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Background Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results. However, it only focuses on sequence information while ignoring the structural information of PPI networks. Structural information of PPI networks such as their degree, position, and neighboring nodes in a graph has been proved to be informative in PPI prediction. Results Facing the challenge of representing graph information, we introduce an improved graph representation learning method. Our model can study PPI prediction based on both sequence information and graph structure. Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods. Statistically, Our method achieves state-of-the-art accuracy of 99.15% on Human protein reference database (HPRD) dataset and also obtains best results on Database of Interacting Protein (DIP) Human, Drosophila , Escherichia coli ( E. coli ), and Caenorhabditis elegans ( C. elegan ) datasets. Conclusion Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved graph representation learning method, to automatically learn to encode graph structure into low-dimensional embeddings. Experimental results demonstrate that our method outperforms other existing sequence-based methods on several datasets. We also prove the robustness of our model for very sparse networks and the generalization for a new dataset that consists of four datasets: HPRD, E.coli , C.elegan , and Drosophila .

Activator protein-1 (AP-1) is a transcription factor that consists of a diverse group of members including Jun, Fos, Maf, and ATF. AP-1 involves a number of processes such as proliferation, migration, and invasion in cells. Dysfunctional AP-1 activity is associated with cancer initiation, development, invasion, migration and drug resistance. Therefore, AP-1 is a potential target for cancer targeted therapy. Currently, some small molecule inhibitors targeting AP-1 have been developed and tested, showing some anticancer effects. However, AP-1 is complex and diverse in its structure and function, and different dimers may play different roles in different type of cancers. Therefore, more research is needed to reveal the specific mechanisms of AP-1 in cancer, and how to select appropriate inhibitors and treatment strategies. Ultimately, this review summarizes the potential of combination therapy for cancer.

Facial expression recognition faces great challenges due to factors such as face similarity, image quality, and age variation. Although various existing end-to-end Convolutional Neural Network (CNN) architectures have achieved good classification results in facial expression recognition tasks, these network architectures share a common drawback that the convolutional kernel can only compute the correlation between elements of a localized region when extracting expression features from an image. This leads to difficulties for the network to explore the relationship between all the elements that make up a complete expression. In response to this issue, this article proposes a facial expression recognition network called HFE-Net. In order to capture the subtle changes of expression features and the whole facial expression information at the same time, HFE-Net proposed a Hybrid Feature Extraction Block. Specifically, Hybrid Feature Extraction Block consists of parallel Feature Fusion Device and Multi-head Self-attention. Among them, Feature Fusion Device not only extracts the local information in expression features, but also measures the correlation between distant elements in expression features, which helps the network to focus more on the target region while realizing the information interaction between distant features. And Multi-head Self-attention can calculate the correlation between the overall elements in the feature map, which helps the network to extract the overall information of the expression features. We conducted a lot of experiments on four publicly available facial expression datasets and verified that the Hybrid Feature Extraction Block constructed in this paper can improve the network’s recognition ability for facial expressions.

Background Intratumor heterogeneity (ITH) is associated with tumor progression, unfavorable prognosis, immunosuppression, genomic instability, and therapeutic resistance. Thus, evaluation of ITH levels is valuable in cancer diagnosis and treatment. Methods We proposed a new mRNA-based ITH evaluation algorithm (DEPTH2) without reference to normal controls. DEPTH2 evaluates ITH levels based on the standard deviations of absolute z-scored transcriptome levels in tumors, reflecting the asynchronous level of transcriptome alterations relative to the central tendency in a tumor. Results By analyzing 33 TCGA cancer types, we demonstrated that DEPTH2 ITH was effective in measuring ITH for its significant associations with tumor progression, unfavorable prognosis, genomic instability, reduced antitumor immunity and immunotherapy response, and altered drug response in diverse cancers. Compared to other five ITH evaluation algorithms (MATH, PhyloWGS, ABSOLUTE, DEPTH, and tITH), DEPTH2 ITH showed a stronger association with unfavorable clinical outcomes, and in characterizing other properties of ITH, such as its associations with genomic instability and antitumor immunosuppression, DEPTH2 also displayed competitive performance. Conclusions DEPTH2 is expected to have a wider spectrum of applications in evaluating ITH in comparison to other algorithms.

Exosomes are nanoscale extracellular vesicles secreted by cells and enclosed by a lipid bilayer membrane containing various biologically active cargoes such as proteins, lipids, and nucleic acids. Engineered exosomes generated through genetic modification of parent cells show promise as drug delivery vehicles, and they have been demonstrated to have great therapeutic potential for treating cancer, cardiovascular, neurological, and immune diseases, but systematic knowledge is lacking regarding optimization of drug loading and assessment of delivery efficacy. This review summarizes current approaches for engineering exosomes and evaluating their drug delivery effects, and current techniques for assessing exosome drug loading and release kinetics, cell targeting, biodistribution, pharmacokinetics, and therapeutic outcomes are critically examined. Additionally, this review synthesizes the latest applications of exosome engineering and drug delivery in clinical translation. The knowledge compiled in this review provides a framework for the rational design and rigorous assessment of exosomes as therapeutics. Continued advancement of robust characterization methods and reporting standards will accelerate the development of exosome engineering technologies and pave the way for clinical studies. Graphical Abstract

