2,099 research outputs found

    Tumor Segmentation and Classification Using Machine Learning Approaches

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    Medical image processing has recently developed progressively in terms of methodologies and applications to increase serviceability in health care management. Modern medical image processing employs various methods to diagnose tumors due to the burgeoning demand in the related industry. This study uses the PG-DBCWMF, the HV area method, and CTSIFT extraction to identify brain tumors that have been combined with pancreatic tumors. In terms of efficiency, precision, creativity, and other factors, these strategies offer improved performance in therapeutic settings. The three techniques, PG-DBCWMF, HV region algorithm, and CTSIFT extraction, are combined in the suggested method. The PG-DBCWMF (Patch Group Decision Couple Window Median Filter) works well in the preprocessing stage and eliminates noise. The HV region technique precisely calculates the vertical and horizontal angles of the known images. CTSIFT is a feature extraction method that recognizes the area of tumor images that is impacted. The brain tumor and pancreatic tumor databases, which produce the best PNSR, MSE, and other results, were used for the experimental evaluation

    Ruthenium metallotherapeutics: a targeted approach to combatting multidrug resistant pathogens

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    The discovery of antibiotics revolutionised healthcare practice. However due to overuse, inappropriate use, widespread prophylaxis therapy and the lack of new developments, the threat of antimicrobial resistance is now a major global threat to health. By 2050, it is estimated that mortality due to antimicrobial resistant infections will exceed 10 million people per annum, superseding cancer as the leading cause of global mortality. The use of drug repurposing to identify potential therapies which combat antimicrobial resistance is one potential solution. Metals have been used as antimicrobial agents throughout the history of medicine for a broad range of applications, including the use of Silver as an antimicrobial agent which dates back to antiquity. More recently, Ruthenium metallotherapeutic complexes have been shown to exhibit highly active antimicrobial properties by targeting a range of bacterial species, and in contrast to traditional antibiotics, these compounds are thought to elicit antibacterial activity at multiple sites within the bacterial cell, which may reduce the possibility of resistance evolution. This study aimed to evaluate the antimicrobial activity of a series of Ruthenium metallotherapeutic complexes against multidrug-resistant bacterial pathogens, with a focus on use within wound care applications. Antimicrobial susceptibility assays identified two lead candidates, Hexaammineruthenium (III) chloride and [Chlorido(η6-p-cymene)(N-(4-chlorophenyl)pyridine-2-carbothioamide) ruthenium (II)] chloride which demonstrated activity against Pseudomonas aeruginosa and Staphylococcus aureus respectively with MIC values ranging between 4 μg mL-1 and 16 μg mL-1. Furthermore, Hexaammineruthenium (III) chloride demonstrated antibiofilm activity in both a time and concentration-dependent manner. Synergy studies combining lead complexes with antibiotics demonstrated the potential for use as resistance breakers. Subsequent in vitro infection modelling using scratch assays with skin cell lines, coupled with a 3D full thickness skin wound infection model was used to determine potential applied applications of Hexaammineruthenium (III) chloride for use as topical antimicrobial agent against P. aeruginosa infections. Antimicrobial mechanistic studies demonstrated that Hexaammineruthenium (III) chloride targeted the bacterial cell ultrastructure of P. aeruginosa strain PAO1 as cell perturbations were observed when treated cells were analysed by scanning electron microscopy. Furthermore, exposure of P. aeruginosa PAO1 to Hexaammineruthenium (III) chloride also resulted in a concentration dependent membrane depolarisation, which further supported the antimicrobial mechanistic role. Finally, global changes in gene expression following exposure of P. aeruginosa strain PAO1 to Hexaammineruthenium (III) chloride were explored by RNA sequencing. Genes involved in ribosome function, cofactor biosynthesis and membrane fusion were downregulated, which provided a further insight into the wider mechanisms of antibacterial activity. The research conducted in the present study indicated the potential use of Hexaammineruthenium (III) chloride (and derivatives) as a potential treatment option for chronic wounds infected with P. aeruginosa, which could be applied as either a direct treatment or used within antimicrobial wound care applications

