blood of BC patients. Conclusions: The ANRS can act as an independent prognostic indicator for BC patients, providing significant guidance for the implementation of chemotherapy and immunotherapy in these patients. Additionally, PLK1 has emerged as a potential blood-based diagnostic marker for breast cancer patients.

Graph neural link predictor based on cycle structure

AbstractCurrently, the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure, which predominantly rely on low‐order structural information and do not explore the multivariate interactions between nodes from the perspective of higher‐order structural information present in the network. The cycle structure is a higher‐order structure that lies between the star and clique structures, where all nodes within the same cycle can interact with each other, even in the absence of direct edges. If a node is encompassed by multiple cycles, it indicates that the node interacts and associates with a greater number of nodes in the network, and it means the node is more important in the network to some extent. Furthermore, if two nodes are included in multiple cycles, it signifies the two nodes are more likely to be connected. Therefore, firstly, a multi‐information fusion node importance algorithm based on the cycle structure information is proposed, which integrates both high‐order and low‐order structural information. Secondly, the obtained integrated structure information and node feature information is regarded as the input features, a two‐channel graph neural network model is designed to learn the cycle structure information. Then, the cycle structure information is utilised for the task of link prediction, and a graph neural link predictor with multi‐information interactions based on the cycle structure is developed. Finally, extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation, the proposed graph neural network model can effectively learn the cycle structure information, and using higher‐order structural information—cycle information proves to significantly enhance the overall link prediction performance.

A new immunomagnetic microfluidic device for characterizing EGFR mutations in circulating tumor cells from patients withnon-small cell lung cancer

Incorporating precision oncology into cancer management has begun to improve clinical outcomes. Accurate sampling techniques that detect molecular aberrations are crucial for effective implementation. Circulating tumor cells (CTCs), derived from primary or metastatic sites and present in the blood, are proposed as useful diagnostic tools, though their use has been limited due to their rarity, especially in early-stage cancers. This study presents a novel immunomagnetic microfluidic device that efficiently isolates CTCs for analyzing epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC). The device was designed and laser-cut from polymethylmethacrylate. Validation experiments involved spiking PC-9 cells (an established lung cancer cell line containing GLU 746-ALA 750 deletion mutations in exon 19 of the EGFR gene) into media and isolating these cells. Exons 18 – 21 of EGFR were amplified using a polymerase chain reaction to demonstrate the device’s rapid mutation detection capability. Next-generation sequencing was used to characterize these exons in a cohort of 38 NSCLC patients, successfully isolating CTCs from all. Among these patients, 30 (79%) had EGFR mutations, with exon 19 showing the highest mutation rate (87%) and exon 21 the highest point mutation rate (23%). Our device captured CTCs effectively in <1 h, enabling mutation detection. Further studies are needed to assess the prognostic significance of these mutations, but this technology has potential applications in various solid tumors.

Automatic detection of Android crypto ransomware using supervisor reduction

AbstractThis paper proposes a finite-state machine based approach to recognise crypto ransomware based on their behaviour. Malicious and benign Android applications are executed to capture the system calls they generate, which are then filtered and tokenised and converted to finite-state machines. The finite-state machines are simplified using supervisor reduction, which generalises the behavioural patterns and produces compact classification models. The classification models can be implemented in a lightweight monitoring system to detect malicious behaviour of running applications quickly. An extensive set of cross validation experiments is carried out to demonstrate the viability of the approach, which show that ransomware can be classified accurately with an F1 score of up to 93.8%.

Discovery of a Compound That Inhibits IRE1α S-Nitrosylation and Preserves the Endoplasmic Reticulum Stress Response under Nitrosative Stress.

