4,553 research outputs found

    Systemic function impairment and neurodegeneration in the general population

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    Investigating the impact of lung cancer cell-of-origin on tumour metabolic phenotype and heterogeneity

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    Non-small-cell lung cancer has been described as highly heterogenous which results in different metabolic phenotypes. There are multiple factors which contribute to this heterogeneity, one of which is the tumour cell-of-origin. In the lung, there are five cell types reported to be cells-of-origin: alveolar epithelial type 2, club, basal, neuroendocrine and bronchioalveolar stem cells. This project focuses on the interaction between the cell-of-origin and the metabolic phenotype of lung cancer, and we aim to assess the contribution of the cell-of-origin to lung cancer metabolic resultant phenotype and heterogeneity. To accomplish this, we have established two complementary model systems, one in vitro and one in vivo. In our in vitro model, we isolated specific lung cell types, including AT2 cells, basal cells, and club cells, utilising their unique cell surface markers. By introducing oncogenic KRAS mutations and deleting the P53 gene, we are creating lineage-restricted organoids. These organoids will serve as valuable tools for characterizing the metabolic aspects of tumours arising from different cell-of-origin backgrounds within an in vitro setting. In our in vivo model, we induced NSCLC tumours in mice with genetic modifications using viral vectors, namely Ad5-mSPC-Cre, Ad5-CC10-Cre, and Ad5- bk5-Cre. These vectors are selectively expressed in AT2, club, and basal cells, respectively. To ensure the validity of our comparisons, we have carefully monitored tumour growth dynamics and burden in these mouse models. Our comprehensive analysis has revealed three distinct transcriptomic subtypes (S1, S2, and Acetate) within these NSCLC tumours. Notably, S1 and Acetate subtypes are enriched in tumours originating from specific cell types. Positron emission tomography (PET) imaging has unveiled metabolic variations, with S1 tumours displaying heightened [18F]FDG uptake and the Acetate subtype exhibiting increased [11C]acetate uptake. Furthermore, our multi-omics approach, encompassing transcriptomics, proteomics, and metabolomics, has exposed disparities in critical metabolic pathways, such as glycolysis, hypoxia response, and apoptosis. In summary, our research provides a comprehensive examination of the metabolic heterogeneity of NSCLC based on the cell-of-origin independently of genomic alterations

    Towards personalized medicine for metastatic urothelial cancer

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    Cellular and molecular mechanisms of inflammatory arthritis and fibromyalgia