This paper assessed the antifungal effects of sage oil, lavender oil, mint oil, and tea tree oil on the postharvest fungal pathogen, Botrytis cinerea, which causes gray molds. The change of morphology, physiological, and biochemical characteristics about fungal hyphae and conidia were determined. As results show, all four oils can effectively inhibit the growth of B.cinerea and the antifungal effects are dose dependent. The best antifungal effect has been found from mint oil. According to in vitro studies, volatile vapor worked better than direct contact. With volatile vapor, the growth of B.cinerea was inhibited completely at 2 for all four oils. Mint oil at 500 μL/L and its volatile vapor at 25 could inhibit both conidia germination and disease incidence significantly in vivo. Observations by using scanning electron microscope and transmission electron microscope revealed that, mint oil could destroy the ultrastructure of hyphae and conidia, resulting in markedly shriveling and crinkling of the hyphae and conidia. It could also thicken and disrupt cell wall, causing cellular nucleic acids and proteins divulged with the damage of the cell wall. The chemical composition analysis of mint oil using GC/MS revealed that its main components were cyclohexanol, cyclohexanone, and some alkenes and alkanes. The majority of the components were effective antifungal agents. The content of volatile cyclohexanol and cyclohexanone were found to be 39.79% and 22.24% respectively.

The efficacy and safety of PD-1/PD-L1 inhibitors combined with CTLA-4 inhibitors in the treatment of advanced colorectal cancer is controversial. This meta-analysis aimed to evaluate the efficacy and safety of PD-1/PD-L1 inhibitors combined with CTLA-4 inhibitors for advanced colorectal cancer. PubMed, Embase, the Cochrane Library, and Web of Science databases were systematically searched for relevant studies. Outcomes including median progression-free survival (mPFS), median overall survival (mOS), overall response rate (ORR), disease control rate (DCR), treatment-related adverse events (TRAEs) and ≥grade 3 TRAEs were extracted for further analysis. The risk of bias was assessed by subgroup analysis. 12 articles with 566 patients were identified and subjected to meta-analysis. With regard to survival analysis, the pooled mOS and mPFS were 6.66 months (95%CI 4.85-9.16) and 2.92 months (95%CI 2.23-3.83), respectively. In terms of tumor response, the pooled ORR and DCR were 21% (95%CI 6%-41%) and 49% (95%CI 27%-71%), respectively. The pooled AEs rate and ≥ grade 3 AEs rate were 94% (95%CI 86%-99%) and 44% (95%CI 30%-58%). PD-1/PD-L1 inhibitors combined with CTLA-4 inhibitors have shown promising clinical responses in the treatment of colorectal cancer (CRC). Although the incidence of adverse reactions is high, they are generally tolerable. https://inplasy.com/, identifier INPLASY202480030.

Exosomes are nanoscale extracellular vesicles present in bodily fluids that mediate intercellular communication by transferring bioactive molecules, thereby regulating a range of physiological and pathological processes. Exosomes can be secreted from nearly all cell types, and the biological function of exosomes is heterogeneous and depends on the donor cell type and state. Recent research has revealed that the levels of exosomes released from the endosomal system increase in cells undergoing programmed cell death. These exosomes play crucial roles in diseases, such as inflammation, tumors, and autoimmune diseases. However, there is currently a lack of systematic research on the differences in the biogenesis, secretion mechanisms, and composition of exosomes under different programmed cell death modalities. This review underscores the potential of exosomes as vital mediators of programmed cell death processes, highlighting the interconnection between exosome biosynthesis and the regulatory mechanisms governing cell death processes. Furthermore, we accentuate the prospect of leveraging exosomes for the development of innovative biomarkers and therapeutic strategies across various diseases.

The tourism industry’s explosive growth has triggered severe carbon emission issues, making enhancing tourism carbon efficiency (TCE) a pressing concern for achieving sustainable tourism development. The widespread application of artificial intelligence (AI) in tourism presents new opportunities. This study applies the Environmental Kuznets Curve (EKC) theory to examine the pathways and mechanisms of AI’s impact on TCE, with a focus on China. The findings reveal that AI significantly enhances TCE, where improvements in tourism labor productivity, the rationalization of the tourism industry structure, and advancements in tourism technology are the key channel mechanisms. Heterogeneity tests indicate that AI substantially boosts TCE in eastern developed regions and areas with deficient tourism resource endowments. Furthermore, AI exhibits significant spatial spillover effects, enhancing both local and neighboring regions’ TCE. These insights provide crucial policy implications for utilizing AI to promote China’s sustainable tourism industry.

Atherosclerosis is characterized as a chronic inflammatory response to cholesterol deposition in arteries. Low-density lipoprotein (LDL), especially the oxidized form (ox-LDL), plays a crucial role in the occurrence and development of atherosclerosis by inducing endothelial cell (EC) dysfunction, attracting monocyte-derived macrophages, and promoting chronic inflammation. However, the mechanisms linking cholesterol accumulation with inflammation in macrophage foam cells are poorly understood. Long non-coding RNAs (lncRNAs) are a group of non-protein-coding RNAs longer than 200 nucleotides and are found to regulate the progress of atherosclerosis. Recently, many lncRNAs interfering with cholesterol deposition or inflammation were identified, which might help elucidate their underlying molecular mechanism or be used as novel therapeutic targets. In this review, we summarize and highlight the role of lncRNAs linking cholesterol (mainly ox-LDL) accumulation with inflammation in macrophages during the process of atherosclerosis.