Inositol-requiring enzyme 1α (IRE1α) is a sensor of endoplasmic reticulum (ER) stress and drives ER stress response pathways. Activated IRE1α exhibits RNase activity and cleaves mRNA encoding X-box binding protein 1, a transcription factor that induces the expression of genes that maintain ER proteostasis for cell survival. Previously, we showed that IRE1α undergoes S-nitrosylation, a post-translational modification induced by nitric oxide (NO), resulting in reduced RNase activity. Therefore, S-nitrosylation of IRE1α compromises the response to ER stress, making cells more vulnerable. We conducted virtual screening and cell-based validation experiments to identify compounds that inhibit the S-nitrosylation of IRE1α by targeting nitrosylated cysteine residues. We ultimately identified a compound (1ACTA) that selectively inhibits the S-nitrosylation of IRE1α and prevents the NO-induced reduction of RNase activity. Furthermore, 1ACTA reduces the rate of NO-induced cell death. Our research identified S-nitrosylation as a novel target for drug development for IRE1α and provides a suitable screening strategy.

Prediction of SO2 concentration at desulfurization outlet of thermal power units based on ReliefF-SC and ISAO-ARELM

Abstract The flue gas desulfurization (FGD) system of thermal power units operates under complex conditions and exhibits significant nonlinearity. Establishing an accurate prediction model for outlet SO2 concentration is crucial for optimizing the control of the FGD system. The study constructs an autoregressive limit learning machine (ARELM) prediction model for SO2 concentration at the desulfurization outlet of thermal power units, leveraging the improved feature selection algorithm ReliefF-SC and the improved snow ablation optimizer (ISAO). Initially, the time delays of the input variables are compensated and the ReliefF-SC algorithm, which incorporates the Spearman correlation coefficient and cosine similarity, is designed to obtain the optimal feature set for predicting SO2 concentration at the outlet. To enhance the ELM's capacity to process time-series data, the autoregressive (AR) concept is integrated into the ELM framework. Furthermore, to mitigate the impact of random initialization of ARELM network parameters on model stability, the ISAO algorithm is proposed by introducing the sine-cosine position update strategy and adaptive adjustment of subpopulation size. Finally, experimental validation is conducted using actual plant operation data. The results indicate that the established SO2 concentration prediction model for desulfurization outlets of thermal power units is highly accurate and offers valuable theoretical guidance and technical support for optimizing the control of the FGD systems.

DRAMP 4.0: an open-access data repository dedicated to the clinical translation of antimicrobial peptides.

Antimicrobial peptides (AMPs) are potential candidates for treating multidrug-resistant bacterial infections, yet only a small number of them have progressed into clinical trials. The main challenges include the poor stability and hemolytic/cytotoxic properties of AMPs. Considering this, in the update of the Data Repository of Antimicrobial Peptides (DRAMP), a new annotation on serum and protease stability is added, and special efforts were made to update the hemolytic/cytotoxic information of AMPs. The DRAMP 4.0 currently holds 30 260 entries (8 001 newly added), consisting of 11 612 general entries, 17 886 patent entries, 96 clinical entries, 377 specific entries, 110 entries with stability data, and 179 expanded entries. A total of 2891 entries possess experimentally determined hemolytic activity information, while 2674 entries contain cytotoxicity data by experimental validation. The update also covers new annotations, statistics, categories, functions, and download links. DRAMP is available online at http://dramp.cpu-bioinfor.org/.

Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to identify novel markers for infantile-onset Pompe disease by analyzing key genes and immune cells infiltrating affected skeletal muscles, building on existing research linking its progression to the immune cell infiltration. The datasets GSE38680 and GSE159062 were downloaded and differential expression genes (DEGs) were identified. Potential markers were screened using support vector machine recursive feature elimination (SVM-RFE) analysis and least absolute shrinkage and selection operator (LASSO) regression models. To analyze DEGs and immune processes, 22 immune cell types in affected tissues were examined using CIBERSORT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment in the calcium signaling pathway, phosphatidylinositol signaling system, ubiquitin-mediated proteolysis and JAK-STAT signaling pathway. Machine learning identified GPNMB, CALML6 and TRIM7 as key genes. Immune cell infiltration analysis showed that the expression levels of the above three genes were closely related to immune cells in our target population under study, including T cells regulatory (Tregs), monocytes, macrophages M0, resting and activated dendritic cells, naive and memory B cells. Overall, our study results may provide new clues for exploring markers related to Pompe disease by highlighting key genes and their relationship with immune infiltration, offering insights into the disease’s development. However, previous studies have shown limited evidence of significant immune cell infiltration in muscle biopsies from Pompe patients, and this lack of direct evidence necessitates further investigation and experimental validation in laboratory settings to confirm these associations.

