Abstract One of the prevalent chronic inflammatory disorders of the nasal mucosa, allergic rhinitis (AR) has become more widespread in recent years. Acupuncture pterygopalatine ganglion (aPPG) is an ...emerging alternative therapy that is used to treat AR, but the molecular mechanisms underlying its anti-inflammatory effects are unclear. This work methodically demonstrated the multi-target mechanisms of aPPG in treating AR based on bioinformatics/topology using techniques including text mining, bioinformatics, and network topology, among others. A total of 16 active biomarkers and 108 protein targets related to aPPG treatment of AR were obtained. A total of 345 Gene Ontology terms related to aPPG of AR were identified, and 135 pathways were screened based on Kyoto Encyclopedia of Genes and Genomes analysis. Our study revealed for the first time the multi-targeted mechanism of action of aPPG in the treatment of AR. In animal experiments, aPPG ameliorated rhinitis symptoms in OVA-induced AR rats; decreased serum immunoglobulin E, OVA-sIgE, and substance P levels; elevated serum neuropeptide Y levels; and modulated serum Th1/Th2/Treg/Th17 cytokine expression by a mechanism that may be related to the inhibition of activation of the TLR4/NF-κB/NLRP3 signaling pathway. In vivo animal experiments once again validated the results of the bioinformatics analysis. This study revealed a possible multi-target mechanism of action between aPPG and AR, provided new insights into the potential pathogenesis of AR, and proved that aPPG was a promising complementary alternative therapy for the treatment of AR.
Abstract Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the ...spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.
Abstract Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer ...cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy’s effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.
Abstract In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological ...predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox’s proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer’s disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.
Abstract Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the ...thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.
The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation ...of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152:2012 LADM) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.
Poser le diagnostic de crise non épileptique est difficile en absence de vidéo-électroencéphalogramme. La commission d´expert de la ligue international contre l´épilepsie propose une démarche ...diagnostic permettant de poser le diagnostic selon un degré de certitude avec ou en absence de vidéo-électroencéphalogramme. Notre objectif était de déterminer la fréquence hospitalière des crises non épileptiques psychogènes en absence de vidéo-électroencéphalogramme. A l´aide du registre de consultation externe, nous avons identifié les patients suivis pour épilepsie avec deux électroencéphalographies inter-critiques normaux, entre janvier 2020 et octobre 2021. Un examen des dossiers médicaux des patients et une évaluation de la validité du diagnostic ont été effectués. Sur 64 patients évalués avec électroencéphalogramme intercritique normal, 19 ont été inclus comme souffrant de crise non épileptique psychogène, soit 26,68%. La moyenne d´âge était de 23,94 +/- 9,4 ans. Les femmes représentaient 68,4%. Les patients suivis en neurologie ont représenté 84%. Un antécédent de traumatisme dans l´enfance était retrouvé dans (47,4 %). La première crise était précédée d´événements stressants dans 47,36%. Le trouble stress posttraumatique était le plus représenté avec 73,7% des cas. L´âge moyen était de 20,95 +/-9,8 ans pour la première la crise et la durée moyenne d´évolution des crises était de 3 ans +/- 2 ans. Cette étude illustre la possibilité de poser un diagnostic présomptif de crise non-épileptique psychogène en absence de vidéo-EEG.
Fertilizantes Tocantins was founded by José Eduardo Motta in 2003, out of his desire of taking advantage of the fast-growing agricultural businesses in the northern region of Brazil. He had previous ...knowledge of the fertilizer sector due to his work in his father’s business for several years. After an impressive success and enormous growth rates until 2016, 50% plus one shares of the company were sold to EuroChem, a European fertilizer producer. This was part of Fertilizantes Tocantins’ strategy to achieve even greater and sustainable growth. In 2020 EuroChem bought the remaining shares of Fertilizantes Tocantins, becoming its sole controller. The company still maintains most of its operational structure and now is fueled with capital and other assets from EuroChem. The challenge ahead lies in being able to maintain the organizational culture and winning position while bringing the best of its controller’s assets to achieve the goal of being a leader in the Brazilian fertilizer market.
