A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

Automatic Detection of Lung Ultrasound Artifacts using a Deep Neural Networks approach

The COVID-19 pandemic has challenged many of the healthcare systems around the world. Many patients who have been hospitalized due to this disease develop lung damage. In low and middle-income countries, people living in rural and remote areas have very limited access to adequate health care. Ultrasound is a safe, portable and accessible alternative;however, it has limitations such as being operator-dependent and requiring a trained professional. The use of lung ultrasound volume sweep imaging is a potential solution for this lack of physicians. In order to support this protocol, image processing together with machine learning is a potential methodology for an automatic lung damage screening system. In this paper we present an automatic detection of lung ultrasound artifacts using a Deep Neural Network, identifying clinical relevant artifacts such as pleural and A-lines contained in the ultrasound examination taken as part of the clinical screening in patients with suspected lung damage. The model achieved encouraging preliminary results such as sensitivity of 94%, specificity of 81%, and accuracy of 89% to identify the presence of A-lines. Finally, the present study could result in an alternative solution for an operator-independent lung damage screening in rural areas, leading to the integration of AI-based technology as a complementary tool for healthcare professionals. © 2023 SPIE.

Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis

Computer-aided diagnosis of COVID-19 from lung medical images has received increasing attention in previous clinical practice and research. However, developing such automatic model is usually challenging due to the requirement of a large amount of data and sufficient computer power. With only 317 training images, this paper presents a Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for data synthetising. In order to take into account, the feature extraction ability and lightness of the model for lung CT images, the CACGAN network is mainly constructed by convolution blocks. During the training process, each iteration will update the discriminator's network parameters twice and the generator's network parameters once. For the evaluation of CACGAN. This paper organized multiple comparison between each pair from CACGAN synthetic data, classic augmented data, and original data. In this paper, 7 classifiers are built, ranging from simple to complex, and are trained for the three sets of data respectively. To control the variable, the three sets of data use the exact same classifier structure and the exact same validation dataset. The result shows the CACGAN successfully learned how to synthesize new lung CT images with specific labels. © 2023 SPIE.

Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.

Linking CT and SPECT based analysis for quantitative follow-up of vascular perfusion defects in COVID-19

COVID-19 still affects a large population worldwide with possible post-traumatic sequelae requiring long-term patient follow-up for the most severe cases. The lung is the primary target of severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) infection. In particular, the virus affects the entire pulmonary vascular tree from large vessels to capillaries probably leading to an abnormal vascular remodeling. In this study we investigated two modalities for assessing this remodeling, SPECT perfusion scintigraphy and computed tomography, the latter enabling the computation of vascular remodeling patterns. We analyzed on a cohort of 30 patients the relationship between vascular remodeling and perfusion defects in the peripheral lung area, which is a predominant focus of the COVID-19 infectious patterns. We found that such relationship exists, demonstrated by moderate significant correlations between SPECT and CT measures. In addition, a vascular remodeling index derived from the z-score normalized peripheral CT images showed a moderate significant correlation with the diffusing capacity of the lung for carbon monoxide (DLCO) measures. Altogether these results point CT scan as a good tool for a standardized, quantitative, and easy-to-use routine characterization and follow-up of COVID-19-induced vascular remodeling. An extensive validation of these results will be carried out in the near future on a larger cohort. © 2023 SPIE.

LANGERHANS CELL HISTIOCYTOSIS IN A CHILD WITH MULTISYSTEMIC LESIONS ASSOCIATED WITH INFECTED POLIOMYELITIS AND SARS-COV-2, FEATURES OF DIAGNOSTICS. CLINICAL CASE.