    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

    Clinical, immunological and genetic features of histiocytic disorders

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    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Medical Image Analysis using Deep Relational Learning

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    In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778

    A deep learning based dual encoder–decoder framework for anatomical structure segmentation in chest X-ray images

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    Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network’s representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder–decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods

    Immune contexture monitoring in solid tumors focusing on Head and Neck Cancer

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    Forti evidenze dimostrano una stretta interazione tra il sistema immunitario e lo sviluppo biologico e la progressione clinica dei tumori solidi. L'effetto che il microambiente immunitario del tumore può avere sul comportamento clinico della malattia è indicato come "immunecontexture". Nonostante ciò, l'attuale gestione clinica dei pazienti affetti da cancro non tiene conto di alcuna caratteristica immunologica né per la stadiazione né per le scelte terapeutiche. Il tumore della testa e del collo (HNSCC) rappresenta il 7° tumore più comune al mondo ed è caratterizzato da una prognosi relativamente sfavorevole e dall'effetto negativo dei trattamenti sulla qualità della vita dei pazienti. Oltre alla chirurgia e alla radioterapia, sono disponibili pochi trattamenti sistemici, rappresentati principalmente dalla chemioterapia a base di platino-derivati o dal cetuximab. L'immunoterapia è una nuova strategia terapeutica ancora limitata al setting palliativo (malattia ricorrente non resecabile o metastatica). La ricerca di nuovi biomarcatori o possibili nuovi meccanismi target è molto rilevante quindi nel contesto clinico dell'HNSCC. In questa tesi ci si concentrerà sullo studio di tre possibili popolazioni immunitarie pro-tumorali studiate nell'HNSCC: i neutrofili tumore-associati (TAN), le cellule B intratumorali con fenotipo immunosoppressivo e i T-reg CD8+. Particolare attenzione è data all'applicazione di moderne tecniche biostatistiche e bioinformatiche per riassumere informazioni complesse derivate da variabili cliniche e immunologiche multiparametriche e per validare risultati derivati ​​in situ, attraverso dati di espressione genica derivati da dataset pubblici. Infine, la seconda parte della tesi prenderà in considerazione progetti di ricerca clinica rilevanti, volti a migliorare l'oncologia di precisione nell'HNSCC, sviluppando modelli predittivi di sopravvivenza, confrontando procedure oncologiche alternative, validando nuovi classificatori o testando l'uso di nuovi protocolli clinici come l'uso dell'immunonutrizione.Strong evidences demonstrate a close interplay between the immune system and the biological development and clinical progression of solid tumors. The effect that the tumor immune microenvironment can have on the clinical behavior of the disease is referred as the immuno contexture. Nevertheless, the current clinical management of patients affected by cancer does not take into account any immunological features either for the staging or for the treatment choices. Head and Neck Cancer (HNSCC) represents the 7th most common cancer worldwide and it is characterized by a relatively poor prognosis and detrimental effect of treatments on the quality of life of patients. Beyond surgery and radiotherapy, few systemic treatments are available, mainly represented by platinum-based chemotherapy or cetuximab. Immunotherapy is a new therapeutical strategy still limited to the palliative setting (recurrent not resectable or metastatic disease). The search for new biomarkers or possible new targetable mechanisms is meaningful especially in the clinical setting of HNSCC. In this thesis a focus will be given on the study of three possible pro-tumoral immune populations studied in HNSCC: the tumor associated neutrophils (TAN), intratumoral B-cells with a immunosuppressive phenotype and the CD8+ T-regs. Biostatistical and bioinformatical techniques are applied to summarize complex information derived from multiparametric clinical and immunological variables and to validate in-situ derived findings through gene expression data of public available datasets. Lastly, the second part of the thesis will take into account relevant clinical research projects, aimed at improving the precision oncology in HNSCC developing survival prediction models, comparing alternative oncological procedures, validating new classifiers or testing the use of novel clinical protocols as the use of immunnutrition

    An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

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    Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy
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