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Resistance to immune checkpoint inhibitors in KRAS-mutant non-small cell lung cancer
No full textNon-small cell lung cancer (NSCLC) patients with Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation are associated with significant clinical heterogeneity and a poor prognosis to standard NSCLC therapies such as surgical resection, radiotherapy, chemotherapies, and targeted medicines. However, the application of immune checkpoints inhibitors (ICIs) has dramatically altered the therapeutic pattern of NSCLC management. Clinical studies have indicated that some KRAS-mutant NSCLC patients could benefit from ICIs; however, the responses in some patients are still poor. This review intends to elucidate the mechanisms of resistance to immunotherapy in KRAS-driven NSCLC and highlight the TME functions altered by immunoinhibitors, immunostimulators, and cancer metabolism. These metabolic pathways could potentially be promising approaches to overcome immunotherapy resistance
Tumor-derived exosomes: immune properties and clinical application in lung cancer
No full textLung cancer is the leading cause of cancer-related death worldwide. Despite advances in diagnosis and treatment of lung cancer, the overall survival remains poor. Evidence indicates that lung cancer development is a complex and dynamic process that involves interactions between tumor cells and their microenvironments, including immune cells. Exosomes are small extracellular vesicles secreted by most cell types; they contain functional molecules that allow intercellular communication. Tumor-derived exosomes (TEXs) carry both immunosuppressive and immunostimulatory mediators and may be involved in various immunomodulatory effects. TEXs, which partially mimic profiles of the parent cells, are a potential source of cancer biomarkers for prognosis, diagnosis, and prediction of response to therapy. In addition, TEXs may interfere with immunotherapies, but they also could be used as adjuvants and antigenic components in vaccines against lung cancer. In the context of lung cancer, identifying TEXs and understanding their contribution to tumorigenesis and the response to immunotherapies represents a challenging research area
Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm
No full textAim: Currently, the obesity epidemic is one of the biggest problems for human health. Obesity is impacted on survival in patients with breast cancer. However, key biomarkers of obesity-related breast cancer risk are still not well known. Thus, using machine learning to identify the most appropriate features in obesity-associated breast cancer patients may improve the predictive accuracy and interpretability of regression models.Methods: In the present study, we identified 23 differentially expressed genes (DEGs) from the GSE24185 transcriptome dataset. Seed genes were identified from DEGs, the co-expression network genes and hub genes of the protein-protein interaction network. Pathway enrichment analysis was performed for DEGs. The Ridge penalty regression model was executed by using P-values of enriched pathways and seed gene pathway association score to obtain the most relevant molecular signatures. The model was performed using 10-fold cross-validation to fit the penalized models.Results: Angiotensin II receptor type 1 (AGTR1), cyclin D1 (CCND1), glutamate ionotropic receptor AMPA type subunit 2 (GRIA2), interleukin-6 cytokine family signal transducer (IL6ST), matrix metallopeptidase 9 (MMP9), and protein kinase CAMP-dependent type II regulatory subunit beta (PRKAR2B) were considered as candidate molecular signatures of obese patients with breast cancer. In addition, RAF-independent MAPK1/3 activation, collagen degradation, bladder cancer, drug metabolism-cytochrome P450, and signaling by Hedgehog pathways in cancer were primarily associated with obesity-associated breast cancer.Conclusion: These genes may be used for risk analysis of the disease progression of obese patients with breast cancer. Corresponding genes and pathways should be validated via experimental studies
Zebrafish models of inherited retinal dystrophies
No full textInherited retinal degenerations (IRDs) cause permanent vision impairment or vision loss due to the death of rod and cone photoreceptors. Animal models of IRDs have been instrumental in providing knowledge of the pathological mechanisms that cause photoreceptor death and in developing successful approaches that could slow or prevent vision loss. Zebrafish models of IRDs represent an ideal model system to study IRDs in a cone-rich retina and to test strategies that exploit the natural ability to regenerate damaged neurons. This review highlights those zebrafish mutants and transgenic lines that exhibit adult-onset retinal degeneration and serve as models of retinitis pigmentosa, cone-rod dystrophy, and ciliopathies
Stem cell-based therapy for myopic maculopathy: a new concept
