Search results for: lung segmentation

Authors: Vahid Anari, Leila Shahmohammadi

Abstract:

Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

Procedia PDF Downloads 67

Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

Abstract:

In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 139

Authors: Yun Chen, Qihang Liu, Catherine Ticehurst, Chandrama Sarker, Fazlul Karim, Dave Penton, Ashmita Sengupta

Abstract:

The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect with water. The ICESAT-2 track generates multiple VSs as it crosses the different water bodies. The difficulties are particularly pronounced in large river basins where there are many tributaries and meanders often adjacent to each other. One challenge is to split photon segments along a beam to accurately partition them to extract only the true representative water height for individual elements. As far as we can establish, there is no automated procedure to make this distinction. Earlier studies have relied on human intervention or river masks. Both approaches are unsatisfactory solutions where the number of intersections is large, and river width/extent changes over time. We describe here an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparison with river water level observations at 10 different stations on 37 different dates along the Lower Murray River, Australia. The congruence is very high and without detectable bias. In addition, we compared different outlier removal methods on the mean WSE calculation at VSs post the auto-segmentation process. All four outlier removal methods perform almost equally well with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189m) and MAE (0.130–0.142m). Overall, the auto-segmentation method developed here is an effective and efficient approach to deriving accurate mean WSE at river VSs. It provides a much better way of facilitating the application of ICESAT-2 ATL13 altimetry to rivers compared to previously reported studies. Therefore, the findings of our study will make a significant contribution towards the retrieval of hydraulic parameters, such as water surface slope along the river, water depth at cross sections, and river channel bathymetry for calculating flow velocity and discharge from remotely sensed imagery at large spatial scales.

Keywords: lidar sensor, virtual station, cross section, mean water surface elevation, beam/track segmentation

Procedia PDF Downloads 62

Authors: Mahdi Bazarganigilani

Abstract:

Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

Procedia PDF Downloads 163

Authors: Safak Isik, Ozalp Vayvay

Abstract:

Strategic partnerships with suppliers play a vital role for the long-term value-based supply chain. This strategic collaboration keeps still being one of the top priority of many business organizations in order to create more additional value; benefiting mainly from supplier’s specialization, capacity and innovative power, securing supply and better managing costs and quality. However, many organizations encounter difficulties in initiating, developing and managing those partnerships and many attempts result in failures. One of the reasons for such failure is the incompatibility of members of this partnership or in other words wrong supplier selection which emphasize the significance of the selection process since it is the beginning stage. An effective selection process of strategic suppliers is critical to the success of the partnership. Although there are several research studies to select the suppliers in literature, only a few of them is related to strategic supplier selection for long-term partnership. The purpose of this study is to propose a conceptual model for the selection of strategic partnership suppliers. A two-stage approach has been used in proposed model incorporating first segmentation and second selection. In the first stage; considering the fact that not all suppliers are strategically equal and instead of a long list of potential suppliers, Kraljic’s purchasing portfolio matrix can be used for segmentation. This supplier segmentation is the process of categorizing suppliers based on a defined set of criteria in order to identify types of suppliers and determine potential suppliers for strategic partnership. In the second stage, from a pool of potential suppliers defined at first phase, a comprehensive evaluation and selection can be performed to finally define strategic suppliers considering various tangible and intangible criteria. Since a long-term relationship with strategic suppliers is anticipated, criteria should consider both current and future status of the supplier. Based on an extensive literature review; strategical, operational and organizational criteria have been determined and elaborated. The result of the selection can also be used to determine suppliers who are not ready for a partnership but to be developed for strategic partnership. Since the model is based on multiple criteria for both stages, it provides a framework for further utilization of Multi-Criteria Decision Making (MCDM) techniques. The model may also be applied to a wide range of industries and involve managerial features in business organizations.

Keywords: Kraljic’s matrix, purchasing portfolio, strategic supplier selection, supplier collaboration, supplier partnership, supplier segmentation

Procedia PDF Downloads 239

Authors: A. Bensaid, T. Mostephaoui, R. Nedjai

Abstract:

A considerable area of Algerian lands is threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mecheria department generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of LANDSAT images (5, 7, and 8) of three scenes 197/37, 198/36 and 198/37 for the year 2020. As a second step, we prospect the use of geospatial techniques to monitor the progression of sand dunes on developed (urban) lands as well as on the formation of sandy accumulations (dune, dunes fields, nebkha, barkhane, etc.). For this purpose, this study made use of the semi-automatic processing method for the dynamic segmentation of images with very high spatial resolution (SENTINEL-2 and Google Earth). This study was able to demonstrate that urban lands under current conditions are located in sand transit zones that are mobilized by the winds from the northwest and southwest directions.