Langerhans cell histiocytosis (LCH) is an abnormal clonal proliferation of Langerhans cells. The prognosis varies depending on the form of the disease and organ damage. Any organs and systems can be involved in the pathological process in various combinations. A poor response to standard therapy and an unfavorable prognosis are characteristic of patients with a multisystem form of LCH and involvement of organs at risk. Skin lesions are a classic sign of LCH. Purpose - to describe the complexity and duration of diagnosis of LCH with multisystem damage in a boy aged 2 years and 2 months, infected with poliomyelitis and coronavirus. Clinical case. The first clinical manifestations of LCH in the child debuted with an eczematous-seborrheic rash on the scalp with spread to the limbs and trunk. The child was treated for toxicoderma, hemorrhagic vasculitis at the place of residence for 6 months. The boy lost 1.5 kg of body weight in 1 month. At the time of hospitalization, seborrheic-eczematous rashes on the skin with a hemorrhagic component, trophic-inflammatory changes in the nails of the hands, signs of protein-energy deficiency, stomatitis, gingivitis, hepatosplenomegaly, polyserositis, diabetes insipidus, osteolytic foci of the frontal bones were found. Results of the tests: anemia, thrombocytopenia, hypoproteinemia and hypoalbuminemia, coagulation disorders. The patient had the onset of lower flaccid paraparesis, muscle hypotonia. The boy was diagnosed with a number of infectious complications, including poliomyelitis (a derivative of vaccine poliovirus type 2), COVID-19. The child received LCH-III cytostatic therapy with a positive effect. The research was carried out in accordance with the principles of the Helsinki Declaration. The informed consent of the patient was obtained for conducting the studies.Copyright © 2023 Institute of Physics of the Russian Academy of Sciences. All rights reserved.

A Simulation of a Design and Implementation of a New Low- Cost Prototype Portable Smart Ventilator

The outbreak of the Covid-19 virus prompted many engineers and researchers around the world to seek to develop mechanical ventilation devices and make them easy to use and affordable. This paper presents a simulation model for a group of medical sensors and gives very accurate results. This model contributes to the development and improvement of the artificial breathing system by comparing the results between the simulation model and the realistic response of the human lung. © 2023 IEEE.

Development of an acquisition system to monitoring asynchronous tele-ultrasound for lung patients

Lung ultrasound imaging allows the detection and evaluation of the lung damage generated by COVID-19. However, several infrastructure and logistical limitations prevent them from being carried out in isolated and remote areas. In this work, a system for the acquisition of medical images through asynchronous tele-ultrasounds was developed. The system is based on a graphical user interface, which records the three video cameras, the ultrasound image and the accelerometer simultaneously. The interface was developed according to the Volume Sweep Imaging acquisition protocol. The translational and rotational movement of the transducer are tracked and monitored by the accelerometer and the position of the transducer is obtained from the images acquired by the three video cameras. The results show a correct functioning of the system overall, being viable to be implemented for data acquisition and calculation of error, although in order to validate the error calculation there is still more research to be done. © 2023 SPIE.

An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Advancements in organs-on-chips technology for viral disease and anti-viral research

Disease models that can accurately recapitulate human pathophysiology during infection and clinical response to antiviral therapeutics are still lacking, which represents a major barrier in drug development. The emergence of human Organs-on-a-Chip that integrated microfluidics with three-dimensional (3D) cell culture, may become the potential solution for this urgent need. Human Organs-on-a-Chip aims to recapitulate human pathophysiology by incorporating tissue-relevant cell types and their microenvironment, such as dynamic fluid flow, mechanical cues, tissue–tissue interfaces, and immune cells to increase the predictive validity of in vitro experimental models. Human Organs-on-a-Chip has a broad range of potential applications in basic biomedical research, preclinical drug development, and personalized medicine. This review focuses on its use in the fields of virology and infectious diseases. We reviewed various types of human Organs-on-a-Chip-based viral infection models and their application in studying viral life cycle, pathogenesis, virus-host interaction, and drug responses to virus- and host-targeted therapies. We conclude by proposing challenges and future research avenues for leveraging this promising technology to prepare for future pandemics.

Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-The-Art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-Trained models;second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease. © 2023 IEEE.

Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

An Automated CHNN Model for the Classification and Detection of Lung Diseases using Transfer Learning

The COVID-19 pandemic began in December2019 and caused a global crisis. The WHO declared it a pandemic on March 11, 2020. Since October 10, 2020, COVID-19 has affected 200+ countries, causing over 37 million confirmed cases and 1 million deaths. RT-PCR is the usual method for detecting it, but it has drawbacks. Individuals who exhibit symptoms of COVID-19 but receive negative results from RT-PCR tests may be diagnosed with the disease using chest X-rays and CT scans, as these imaging techniques are capable of detecting lung abnormalities that are commonly associated with COVID-19, including consolidation and ground-glass opacities. The detection of COVID-19 systems faces numerous challenges, including false negatives, limited testing capacity, a scarcity of imaging equipment, and a shortage of data. With the increasing number of cases, there is a pressing need for a quicker, more cost-effective screening method. Chest X-ray scans can serve as a supplementary or confirming approach as they are fast and readily available. An Automated Hybrid Convolutional Neural network-Hopfield Neural Network (CHNN) is proposed in this study by extracting the features using VGG-19 for the classification and detection of lung diseases. In this work, both two-fold and multi-class classifications have been done with 99% and 97% accuracy respectively. © 2023 IEEE.

