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The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug–drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug–drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience. [ABSTRACT FROM AUTHOR]

Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy. [ABSTRACT FROM AUTHOR]

The prognosis of patients with malignant melanoma has been improved in recent decades due to advancements in immunotherapy. However, a considerable proportion of patients are refractory to treatment, particularly at advanced stages. This underscores the necessity of developing a new strategy to improve it. Alternative polyadenylation (APA), as a marker of crucial posttranscriptional regulation, has emerged as a major new type of epigenetic marker involved in tumorigenesis. However, the potential roles of APA in shaping the tumor microenvironment (TME) are largely unexplored. Herein, we collected two cohorts comprising melanoma patients who received immune checkpoint inhibitor (ICI) immunotherapy to quantify transcriptome-wide discrepancies in APA. We observed a global change in 3′-UTRs between responders and non-responders, which might involve DNA damage response, angiogenesis, PI3K-AKT signaling pathways, etc. Ten putative master APA regulatory factors for those APA events were detected via a network analysis. Notably, we established an immune response-related APA scoring system (IRAPAss), which exhibited a great performance of predicting immunotherapy response in multiple cohorts. Furthermore, we examined the correlation of APA with TME at the single-cell level using four single-cell immune profiles of tumor-infiltrating lymphocytes (TILs), which revealed an overall discrepancy in 3′-UTR length across diverse T cell populations, probably contributing to immunoregulation in melanoma. In conclusion, our study provides a transcriptional landscape of APA implicated in immunoregulation, which might lay the foundation for developing a new strategy for improving immunotherapy response for melanoma patients by targeting APA. [ABSTRACT FROM AUTHOR]

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell–cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell–cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell–cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell). [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions. [ABSTRACT FROM AUTHOR]

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore). [ABSTRACT FROM AUTHOR]

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy. [ABSTRACT FROM AUTHOR]

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway). [ABSTRACT FROM AUTHOR]

Reversible plows are one of the important and efficient tools in primary tillage, which are affected by many dynamic loads. These tools are damaged in different working conditions, and they are damaged from the stem area. For this reason, in this research, a method based on the finite element method and genetic algorithm was presented to optimize the reversible plow shaft. In this research, two parameters of cross-sectional area and stem curvature were investigated as independent variables. A total of 24 different models for the plow shaft were designed in SolidWorks software, FEM software and used Iron Cow Stem. Then, the different designs of the stem in the environment of the abacus were loaded and stress free occurred in them and were eliminated. Then, using an artificial neural network, a model was presented to estimate the von Mises tension based on the information related to the cross-sectional area and stem curvature, and this model was able to estimate the maximum von Mises tension with an accuracy of 99%. Then the mentioned model was linked with the genetic algorithm and it was used to optimize the plow shaft. After selecting the optimized model through the genetic algorithm, the plow shaft was designed again and the tireless stress that occurred in it under the same loading conditions as the previous conditions was eliminated. The results showed that the amount of maximum stress in this model decreased by 6% compared to the previous models and the best stem designs is (Stress (MPa) VonMises=295.2). [ABSTRACT FROM AUTHOR]

The light-dark cycle significantly impacts the growth and development of animals. Mantis shrimps (Oratosquilla oratoria) receive light through their complex photoreceptors. To reveal the adaptive expression mechanism of the mantis shrimp induced in a dark environment, we performed comparative transcriptome analysis with O. oratoria cultured in a light environment (Oo-L) as the control group and O. oratoria cultured in a dark environment (Oo-D) as the experimental group. In the screening of differentially expressed genes (DEGs) between the Oo-L and Oo-D groups, a total of 88 DEGs with |log2FC| > 1 and FDR < 0.05 were identified, of which 78 were upregulated and 10 were downregulated. Then, FBP1 and Pepck were downregulated in the gluconeogenesis pathway, and MKNK2 was upregulated in the MAPK classical pathway, which promoted cell proliferation and differentiation, indicating that the activity of mantis shrimp was slowed and the metabolic rate decreases in the dark environment. As a result, the energy was saved for its growth and development. At the same time, we performed gene set enrichment analysis (GSEA) on all DEGs. In the KEGG pathway analysis, each metabolic pathway in the dark environment showed a slowing trend. GO was enriched in biological processes such as eye development, sensory perception and sensory organ development. The study showed that mantis shrimp slowed down metabolism in the dark, while the role of sensory organs prominent. It provides important information for further understanding the energy metabolism and has great significance to study the physiology of mantis shrimp in dark environment. [ABSTRACT FROM AUTHOR]