Keywords: land development, GIS, segmentation, remote sensing

Procedia PDF Downloads 155

Authors: Jessica Chorostecki, Aashka Shah, Fen Bao, Ginny Bao, Edwin George, Navid Seraji-Bozorgzad, Veronica Gorden, Christina Caon, Elliot Frohman

Abstract:

Background: Parkinson’s Disease (PD) is a neuro degenerative disorder associated with the loss of dopaminergic cells and the presence α-synuclein (AS) aggregation in of Lewy bodies. Both dopaminergic cells and AS are found in the retina. Optical coherence tomography (OCT) allows high-resolution in-vivo examination of retinal structure injury in neuro degenerative disorders including PD. Methods: We performed a cross-section OCT study in patients with definite PD and healthy controls (HC) using Spectral Domain SD-OCT platform to measure the peripapillary retinal nerve fiber layer (pRNFL) thickness and total macular volume (TMV). We performed intra-retinal segmentation with fully automated segmentation software to measure the volume of the RNFL, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL). Segmentation was performed blinded to the clinical status of the study participants. Results: 101 eyes from 52 PD patients (mean age 65.8 years) and 46 eyes from 24 HC subjects (mean age 64.1 years) were included in the study. The mean pRNFL thickness was not significantly different (96.95 μm vs 94.42 μm, p=0.07) but the TMV was significantly lower in PD compared to HC (8.33 mm3 vs 8.58 mm3 p=0.0002). Intra-retinal segmentation showed no significant difference in the RNFL volume between the PD and HC groups (0.95 mm3 vs 0.92 mm3 p=0.454). However, GCL, IPL, INL, and ONL volumes were significantly reduced in PD compared to HC. In contrast, the volume of OPL was significantly increased in PD compared to HC. Conclusions: Our finding of the enlarged OPL corresponds with mRNA expression studies showing localization of AS in the OPL across vertebrate species and autopsy studies demonstrating AS aggregation in the deeper layers of retina in PD. We propose that the enlargement of the OPL may represent a potential biomarker of AS aggregation in PD. Longitudinal studies in larger cohorts are warranted to confirm our observations that may have significant implications in disease monitoring and therapeutic development.

Keywords: Optical Coherence Tomography, biomarker, Parkinson's disease, alpha-synuclein, retina

Procedia PDF Downloads 438

Authors: Arundhuti Devi, Gitumani Devi, Krishna G. Bhattacharyya

Abstract:

The goal of this study was to assess the characteristics of the airborne dust created by opencast coal mining and its relation to population hospitalization risk for skin and lung diseases in Margherita Coalfield, Assam, India. Air samples were collected for 24 h in three 8-h periods. For the collection of particulate matter (PM10) and total suspended particulate matter (SPM) samples, respiratory dust samplers with glass microfiber filter papers were used. PM10 was analyzed for Cu, Cd, Cr, Mn, Zn, Ni, Fe and Pb with Flame Atomic Absorption Spectrophotometer (FAAS). SPM and PM10 concentrations were respectively found to be as high as 1,035 and 265.85 μg/m³ in work zone air. The concentration of metals associated with PM10 showed values higher than the permissible limits. It was observed that the average concentrations of the metals Fe, Pb, Ni, Zn, and Cu were very high during the winter month of December, those of Cd and Cr were high during the month of May and Mn was high during February. The morphology of the particles studied with scanning electron microscopy (SEM) gave significant results. Due to opencast coal mining, the air in the work zone, as well as the general ambient air, was found to be highly polluted with respect to dust. More than 8000 patient records maintained by the hospital authority were collected from three hospitals in the area. The highest percentage of people suffering from lung diseases are found in Margherita Civil Hospital (~26.77%) whereas most people suffering from skin diseases reported for treatment in the ESIC hospital (47.47%). Both PM10 and SPM were alarmingly high, and the results were in conformity with the high incidence of lung and other respiratory diseases in the study area.