No full textMyopia has reached epidemic proportions in the world, especially in East Asia. Pathologic myopia is an extreme type of high myopia that can cause irreversible blindness. Myopic maculopathy is one of the characteristics of pathologic myopia. Nowadays, limited treatments can preserve the visual outcome of these patients. We review the current treatment in practice for myopic maculopathy. Furthermore, based on the current stem cell-based therapy used in degenerative ocular diseases, we discuss a new concept of stem cell therapy for myopic maculopathy
Trends summarization of times series: a multi-objective genetic algorithm-based model
No full textAim: The explosion of the amounts of data generated in many application domains, makes the paradigm of data summarization more essential. Furthermore, it is of a great interest to effectively handle some specific needs. In this work, we discuss an advanced model to drive linguistic summarization in the context of time series. This model relies on a multi-objective genetic algorithm mechanism to generate a set of best summaries from a large number of candidates.Methods: To achieve this objective, the current work is divided into two parts: The first part is dedicated for extracting the linguistic summaries of the dynamic characteristics of the trends of time series. It is achieved using the traditional genetic algorithm where the fitness function represents the truth degree of the linguistic quantified proposition. The second part is devoted to formalise the problem of interest as a multi-criteria optimization problem. We use different quality measures of summary as targets for improving the predicted set of summaries. To reach this goal, we use the Fast Non-Dominated Sorting Genetic Algorithm NSGA-II.Results: We evaluate the proposed approach on real data from a Smart Campus application (Neocampus project of the University of Toulouse, France). The results are promising and confirming the usability of the proposed approach.Conclusion: The proposed approach overcomes the problem of the overabundance of irrelevant linguistic summaries of the time series. It allows selecting a set of best summaries regarding some relevant criteria
Molecular chemisorption: a new conceptual paradigm for hydrogen storage
No full textDeveloping efficient hydrogen storage materials and the corresponding methods is the key to successfully realizing the “hydrogen economy”. The ideal hydrogen storage materials should be capable of reversibly ab-/desorbing hydrogen under mild temperatures with high hydrogen capacities. To achieve this target, the ideal enthalpy of adsorption is determined to be 15-50 kJ/mol for hydrogen storage. However, the current mainstream methods, including molecular physisorption and atomic chemisorption, possess either too high or too low enthalpy of hydrogen adsorption, which are not suitable for practical application. To this end, hydrogen storage via molecular chemisorption is perceived to regulate the adsorption enthalpy with intermediate binding energy between the molecular physisorption and atomic chemisorption, enabling the revisable hydrogen ad-/desorption possible under ambient temperatures. In this review, we will elaborate the molecular chemisorption as a new conceptual paradigm and materials design to advance future solid-state hydrogen storage
The use and applicability of Internet search queries for infectious disease surveillance in low- to middle-income countries
No full textUncontrolled outbreaks of emerging infectious diseases can pose threats to livelihoods and can undo years of progress made in developing regions, such as Sub-Saharan Africa. Therefore, the surveillance and early outbreak detection of infectious diseases, e.g., Dengue fever, is crucial. As a low-cost and timely source, Internet search queries data [e.g., Google Trends data (GTD)] are used and applied in epidemiological surveillance. This review aims to identify and evaluate relevant studies that used GTD in prediction models for epidemiological surveillance purposes regarding emerging infectious diseases. A comprehensive literature search in PubMed/MEDLINE was carried out, using relevant keywords identified from up-to-date literature and restricted to low- to middle-income countries. Eight studies were identified and included in the current review. Three focused on Dengue fever, three analyzed Zika virus infections, and two were about COVID-19. All studies investigated the correlation between GTD and the cases of the respective infectious disease; five studies used additional (time series) regression analyses to investigate the temporal relation. Overall, the reported positive correlations were high for Zika virus (0.75-0.99) or Dengue fever (0.87-0.94) with GTD, but not for COVID-19 (-0.81 to 0.003). Although the use of GTD appeared effective for infectious disease surveillance in low- to middle-income countries, further research is needed. The low costs and availability remain promising for future surveillance systems in low- to middle-income countries, but there is an urgent need for a standard methodological framework for the use and application of GTD
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