Verticillium dahliae is a soil-borne hemibiotrophic fungal pathogen that threatens cotton production worldwide. In this study, we assemble the genomes of two V. dahliae isolates: the more virulence and defoliating isolate V991 and nondefoliating isolate 1cd3-2. Transcriptome and comparative genomics analyses show that genes associated with pathogen virulence are mostly induced at the late stage of infection (Stage II), accompanied by a burst of reactive oxygen species (ROS), with upregulation of more genes involved in defense response in cotton. We identify the V991-specific virulence gene SP3 that is highly expressed during the infection Stage II. V. dahliae SP3 knock-out strain shows attenuated virulence and triggers less ROS production in cotton plants. To control the disease, we employ polyethyleneimine-coated MXene quantum dots (PEI-MQDs) that possess the ability to remove ROS. Cotton seedlings treated with PEI-MQDs are capable of maintaining ROS homeostasis with enhanced peroxidase, catalase, and glutathione peroxidase activities and exhibit improved tolerance to V. dahliae. These results suggest that V. dahliae trigger ROS production to promote infection and scavenging ROS is an effective way to manage this disease. This study reveals a virulence mechanism of V. dahliae and provides a means for V. dahliae resistance that benefits cotton production. Verticillum wilt is an important cotton disease caused by fungal pathogen Verticillium dahiae. Here, the authors assemble the genomes of defoliating and non-defoliating isolates of the pathogen, identify virulence gene SP3, and develop a disease control strategy using polyethyleneimine-coated MXene quantum dots. [ABSTRACT FROM AUTHOR]

The incidence of congenital heart diseases (CHDs) shows an increasing trend and results in large health burdens in China. However, there have been inconsistent results of the relationship between lead (Pb) level and risk of CHDs. We performed a pair-matched case–control study and included 97 cases and 194 controls to investigate the association between pregnancy Pb exposure and the risk of CHDs in a birth cohort study conducted in Lanzhou, China. The results showed that compared to the lowest Pb tertile, both highest tertile levels of maternal blood and umbilical cord blood Pb were associated with an increased risk of CHDs. The similar significant results were found in cases with isolated CHDs. Compared to both lowest tertiles of Pb level in maternal blood and umbilical cord blood, the highest tertile was associated with an increased risk of CHDs, especially for isolated CHDs. Overall, our study suggests a significant association between pregnancy Pb exposure and risk of CHDs, especially for isolated CHDs. Future studies are needed to elucidate the underlying mechanism. [ABSTRACT FROM AUTHOR]

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners' realization. The teaching evaluation of traditional vocational skill training requires time, workforce, and educational resources. Due to the limitations of experimental conditions, it is easy to ignore the procedural characteristics of skill training and difficult to implement the procedural evaluation. Based on the above problems, combined with virtual reality and the parts of vocationally skilled auto repair training specialty, using machine learning methods, engineering design of process evaluation method for skilled auto repair training, and takes the secondary vocational auto repair specialty as an example, constructs an evaluation index model based on KSA theoretical model, and evaluates three dimensions: knowledge acquisition, skill mastery, and ability cultivation (knowledge, skill, ability, KSA). The experimental verification of the process evaluator is carried out in the theoretical training evaluation auto repair system (TTE) based on virtual reality. The experimental results can effectively evaluate the practical training of students. The research results of this paper provide a new perspective and reference for the learning evaluation of skill-based training majors. [ABSTRACT FROM AUTHOR]