Exploring the antisnake venom Potential of Zingiber officinale and its bioactive compounds against Naja nigricollis venom through computational approaches and experimental validation.

Snakebite envenomation remains a significant medical challenge, particularly in tropical and subtropical regions. The present study investigates the inhibitory potential of Zingiber officinale (ginger) and its bioactive compounds against Naja nigricollis venom using in silico approaches and animal models. No protection was observed in the in vivo studies but the extract of the plant was able to prolong the time of death with mean survival time ranging from 2.01 - 2.83 hours. In terms of the in vitro studies, the extract was able to significantly (p<0.05) detoxify the N. nigricollis venom by 80 % at the graded doses; standard antisnake venom (ASV) offered 100 % protection to mice. Molecular docking analysis revealed strong binding affinities between the bioactive compounds and the PLA2 enzyme, indicating potential inhibitory effects. The stability and dynamics of the protein-ligand complexes were further validated through molecular dynamics (MD) simulations, which confirmed the persistence of these interactions over time. In conclusion, the findings suggest that the bioactive compounds from Z. officinale could serve as promising inhibitors of PLA2, providing a foundation for the development of novel snakebite envenomation therapies.

Exploring the therapeutic potential of marine actinomycetes: a systems biology-based approach for Alzheimer’s disease treatment

Abstract Background This study addresses the urgent need for novel Alzheimer’s Disease (AD) treatments, focusing on the therapeutic potential of marine Actinomycetes compounds. Current AD therapies provide only symptomatic relief, necessitating a paradigm shift toward more effective interventions. Methodology Ninety-one bioactive compounds were methodically identified from Actinomycetes strains in the Indian Ocean. Rigorous ADME analysis and in silico toxicological screening narrowed the selection to 19 compounds, including Helquinoline, Bonactin, Azamerone, and Arcyriaflavin A. These compounds demonstrated favorable drug-like properties and activity against crucial AD targets. Utilizing network pharmacology, a bioactive-target-disease association network was constructed to unveil intricate relationships between compounds and target proteins in the context of AD. Topological analysis highlighted influential targets such as SRC, MAPK1, EGFR, PRKCA, PRKCD, and CDK2. Protein–Protein Interaction (PPI) mapping revealed interconnected pathways influenced by these compounds. Results Focus narrowed to the top 10 pathways associated with key hub–bottleneck genes. The GnRH signaling, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathways exhibited remarkable fold enrichment, emphasizing their central roles in AD pathogenesis. The GnRH signaling pathway aligned with endocrine dysregulation in AD, EGFR’s dual role in prion-like propagation and amyloid-β pathology, and ErbB signaling’s multifaceted contributions. Conclusion In conclusion, this study presents marine Actinomycetes compounds as potential poly-pharmacological modulators in AD. Despite promising results, cautious optimism is warranted, requiring further experimental validation. The identified compounds and pathways offer a novel perspective, laying the groundwork for targeted interventions within the intricate landscape of AD. This research contributes to advancing AD therapeutics within a systems biology framework, introducing innovative approaches to address this complex neurodegenerative disorder.

Expression and clinical significance of PIKFYVE gene in hepatocellular carcinoma analyzed based on TCGA database and experimental validation