Keywords: heavy metals, open cast coal mining, PM10, respiratory diseases

Procedia PDF Downloads 317

Authors: N. Rajendra Prasad

Abstract:

Resveratrol (RSV), a grape phytochemical, has drawn greater attention because of its beneficial ef-fects against cancer. However, RSV has some draw-backs such as unstabilization, poor water solubility and short biological half time, which limit the utili-zation of RSV in medicine, food and pharmaceutical industries. In this study, we have encapsulated RSV in gelatin nanoparticles (GNPs) and studied its anti-cancer efficacy in NCI-H460 lung cancer cells. SEM and DLS studies have revealed that the prepared RSV-GNPs possess spherical shape with a mean diameter of 294 nm. The successful encapsulation of RSV in GNPs has been achieved by the cross-linker glutaraldehyde probably through Schiff base reaction and hydrogen bond interaction. Spectrophotometric analysis revealed that the max-imum of 93.6% of RSV has been entrapped in GNPs. In vitro drug release kinetics indicated that there was an initial burst release followed by a slow and sustained release of RSV from GNPs. The prepared RSV-GNPs exhibited very rapid and more efficient cellular uptake than free RSV. Further, RSV-GNPs treatment showed greater antiproliferative efficacy than free RSV treatment in NCI-H460 cells. It has been found that greater ROS generation, DNA damage and apoptotic incidence in RSV-GNPs treated cells than free RSV treatment. Erythrocyte aggregation assay showed that the prepared RSV-GNPs formulation elicit no toxic response. HPLC analysis revealed that RSV-GNPs was more bioavailable and had a longer half-life than free RSV. Hence, GNPs carrier system might be a promising mode for controlled delivery and for improved therapeutic index of poorly water soluble RSV.

Keywords: resveratrol, coacervation, anticancer gelatin nanoparticles, lung cancer, controlled release

Procedia PDF Downloads 448

Authors: Syue-Wen Lin

Abstract:

Objective: This article explores the nursing experience of an 84-year-old male lung cancer patient who underwent a thoracoscopic right lower lobectomy and treatment. The patient has multiple medical histories, including hypertension and diabetes. The nursing process involved cancer treatment, postoperative pain management, as well as wound care and healing. Methods: The nursing period is from February 10 to February 17, 2024. During the nursing process, pain management strategies are implemented, including morphine drugs and non-drug methods, and music therapy, essential oil massage, and extended reception time are used to make patients feel physically and mentally comfortable so as to reduce postoperative pain and encourage active participation in rehabilitation. Strict sterile wound dressing procedures and advanced wound care techniques are used to promote wound healing and prevent infection. Due to septic shock, dialysis is used to relieve worsening symptoms. Taking into account the patient's cancer status, the nursing team provides comprehensive cancer care based on the patient's physical and psychological needs. Given the complexity of the patient's condition, including advanced cancer, palliative care is also incorporated throughout the care process to relieve discomfort and provide psychological support. Results: Through comprehensive health assessment, the nursing team fully understood the patient's condition and developed a personalized care plan based on the patient's condition. The interprofessional critical care team provides respiratory therapy and lung expansion exercises to reduce muscle loss while addressing the patient's psychological status, pain management, and vital sign stabilization needs, resulting in a comprehensive approach to care. Lung expansion exercises and the use of a high-frequency chest wall oscillation vest successfully improved sputum drainage and facilitated weaning from mechanical ventilation. In addition, helping patients stabilize their vital signs and the integration of cancer care, pain management, wound care and palliative care helps the patient be fully supported throughout the recovery process, ultimately improving his quality of life. Conclusion: Lung cancer and septic shock present significant challenges to patients, and the nursing team not only provides critical care but also addresses the unique needs of patients through comprehensive infection control, cancer care, pain management, wound care, and palliative care interventions. These measures effectively improve patients' quality of life, promote recovery, and provide compassionate palliative care for terminally ill patients. Nursing staff work closely with family members to develop a comprehensive care plan to ensure that patients receive high-quality medical care as well as psychological support and a comfortable recovery environment.

Keywords: septic shock, lung cancer, palliative care, nursing experience

Procedia PDF Downloads 24

Authors: Rijnbout-St.James Willem, Lindner Volkhard, Scholand Mary Beth, Ashton M. Tillett, Di Gennaro Michael Jude, Smith Silvia Enrica

Abstract:

Introduction: Fibrosing lung diseases are characterized by changes in the lung interstitium and are classified based on etiology: 1) environmental/exposure-related, 2) autoimmune-related, 3) sarcoidosis, 4) interstitial pneumonia, and 4) idiopathic. Among interstitial lung diseases (ILD) idiopathic forms, idiopathic pulmonary fibrosis (IPF) is the most severe. Pathogenesis of IPF is characterized by an increased presence of proinflammatory mediators, resulting in alveolar injury, where injury to alveolar epithelium precipitates an increase in collagen deposition, subsequently thickening the alveolar septum and decreasing gas exchange. Identifying biomarkers implicated in the pathogenesis of lung fibrosis is key to developing new therapies and improving the efficacy of existing therapies. The transforming growth factor-beta (TGF-B1), a mediator of tissue repair associated with WNT5A signaling, is partially responsible for fibroblast proliferation in ILD and is the target of Pirfenidone, one of the antifibrotic therapies used for patients with IPF. Canonical TGF-B signaling is mediated by the proteins SMAD 2/3, which are, in turn, indirectly regulated by Collagen Triple Helix Repeat Containing 1 (CTHRC1). In this study, we tested the following hypotheses: 1) CTHRC1 is more elevated in the ILD cohort compared to unaffected controls, and 2) CTHRC1 is differently expressed among ILD types. Material and Methods: CTHRC1 levels were measured by ELISA in 171 plasma samples from the deidentified University of Utah ILD cohort. Data represent a cohort of 131 ILD-affected participants and 40 unaffected controls. CTHRC1 samples were categorized by a pulmonologist based on affectation status and disease subtypes: IPF (n = 45), sarcoidosis (4), nonspecific interstitial pneumonia (16), hypersensitivity pneumonitis (n = 7), interstitial pneumonia (n=13), autoimmune (n = 15), other ILD - a category that includes undifferentiated ILD diagnoses (n = 31), and unaffected controls (n = 40). We conducted a single-factor ANOVA of plasma CTHRC1 levels to test whether CTHRC1 variance among affected and non-affected participants is statistically significantly different. In-silico analysis was performed with Ingenuity Pathway Analysis® to characterize the role of CTHRC1 in the pathway of lung fibrosis. Results: Statistical analyses of CTHRC1 in plasma samples indicate that the average CTHRC1 level is significantly higher in ILD-affected participants than controls, with the autoimmune ILD being higher than other ILD types, thus supporting our hypotheses. In-silico analyses show that CTHRC1 indirectly activates and phosphorylates SMAD3, which in turn cross-regulates TGF-B1. CTHRC1 also may regulate the expression and transcription of TGFB-1 via WNT5A and its regulatory relationship with CTNNB1. Conclusion: In-silico pathway analyses demonstrate that CTHRC1 may be an important biomarker in ILD. Analysis of plasma samples indicates that CTHRC1 expression is positively associated with ILD affectation, with autoimmune ILD having the highest average CTHRC1 values. While characterizing CTHRC1 levels in plasma can help to differentiate among ILD types and predict response to Pirfenidone, the extent to which plasma CTHRC1 level is a function of ILD severity or chronicity is unknown.

Keywords: interstitial lung disease, CTHRC1, idiopathic pulmonary fibrosis, pathway analyses

Procedia PDF Downloads 191

Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

Abstract:

Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

Procedia PDF Downloads 93

Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

Abstract:

Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

Procedia PDF Downloads 331

Authors: B. Kishore Kumar, Pogula Rakesh, T. Kishore Kumar

Abstract:

The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB.

Keywords: percussive sounds, spectral centroid, spectral energy, silence removal, feature extraction

Procedia PDF Downloads 594

Authors: Suparman

Abstract:

Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

Procedia PDF Downloads 373

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

Abstract:

Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 97

Authors: Williams A. Adu, Cynthia A. Danquah, Paul P. S. Ossei, Selase Ativui, Michael Ofori, James Asenso, George Owusu

Abstract:

Background Silicosis is an occupational disease of the lung that is caused by chronic exposure to silica dust. There is a higher frequency of co-existence of silicosis with tuberculosis (TB), ultimately resulting in lung fibrosis and respiratory failure. Chronic intake of synthetic drugs has resulted in undesirable side effects. Diosgenin is a steroidal saponin that has been shown to exert a therapeutic effect on lung injury. Therefore, we investigated the ability of diosgenin to reduce the susceptibility of silica-induced TB in rats. Method Silicosis was induced by intratracheal instillation of 50 mg/kg crystalline silica in Sprague Dawley rats. Different doses of diosgenin (1, 10, and 100 mg/kg), Mycobacterium smegmatis and saline were administered for 30 days. Afterwards, 5 of the rats from each group were sacrificed, and the 5 remaining rats in each group, except the control, received Mycobacterium smegmatis. Treatment of diosgenin continued until the 50th day, and the rats were sacrificed at the end of the experiment. The result was analysed using a one-way analysis of variance (ANOVA) with a Graph-pad prism Result At a half-maximal inhibition concentration of 48.27 µM, diosgenin inhibited the growth of Mycobacterium smegmatis. There was a marked decline in the levels of immune cell infiltration and cytokines production. Lactate dehydrogenase and total protein levels were significantly reduced compared to control. There was an increase in the survival rate of the treatment group compared to the control. Conclusion Diosgenin ameliorated silica-induced pulmonary tuberculosis by declining the levels of inflammatory and pro-inflammatory cytokines and, in effect, significantly reduced the susceptibility of rats to pulmonary TB.

