Genetic instability of tumor cells often leads to the occurrence of a large number of mutations, and expression of non-synonymous mutations can produce tumor-specific antigens called neoantigens. ...Neoantigens are highly immunogenic as they are not expressed in normal tissues. They can activate CD4+ and CD8+ T cells to generate immune response and have the potential to become new targets of tumor immunotherapy. The development of bioinformatics technology has accelerated the identification of neoantigens. The combination of different algorithms to identify and predict the affinity of neoantigens to major histocompatibility complexes (MHCs) or the immunogenicity of neoantigens is mainly based on the whole-exome sequencing technology. Tumor vaccines targeting neoantigens mainly include nucleic acid, dendritic cell (DC)-based, tumor cell, and synthetic long peptide (SLP) vaccines. The combination with immune checkpoint inhibition therapy or radiotherapy and chemotherapy might achieve better therapeutic effects. Currently, several clinical trials have demonstrated the safety and efficacy of these vaccines. Further development of sequencing technologies and bioinformatics algorithms, as well as an improvement in our understanding of the mechanisms underlying tumor development, will expand the application of neoantigen vaccines in the future.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or ...fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.
Adipose tissue-derived stem cells (ADSCs) have been shown to enhance wound healing via their paracrine function. Exosomes, as one of the most important paracrine factors, play an essential role in ...this process. However, the concrete mechanisms that underlie this effect are poorly understood. In this study, we aim to explore the potential roles and molecular mechanisms of exosomes derived from ADSCs in cutaneous wound healing.
Normal human skin fibroblasts and ADSCs were isolated from patient skin and adipose tissues. ADSCs were characterized by using flow cytometric analysis and adipogenic and osteogenic differentiation assays. Exosomes were purified from human ADSCs by differential ultracentrifugation and identified by electron microscopy, nanoparticle tracking, fluorescence confocal microscopy and western blotting. Fibroblasts were treated with different concentrations of exosomes, and the synthesis of collagen was analyzed by western blotting; the levels of growth factors were analyzed by real-time quantitative PCR (RT-PCR) and ELISA; and the proliferation and migration abilities of fibroblasts were analyzed by real-time cell analysis, CCK-8 assays and scratch assays. A mouse model with a full-thickness incision wound was used to evaluate the effect of ADSC-derived exosomes on wound healing. The level of p-Akt/Akt was analyzed by western blotting. Ly294002, a phosphatidylinositol 3-kinases (PI3K) inhibitor, was used to identify the underlying mechanisms by which ADSC-derived exosomes promote wound healing.
ADSC-derived exosomes were taken up by the fibroblasts, which showed significant, dose-dependent increases in cell proliferation and migration compared to the behavior of cells without exosome treatment. More importantly, both the mRNA and protein levels of type I collagen (Col 1), type III collagen (Col 3), MMP1, bFGF, and TGF-β1 were increased in fibroblasts after stimulation with exosomes. Furthermore, exosomes significantly accelerated wound healing in vivo and increased the level of p-Akt/Akt in vitro. However, Ly294002 alleviated these exosome-induced changes, suggesting that exosomes from ADSCs could promote and optimize collagen deposition in vitro and in vivo and further promote wound healing via the PI3K/Akt signaling pathway.
This study demonstrates that ADSC-derived exosomes can promote fibroblast proliferation and migration and optimize collagen deposition via the PI3K/Akt signaling pathway to further accelerate wound healing. Our results suggest that ADSCs likely facilitate wound healing via the release of exosomes, and the PI3K/Akt pathway may play a role in this process. Our data also suggest that the clinical application of ADSC-derived exosomes may shed new light on the use of cell-free therapy to accelerate full-thickness skin wound healing and attenuate scar formation.
Schematic diagram shows how the wound healing effect of ADSC-Exos is mediated by the activation of the PI3K/Akt signaling pathways. Display omitted
•ADSC-Exos are internalized into HDFs and regulate their biological behaviors and functions.•ADSC-Exos play an important role in accelerating wound healing via activating PI3K/Akt signaling pathway.•ADSC-Exos may serve as a cell-free therapy for the potential clinical treatment of wound healing.