Objective: To investigate the expression and clinical significance of human FYVE finger-containing phosphoinositide kinase (PIKFYVE) in hepatocellular carcinoma (HCC) on the basis of cancer genome atlas (The cancer genome atlas, TCGA) database analysis and clinical samples experimental validation. Methods: Based on the data information of 424 clinical samples (including 374 cases of HCC tissues and 50 cases of nontumorous liver tissues) in the TCGA database, Cox regression analysis and Kaplan-Meier method were used to analyse the relationship between the PIKFYVE mRNA expression and the clinical characteristics, prognosis for survival of HCC patients. The relationship between the PIKFYVE gene and immune cell infiltration was examined by correlation analysis between the PIKFYVE gene and 24 immune cells. In addition, we analysed the correlation between the mRNA expression of PIKFYVE gene and RAC-alpha serine/threonine-protein kinase (AKT1), phosphatase and tensin homolog (PTEN), protein kinase C, alpha (PRKCA), inositol polyphosphate-5-phosphatase (INPP5D), phosphoinositide-3-kinase regulatory subunit 1(PIK3R1), Inositol Polyphosphate 4-phosphatase Type II (INPP4B) and phospholipase-C4 gene (PLCB4) in HCC tissues. Meanwhile, paraffin sections of highly differentiated, moderately differentiated, poorly differentiated, and nontumorous liver tissue in the Department of Pathology of the First Affiliated Hospital of Xinjiang Medical University were collected, each of which was 30 cases, and the histopathological observation was carried out by HE staining, and the expression levels of PIKFYVE and Ki67 proteins were verified by immunohistochemistry in each clinical sample. Results: The expression level of PIKFYVE gene in HCC tumours was significantly higher than that in normal liver tissues (P=0.000 2, P<0.01), and the overall survival of patients in the low PIKFYVE expression group was significantly longer than that in the high expression group (HR=1.57, 95%CI: 1.10～2.25, P=0.014). The results of Univariate Cox regression analysis showed that there was an effect of TNM stage, pathological stage, tumour status and residual tumour on Overall survival (OS) (P<0.05), and the expression level of PIKFYVE had an effect on OS survival (P<0.05); the PIKFYVE prognostic risk model score ratio was HR=1.533 (1.077-2.181, P=0.018). Multivariate Cox regression analysis showed a PIKFYVE prognostic risk model score ratio HR=1.481 (0.886-2.476, P=0.134) and an area under the Receiver Operating Characteristic curve of 0.640, which was greater than 0.5, suggesting that the PIKFYVE prognostic risk model has a predictive value in survival prediction. Correlation analysis showed that the expression level of PIKFYVE was highly correlated with immune cell infiltration and TP53 (P<0.01). The immunohistochemistry staining results showed that the expression of PIKFYVE in HCC tissues was significantly higher than that of nontumorous tissues (P<0.05), and there was a negative correlation with the degree of differentiation. Conclusion: PIKFYVE, as an independent risk factor for HCC, is expected to be developed as a clinical diagnostic biomarker for HCC, which will provide a reference for new drugs for the treatment of HCC.

Transient Sand Scour Dynamics Induced by Pulsed Submerged Water Jets: Simulation Analysis

Water jet scouring technology is extensively applied in marine engineering, harbor maintenance, river training, and various other fields, showcasing a broad spectrum of potential applications. However, achieving a comprehensive understanding of the transient sand scouring characteristics of water jets remains challenging due to the inherent complexity of the coupled flow structure involving submerged jets and environmental fluids, along with the intricate dynamics of two-phase flow. This study, rooted in numerical simulation and experimental validation, introduces pulse characteristics into a submerged jet. A thorough investigation is conducted to explore the transient sand scouring characteristics and sand transport laws of the submerged jet under diverse working conditions. The results of this study revealed that the main reason for the asymmetry of the sand pit morphology is not the non-uniform distribution of sand grains, but more likely caused by turbulence effects. Simultaneously, within the initial 0.25 s of the pulse cycle, suspended sediment resulting from the pulsed jet in the preceding cycle gradually transports to the dune and its surrounding areas. Subsequently, from 0.25 s to 0.5 s, sediment on both sides of the pit’s bottom undergoes movement and amalgamation with the sediment that remained unsettled during the previous cycle. The findings reveal that higher jet velocities significantly enhance sediment suspension, migration, and redeposition, leading to deeper erosion and the rapid formation of the sand pit’s outline within 2 s. Additionally, the jet velocity and the impact distance are identified as critical factors influencing erosion depth and sediment dynamics. These insights advance the understanding of erosion mechanisms driven by pulsed jets, highlighting their impact on sediment transport processes. The research findings provide important guidance for dredging and ocean engineering fields and offer a theoretical basis for improving the understanding of submerged jet scouring mechanisms.