Keywords: silicosis, tuberculosis, diosgenin, fibrosis, crystalline silica

Procedia PDF Downloads 65

Authors: Douglas W. Scott

Abstract:

The Allemande from J. S. Bach's Partita for solo flute (BWV 1013) presents many unique challenges for any flautist, especially in terms of segmentation analysis required to select breathing places in the first half. Without claiming to identify a 'correct' solution to this problem, this paper analyzes the section in terms of a set of techniques based around a statistical property commonly (if not ubiquitously) found in music, namely Zipf’s law. Specifically, the paper considers violations of this expected profile at various levels of analysis, an approach which has yielded interesting insights in previous studies. The investigation is then grounded by considering four actual solutions to the problem found in recordings made by different flautists, which opens up the possibility of expanding Zipfian analysis to include a consideration of inter-onset-intervals (IOIs). It is found that significant deviations from the expected Zipfian distributions can reveal and highlight stylistic choices made by different performers.

Keywords: inter-onset-interval, Partita for solo flute, BWV 1013, segmentation analysis, Zipf’s law

Procedia PDF Downloads 184

Authors: Nitesh Kumar, Eoin Twohig, jasparl cheema, Sadiq mawji, Yousif al najjar

Abstract:

Basal cell carcinoma (BCC) is the commonest cutaneous malignancy affecting humans. Despite this, distant spread is exceptionally rare. Metastatic BCC (mBCC) is estimated to occur in 0.0028 - 0.5%. it aim to illustrate with the aid of histological slides, a case of mBCC occurring in a fit and well 67-year-old. Initial diagnosis of desmoplastic BCC was made in 2006 from a scalp biopsy with the lesion then being excised. Re-excision of local recurrence was undertaken the following year. In 2014 the patient presented with an ipsilateral level 2a mass. Fine Needle Aspiration raised the suspicion of metastatic carcinoma. The patient had excision of two nodes from the left neck alongside pharyngeal tonsillectomy and tongue base biopsies. Histologically, the nodes closely resembled the immunophenotype of the initial scalp lesion. The patient subsequently had a modified radical neck dissection, and residual mBCC was excised from the left Sternocleidomastoid muscle. In 2023 the patient developed haematuria. On further investigation bilateral lung lesions on CT were noted with subsequent biopsy confirming mBCC. Spinal and renal lesions have also been found. Histopathology showed clear resemblance of the lung metastases to both those in the neck and the primary (scalp BCC) – with no squamous differentiation seen. The time span from primary to occurrence of lung metastasis (18 years) affirms the indolent and slow growing nature of BCC.  This case fulfils Lattes and Kessler diagnostic criteria. High risk cases are described as those with advanced local presentation, primary tumour on the Head and Neck and locally recurrent lesions.

Keywords: BCC, metastasis, rare, skin cancer

Procedia PDF Downloads 57

Authors: Isaac Martin, Abigail Lark, Sandra Morales, Eric W. Alton, Jane C. Davies

Abstract:

Objectives: The cystic fibrosis (CF) lung is a unique microbiological niche, wherein harmful bacteria persist for many years despite antibiotic therapy. Pseudomonas aeruginosa (Pa), the major culprit leading to lung decline and increased mortality, thrives in the lungs of patients with CF due to several factors that have been linked with poor antibiotic performance. Our group is investigating alternative therapies including bacteriophage cocktails with which we have previously demonstrated efficacy against planktonic organisms. In this study, we explored the effects of a 4-phage cocktail on Pa grown in two different conditions, intended to mirror the CF lung: a) alongside standard antibiotic treatment in pre-formed biofilms (structures formed by Pa-secreted exopolysaccharides which provide both physical and cell division barriers to antimicrobials and host defenses and b) in an acidic environment postulated to be present in the CF airway due both to the primary defect in bicarbonate secretion and secondary effects of inflammation. Methods: 16 Pa strains from CF patients at the Royal Brompton Hospital were selected based on sensitivity to a) ceftazidime/ tobramycin and b) the phage cocktail in a conventional plaque assay. To assess efficacy of phage in biofilms, 96 well plates with Pa (5x10⁷ CFU/ ml) were incubated in static conditions, allowing adherent bacterial colonies to form for 24 hr. Ceftazidime and tobramycin (both at 2 × MIC) were added, +/- bacteriophage (4x10⁸ PFU/mL) for a further 24 hr. Cell viability and biomass were estimated using fluorescent resazurin and crystal violet assays, respectively. To evaluate the effect of pH, strains were grown planktonically in shaking 96 well plates at pH 6.0, 6.6, 7.0 and 7.5 with tobramycin or phage, at varying concentrations. Cell viability was quantified by fluorescent resazurin assay. Results: For the biofilm assay, treatment groups were compared with untreated controls and expressed as percent reduction in cell viability and biomass. Addition of the 4-phage cocktail resulted in a 1.3-fold reduction in cell viability and 1.7-fold reduction in biomass (p < 0.001) when compared to standard antibiotic treatment alone. Notably, there was a 50 ± 15% reduction in cell viability and 60 ± 12% reduction in biomass (95% CI) for the 4 biofilms demonstrating the most resistance to antibiotic treatment. 83% of strains tested (n=6) showed decreased bacterial killing by tobramycin at acidic pHs (p < 0.01). However, 25% of strains (n=12) showed improved phage killing at acidic pHs (p < 0.05), with none showing the pattern of reduced efficacy at acidic pH demonstrated by tobramycin. Conclusion: The 4-phage anti-Pa cocktail tested against Pa performs well in pre-formed biofilms and in acidic environments; two conditions intended to mimic the CF lung. To our knowledge, these are the first data looking at the effects of subtle pH changes on phage-mediated bacterial killing in the context of Pa infection. These findings contribute to a growing body of evidence supporting the use of nebulised lytic bacteriophage as a treatment in the context of lung infection.

Keywords: biofilm, cystic fibrosis, pH, Pseudomonas aeruginosa, lytic bacteriophage

Procedia PDF Downloads 176

Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

Abstract:

Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

Procedia PDF Downloads 186

Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

Abstract:

The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

Procedia PDF Downloads 136

Authors: Emad A. Shalaby

Abstract:

Cancer is a disease that causes abnormal cell proliferation and invades nearby tissues. Lung cancer is the second most frequent cancer worldwide. Natural anti-cancer drugs have been developed with low side effects and toxicity. Citrus peels and extracts have been demonstrated to have significant pharmacological and physiological effects as a result of the high concentration of phenolic compounds found in citrus fruits, particularly peels. Tangerine peels can serve as an effective source of bioactive substances such as phenolics, flavonoids, and catechins, which have antioxidant, antibacterial, anticancer, and anti-inflammatory properties. Consequently, this work aims to determine the anticancer activity of ethanol extract of Tangerine peels against the A549 cell line and identify the phenolic compound profile (19 compounds) by using HPLC. Anticancer and antioxidant potentials of the extract were evaluated by MTT assay and TLC- TLC-bioautography sprayed with DPPH reagent, respectively. The obtained results revealed that tangerine peel extract showed significant activity against the A549 cell line with IC50 of 97.66 μg/mL. HPLC analysis proved that the highest concentration is naringenin 464.05 mg/g. More studies indicate that naringenin has significant anticancer potential on A549 cancer cells. The results showed that naringenin binds t0 EGFR protein in A549 with high binding affinity and thus may reduce lung cancer cell migration and enhance the apoptosis of cancer cells. From the obtained results it could be concluded that tangerine peel extract is an effective anti-cancer agent that may potentially serve as a natural therapeutic option for lung cancer treatment.

Keywords: tangerine peel, A549 cell line, anticancer, naringenin, HPLC analysis, naringenin, TLC bioautography

Procedia PDF Downloads 63

Authors: Kirolos Gerges Yakoub Gerges

Abstract:

Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

Procedia PDF Downloads 28

Authors: Anna Rudzińska, Katarzyna Szklener, Pola Juchaniuk, Anna Rodzajweska, Katarzyna Machulska-Ciuraj, Monika Rychlik- Grabowska, Michał łOziński, Agnieszka Kolak-Bruks, SłAwomir Mańdziuk

Abstract:

Introduction: Immune checkpoint inhibition (ICI) belongs to the modern forms of anti-cancer treatment. Due to the constant development and continuous research in the field of ICI, many aspects of the treatment are yet to be discovered. One of the less researched aspects of ICI treatment is the influence of the adverse effects on the treatment success rate. It is suspected that adverse events in the course of the ICI treatment indicate a better response rate and correlate with longer progression-free- survival. Methodology: The research was conducted with the usage of the documentation of the Department of Clinical Oncology and Chemotherapy. Data of the patients with a lung cancer diagnosis who were treated between 2019-2022 and received ICI treatment were analyzed. Results: Out of over 133 patients whose data was analyzed, the vast majority were diagnosed with non-small cell lung cancer. The majority of the patients did not experience adverse effects. Most adverse effects reported were classified as grade 1 or grade 2 according to CTCAE classification. Most adverse effects involved skin, thyroid and liver toxicity. Statistical significance was found for the adverse effect incidence and overall survival (OS) and progression-free survival (PFS) (p=0,0263) and for the time of toxicity onset and OS and PFS (p<0,001). The number of toxicity sites was statistically significant for prolonged PFS (p=0.0315). The highest OS was noted in the group presenting grade 1 and grade 2 adverse effects. Conclusions: Obtained results confirm the existence of the prolonged OS and PFS in the adverse-effects-charged patients, mostly in the group presenting mild to intermediate (Grade 1 and Grade 2) adverse effects and late toxicity onset. Simultaneously our results suggest a correlation between treatment response rate and the toxicity grade of the adverse effects and the time of the toxicity onset. Similar results were obtained in several similar research conducted - with the proven tendency of better survival in mild and moderate toxicity; meanwhile, other studies in the area suggested an advantage in patients with any toxicity regardless of the grade. The contradictory results strongly suggest the need for further research on this topic, with a focus on additional factors influencing the course of the treatment.

Keywords: adverse effects, immunotherapy, lung cancer, PD-1/PD-L1 inhibitors

Procedia PDF Downloads 92

Authors: Soheil Zorofchian Moghadamtousi, Habsah Abdul Kadir, Mohammadjavad Paydar, Elham Rouhollahi, Hamed Karimian

Abstract:

The present study was designed to evaluate the molecular mechanisms of Annona muricata leaves ethyl acetate extract (AMEAE) against lung cancer A549 cells. Cell viability analysis revealed the selective cytotoxic effect of AMEAE towards A549 cells. Treatment of A549 cells with AMEAE significantly elevated the reactive oxygen species formation, followed by attenuation of mitochondrial membrane potential via upregulation of Bax and downregulation of Bcl-2, accompanied by cytochrome c release to the cytosol. The released cytochrome c triggered the activation of caspase-9 followed by caspase-3. In addition, AMEAE-induced apoptosis was accompanied by cell cycle arrest at G1 phase. Our data showed for the first time that AMEAE inhibited the proliferation of A549 cells, leading to cell cycle arrest and programmed cell death through activation of the mitochondrial-mediated signaling pathway.

Keywords: Annona muricata, lung cancer, apoptosis, mitochondria

Procedia PDF Downloads 324

Authors: Shweh Fern Loo, Jun Yin Ong, Than Zaw Oo

Abstract:

Acute respiratory distress syndrome (ARDS) is a common serious condition of bilateral lung infiltrates that develops secondary to various underlying conditions such as diseases or injuries. ARDS with severe hypercapnia is associated with higher ICU mortality and morbidity. Venovenous Extracorporeal membrane oxygenation (VV-ECMO) support has been established to avert life-threatening hypoxemia and hypercapnic respiratory failure despite optimal conventional mechanical ventilation. However, VV-ECMO is relatively not advisable in particular groups of patients, especially in multi-organ failure, advanced age, hemorrhagic complications and irreversible central nervous system pathology. We presented a case of a 79-year-old Chinese lady without any pre-existing lung disease admitted to our hospital intensive care unit (ICU) after acute presentation of breathlessness and chest pain. After extensive workup, she was diagnosed with rapidly progressing acute interstitial pneumonia with ARDS and hypercapnia respiratory failure. The patient received lung protective strategies of mechanical ventilation and neuromuscular blockage therapy as per clinical guidelines. However, hypercapnia respiratory failure was refractory, and she was deemed not a good candidate for VV-ECMO support given her advanced age and high vasopressor requirements from shock. Alternative therapy with extracorporeal CO2 removal (ECCO2R) was considered and implemented. The patient received 12 days of ECCO2R paired with muscle paralysis, optimization of lung-protective mechanical ventilation and dialysis. Unfortunately, the patient still had refractory hypercapnic respiratory failure with dual vasopressor support despite prolonged therapy. Given failed and futile medical treatment, the family opted for withdrawal of care, a conservative approach, and comfort care, which led to her demise. The effectivity of extracorporeal CO2 removal may depend on disease burden, involvement and severity of the disease. There is insufficient data to make strong recommendations about its benefit-risk ratio for ECCO2R devices, and further studies and data would be required. Nonetheless, ECCO2R can be considered an alternative treatment for refractory hypercapnic respiratory failure patients who are unsuitable for initiating venovenous ECMO.