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    In Study I, we examined the impact of the hR100E-NGF mutation on inflammatory pain and bone erosion in both female and male mice. Our findings indicate that the hR100E-NGF mutation did not affect the development of the peripheral sensory nervous system at the lumbar DRG, sciatic nerve, ankle joint, or glabrous skin. Moreover, hR100E-NGF mice displayed sensory thresholds similar to those of the hWT-NGF mice in response to mechanical, heat, or cold stimulation under normal conditions. The hR100E-NGF and hWT-NGF mice developed comparable mechanical and heat sensitivity impairments after the intra-articular injection of complete Freund’s adjuvant. Notably, the hR100E-NGF mice were insensitive to nociceptive stimulation in the deeper tissues assessed by weight bearing and gait analysis. Furthermore, mRNA analysis from the inflamed joint showed a differential sex-dependent gene expression profile between hR100E-NGF female and male mice. Finally, the hR100E-NGF female but not the male mice were protected against the CFA-bone erosion. These data collectively demonstrate that the R100E NGF mutation effectively protects against joint pain-like behaviors in both male and female mice while providing bone protection exclusively to female mice in a monoarthritis model. We propose that manipulating the signaling of NGF and its receptors in a manner similar to the R100E mutation could be a promising approach to treating chronic pain and maintaining bone health, particularly in women. Study II investigated the effects of injecting purified IgG from fibromyalgia (FM) patients and healthy controls (HC) in mice. We found that the injection of FM IgG but not IgG from healthy controls (HC) induces pressure, mechanical, and cold hypersensitivity in mice that were coupled to enhanced nociceptor responsiveness to mechanical and cold stimulation. The FM IgG-injected mice also developed impaired muscular strength and decreased locomotor activity. Moreover, FM IgG bound and stimulated satellite glial cells (SGCs) in vivo and in vitro. No FM or HC IgG accumulation was found in the brain or spinal cord of the injected mice. Our study also demonstrated that FM IgG can bind to satellite glial cells and neurons in the human DRG. In addition, we observed a significant reduction in the intraepidermal nerve fiber density in the mice 14 days after the FM IgG injection. Our results suggest that transferring FM IgG into mice can replicate some peripheral FM symptoms. This study can provide a valuable animal model for studying the peripheral physiology of FM. Our discovery could significantly advance the understanding and treatment of fibromyalgia and other related conditions. However, more research is needed to understand the cellular and molecular mechanisms involved in FM-IgG-mediated changes in mice. Study III aimed to investigate the frequency of anti-satellite glial cell (SGC) antibodies and the antibody association with the disease severity in FM patients. We used serum (Karolinska Institutet, Sweden; n=30/group) and plasma (McGill University, Canada; n=35/group) samples collected from FM patients and HCs. Our results showed a higher binding intensity of the FM IgG to SGC in vitro. Furthermore, the frequency of SGC bound to FM IgG was significantly higher than HC IgG-treated cells. These findings correlated with pain intensity and fibromyalgia impact questionnaire scores (FIQ, questionnaire was only assessed in the Karolinska cohort). Further cluster analysis separated the FM group into severe and mild groups. Additionally, we found that serum from FM patients contains IgG that binds in greater proportion to SGC in the human DRG, measured by higher signal intensity. There were no differences in the binding intensity to neuronal cell bodies or axons between FM and HC serum samples. Finally, the previous results were confirmed using an FM serum sample with high levels of anti-SGC antibodies in 5 more human DRGs. To summarize, our report indicates that levels of anti-human SGC and anti-mouse SGC antibodies are elevated in patients with FM, which are linked to a more severe form of the disease. Patient stratification based on their profile of anti-SGC antibodies might benefit from therapies aiming to decrease circulating IgG or prevent IgG binding. Our results point to the possible involvement of anti-SGC antibodies and SGCs in the severity of FM; however, more in-depth studies are necessary to elucidate the antigen or antigens expressed in the SGC that bind to the circulating anti-SGC antibodies. In Study IV, we aimed to explore the neuroimmune signature of the FM skin. We processed 16 FM and 16 HC sex-matched skin biopsies by immunohistochemistry. Using a pan-neuronal marker, we found lower intraepidermal nerve fiber density (IENFD) in the FM compared with HC skin. Moreover, the length and volume of dermal NF200+ nerve profiles were significantly elevated, but we found no changes in the length of dermal or epidermal Gap43+ nerve profiles in the FM group. Similarly, we found no changes in the total volume of CD31+ blood vessels between FM and HC skin. Our results showed that the density of non-nerve associated S100b+, CD68+, and CD163+ cells was significantly lower in the FM skin. Furthermore, the dermal CD117+FcERI+ mast cells in the dermis of FM patients were significantly increased compared with the HCs. Additionally, we found similar densities of CD207+, CD3+, or Neutrophil elastase+ cells between FM and HC skin biopsies. mRNA analysis of FM skin showed no changes in Cd68, Cd163, Cx3cr1, or FceR1 mRNA levels between FM and HC skin. In summary, this study reveals crucial dermal and epidermal changes in FM skin, particularly regarding nerve fibers and certain immune cell populations. These findings are highly relevant as they provide deeper insights into the complex interactions between the nervous and immune systems in FM. Understanding these changes could be key to developing more effective treatments for FM, focusing on both the neuropathic and immune components of the disease

    Towards personalized medicine for metastatic urothelial cancer

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    Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model