Node placement is one of the basic problems in a Wireless Sensor Network (WSN). During the operation of a WSN, sensor nodes may fail or die suddenly, which may lead to a coverage hole. To solve this ...problem, the node placement needs to be re-optimized. The dimensions of node placement optimization are high because of the large node number. In view of this defect, a regional optimization dynamic algorithm is put forward. In this paper, the regional optimization problem of node placement is modeled, and a regional optimization dynamic algorithm with a mixed strategy for node placement (MRDA) is proposed. Simulation experiments are carried out for the proposed algorithm and other comparison algorithms. Results of experiments show that the proposed algorithm can greatly reduce the dimensions and narrow the search range, with a significant improvement in the search performance and convergence speed.
ABSTRACT
The mechanism of exosomes derived from activated hepatic stellate cells (HSCs) involved in liver fibrosis is poorly understood. We previously reported that hypoxia‐inducible factor 1 (Hif‐1) ...regulated HSC activation, and, therefore, we investigated in current work whether Hif‐1 regulates exosome secretion and the metabolic switch of HSCs, thus affecting the metabolism of liver nonparenchymal cells. In this study, the characteristics of exosomes from HSCs were assessed via electron microscopy, Western blot analysis, and acetylcholinesterase activity. Confocal microscopy was used to measure the uptake of exosomes by quiescent HSCs, Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs). Hif‐1α was inhibited via 2‐ME or specific small interfering RNAs to investigate its role in exosomes derived from HSCs. It was determined that glucose transporter 1 and pyruvate kinase M2 were increasingly expressed in fibrotic liver samples, cell lysates, and exosomes derived from activated HSCs. Exosomes released from HSCs were associated with activation and glucose uptake of HSCs. Delivery of exosomes from activated HSCs induced glycolysis of quiescent HSCs, KCs, and LSECs. Disruption of Hif‐1 expression suppressed the glycolysis effect delivered by exosomes. Conclusively, our results demonstrated that exosomes secreted by activated HSCs affect the metabolic switch of liver nonparenchymal cells via delivery of glycolysis‐related proteins. These findings represent a novel mechanism that contributes to liver fibrosis and has significant implications for new diagnosis and treatment of liver diseases.—Wan, L., Xia, T., Du, Y., Liu, J., Xie, Y., Zhang, Y., Guan, F., Wu, J., Wang, X., Shi, C. Exosomes from activated hepatic stellate cells contain GLUT1 and PKM2: a role for exosomes in metabolic switch of liver nonparenchymal cells. FASEB J. 33, 8530–8542 (2019). www.fasebj.org
Carbon nanomaterials derived from biomass are considered as important sustainable energy carriers. In this study, we report an approach to synthesize cobalt/nitrogen doped carbon nanotubes (Co-NCNTs) ...for high oxygen reduction reaction (ORR) activity by cobalt catalyzed carbonization of biomass chitosan. It is found that the existence of cobalt results in the transition of graphene-like carbon nanosheets to tubular graphitic carbons. Moreover, a strong chemical bonding of cobalt with nitrogen and carbon in Co-NCNTs is found, which is important for enhancing the ORR activity. The Co-NCNT catalyst under optimized synthetic conditions displays attractive ORR activity superior to those of commercial Pt/C catalysts. Furthermore, the mechanism behind the enhanced ORR activity has also been studied. This study provides a feasible synthesis approach for the scalable production of biomass derived high performance carbon based ORR catalysts.
Biomass chitosan was used for the scalable synthesis of cobalt/nitrogen doped carbon nanotube composites with impressive oxygen reduction reaction activity and stability.
The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in ...various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.
Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for ...microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain-specific deep neural networks. Here, we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images, in which different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information as its input: (1) autofluorescence images of the label-free tissue sample and (2) a "digital staining matrix", which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H&E), Jones' silver stain, and Masson's trichrome stain. Using a single network, this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section, which is currently not feasible with standard histochemical staining methods.
Single-cell RNA sequencing in cancer research Zhang, Yijie; Wang, Dan; Peng, Miao ...
Journal of experimental & clinical cancer research,
03/2021, Letnik:
40, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by ...mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.
To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in ...Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.
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