Keywords: extracorporeal CO2 removal (ECCO2R), acute respiratory distress syndrome (ARDS), acute interstitial pneumonia (AIP), hypercapnic respiratory failure

Procedia PDF Downloads 65

Authors: Haoqi Gao, Koichi Ogawara

Abstract:

Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

Procedia PDF Downloads 144

Authors: Marian Agbugui

Abstract:

The West African Lung fishes are fishes rich in protein and serve as an important source of food supply for man. The kind of food ingested by this group of fishes is dependent on the alimentary canal as well as the fish’s digestive processes which provide suitable modifications for maximum utilization of food taken. A study of the alimentary canal of P. annectens will expose the best information on the anatomy and histology of the fish. Samples of P. annectens were dissected to reveal the liver, pancreas and entire gut wall. Digital pictures of the mouth, jaws and the Gastrointestinal Tract (GIT) were taken. The entire gut was identified, sectioned and micro graphed. P. annectens was observed to possess a terminal mouth that opens up to 10% of its total body length, an adaptive feature to enable the fish to swallow the whole of its pry. Its dentition is made up of incisors- scissor-like teeth which also help to firmly grip, seize and tear through the skin of prey before swallowing. A short, straight and longitudinal GIT was observed in P. annectens which is known to be common feature in lungfishes, though it is thought to be a primitive characteristic similar to the lamprey. The oesophagus is short and distensible similar to other predatory and carnivorous species. Food is temporarily stored in the stomach before it is passed down into the intestine. A pyloric aperture is seen at the end of the double folded pyloric valve which leads into an intestine that makes up 75% of the whole GIT. The intestine begins at the posterior end of the pyloric aperture and winds down in six coils through the whole length intestine and ends at the cloaca. From this study it is concluded that P. annectens possess a composite GIT with organs similar to other lung fishes; it is a detritor with carnivorous abilities.

Keywords: gastrointestinal tract, incisors scissor-like teeth, intestine, mucus, Protopterus annectens, serosa

Procedia PDF Downloads 153

Authors: Pureun-Haneul Lee, Byeong-Gon Kim, Sun-Hye Lee, An-Soo Jang

Abstract:

Background: Nanoparticles may pose adverse health effects due to particulate matter inhalation. Nanoparticle exposure induces cell and tissue damage, causing local and systemic inflammatory responses. The inflammasome is a major regulator of inflammation through its activation of pro-caspase-1, which cleaves pro-interleukin-1β (IL-1β) into its mature form and may signal acute and chronic immune responses to nanoparticles. Objective: The aim of the study was to identify whether nanoparticles exaggerates inflammasome pathway leading to airway inflammation and hyperresponsiveness in an allergic mice model of asthma. Methods: Mice were treated with saline (sham), OVA-sensitized and challenged (OVA), or titanium dioxide nanoparticles. Lung interleukin 1 beta (IL-1β), interleukin 18 (IL-18), NACHT, LRR and PYD domains-containing protein 3 (NLRP3) and caspase-1 levels were assessed with Western Blot. Caspase-1 was checked by immunohistochemical staining. Reactive oxygen species were measured for the marker 8-isoprostane and carbonyl by ELISA. Results: Airway inflammation and hyperresponsiveness increased in OVA-sensitized/challenged mice and these responses were exaggerated by TiO2 nanoparticles exposure. TiO2 nanoparticles treatment increased IL-1β and IL-18 protein expression in OVA-sensitized/challenged mice. TiO2 nanoparticles augmented the expression of NLRP3 and caspase-1 leading to the formation of an active caspase-1 in the lung. Lung caspase-1 expression was increased in OVA-sensitized/challenged mice and these responses were exaggerated by TiO2 nanoparticles exposure. Reactive oxygen species was increased in OVA-sensitized/challenged mice and in OVA-sensitized/challenged plus TiO2 exposed mice. Conclusion: Our data demonstrate that inflammasome pathway activates in asthmatic lungs following nanoparticles exposure, suggesting that targeting the inflammasome may help control nanoparticles-induced airway inflammation and responsiveness.

Keywords: bronchial asthma, inflammation, inflammasome, nanoparticles

Procedia PDF Downloads 375