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    Objective: A prognostic model utilizing CT radiomics, radiological, and clinical features was developed and validated in this study to predict an objective response to initial transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC).Methods: Between January 2017 and December 2022, the baseline clinical characteristics and preoperative and postoperative follow-up imaging data of 108 HCC patients who underwent the first time treatment of DEB-TACE were analyzed retrospectively. The training group (n = 86) and the validation group (n = 22) were randomly assigned in an 8:2 ratio. By logistic regression in machine learning, radiomics, and clinical-radiological models were constructed separately. Finally, the integrated model construction involved the integration of both radiomics and clinical-radiological signatures. The study compared the integrated model with radiomics and clinical-radiological models using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results: The objective response rate observed in a group of 108 HCC patients who received initial DEB-TACE treatment was found to be 51.9%. Among the three models, the integrated model exhibited superior predictive accuracy in both the training and validation groups. The training group resulted in an area under the curve (AUC) of 0.860, along with sensitivity and specificity values of 0.650 and 0.913, respectively. Based on the findings from the validation group, the AUC was estimated to be 0.927. Additionally, it was found that values of sensitivity and specificity were 0.875 and 0.833, respectively. In the validation group, the AUC of the integrated model showed a significant improvement when contrasted to the clinical-radiological model (p = 0.042). Nevertheless, no significant distinction was observed in the AUC when comparing the integrated model with the radiomics model (p = 0.734). The DCA suggested that the integrated model demonstrates advantageous clinical utility.Conclusion: The integrated model, which combines the CT radiomics signature and the clinical-radiological signature, exhibited higher predictive efficacy than either the radiomics or clinical-radiological models alone. This suggests that during the prediction of the objective responsiveness of HCC patients to the first DEB-TACE treatment, the integrated model yields superior outcomes

    Two-layer ensemble of deep learning models for medical image segmentation. [Article]

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    One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification

    Neural Architecture Search for Image Segmentation and Classification

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    Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments. This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs

    ETU-Net: efficient Transformer and convolutional U-style connected attention segmentation network applied to endoscopic image of epistaxis

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    Epistaxis is a typical presentation in the otolaryngology and emergency department. When compressive therapy fails, directive nasal cautery is necessary, which strongly recommended operating under the nasal endoscope if it is possible. Limited by the operator's clinical experience, complications such as recurrence, nasal ulcer, and septum perforation may occur due to insufficient or excessive cautery. At present, deep learning technology is widely used in the medical field because of its accurate and efficient recognition ability, but it is still blank in the research of epistaxis. In this work, we first gathered and retrieved the Nasal Bleeding dataset, which was annotated and confirmed by many clinical specialists, filling a void in this sector. Second, we created ETU-Net, a deep learning model that smartly integrated the excellent performance of attention convolution with Transformer, overcoming the traditional model's difficulties in capturing contextual feature information and insufficient sequence modeling skills in picture segmentation. On the Nasal Bleeding dataset, our proposed model outperforms all others models that we tested. The segmentation recognition index, Intersection over Union, and F1-Score were 94.57 and 97.15%. Ultimately, we summarized effective ways of combining artificial intelligence with medical treatment and tested it on multiple general datasets to prove its feasibility. The results show that our method has good domain adaptability and has a cutting-edge reference for future medical technology development

    COVID-19 Detection on Chest x-ray Images by Combining Histogram-oriented Gradient and Convolutional Neural Network Features

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    The COVID-19 coronavirus epidemic has spread rapidly worldwide after a person became infected with a severe health problem. The World Health Organization has declared the coronavirus a global threat (WHO). Early detection of COVID 19, particularly in cases with no apparent symptoms, may reduce the patients mortality rate. COVID 19 detection using machine learning techniques will aid healthcare systems around the world in recovering patients more rapidly. This disease is diagnosed using x-ray images of the chest; therefore, this study proposed a machine vision method for detecting COVID-19 in x-ray images of the chest. The histogram-oriented gradient (HOG) and convolutional neural network (CNN) features extracted from x-ray images were fused and classified using support vector machine (SVM) and softmax. The proposed feature fusion technique (99.36 percent) outperformed individual feature extraction methods such as HOG (87.34 percent) and CNN (93.64 percent)
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