Application of Convolutional Neural Network techniques to the classification of lung illnesses

The heterotypic perspective of cancer depicts solid tumors as ecosystems composed of aberrant epithelium tumor cells and a multitude of cell types together referred to as stromal cells. Macrophages, which are innate immune cells, are overrepresented in certain environments. Tumor-associated macrophages (TAMs) are macrophages found in the tumor microenvironment;they are derived from the blood's monocytes and are essential for tumor progression. TAMs acquiring tumorigenic qualities is dependent on a complicated interaction between TAMs and tumor cells. Using co-culture studies, we showed that tumor-derived secretory signals promote Tams' tumor-promoting characteristics, shaping up Tams' features in ways that are advantageous to the tumor. When model human monocytes (THP-1) were co-cultured with A549 cells, the A549 cells exhibited increased proliferation, migration, and invasiveness due to the secretion of tumor-promoting cytokines from the THP-1 cells. We showed that EDA-containing Fibronectin secreted by A549 cells reliably mediates the pro-inflammatory response of THP-1 monocytes in a paracrine manner. Ablation of such responses by the treatment of THP-1 cells with TLR-4 blocking antibody implicated Fibronectin-TLR4 axis in tumor-associated inflammation and suggests a paradigm wherein lung carcinoma cell derived EDA-containing Fibronectin drives a pro-inflammatory and pro-metastatic tumor microenvironment. Interestingly, autocrine proliferation, migration, and invasion were all boosted by EDA-containing Fibronectin secreted by A549 cells. Lastly, we demonstrated that the EDA in Fibronectin activates the epithelial-mesenchymal transition pathway in A549 cells, hence granting these cells the ability to metastasize. © 2023 IEEE.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

Covid Analysis using Recurrent Neural Network

The examination of medical images has benefited greatly from the use of artificial intelligence. In contrast to deep learning systems, which do feature extraction automatically and without human interaction, traditional computer vision methods rely on manually produced features that are particular to a certain domain. Having access to medical information for automated analysis is another major factor driving the trend towards deep learning. Chest x-ray pictures are processed in order to segment the lungs and identify diseases in this thesis. Due to its cheap cost, ease of capture, and non-invasive nature, chest x-ray is the most often used medical imaging technology. However, automatic diagnosis in chest x-rays is difficult due to (1) the presence of the rib-cage and clavicle bones, which can obscure abnormalities that are located beneath them, and (2) the fuzzy intensity transitions near the lung and heart, dense abnormalities, rib-cages, and clavicle bones, which make the identification of lung contours subtle. In x-ray image processing, the Convolutional Neural Network (CNN) is the most often used deep learning architecture. Because to the enormous number of parameters in deep CNN architectures, intensive computing resources are required to train these models. Additionally, chest x-ray datasets are often rather tiny, and there is always the risk of overfitting when developing a model. In this dissertation, we propose five convolutional neural networks (CNNs) to identify illness and segment the lungs in chest x-rays. New Line, New Line In the first research paper, an adaptive lightweight convolutional neural network (ALCNN) is created to detect pneumothorax with few parameters. The model readjusts the feature calibration channel-wise using the convolutional layer and attention mechanism. The suggested model outperformed state-of-the-art deep models trained using three different transfer learning methods. One notable aspect of the suggested model is that it requires ten times less parameters than the best deep models currently available. The second paper suggests the FocusCovid methodology for identifying COVID-19. © 2023 IEEE.

Lung-on-a-Chip Models of the Lung Parenchyma.

Since the publication of the first lung-on-a-chip in 2010, research has made tremendous progress in mimicking the cellular environment of healthy and diseased alveoli. As the first lung-on-a-chip products have recently reached the market, innovative solutions to even better mimic the alveolar barrier are paving the way for the next generation lung-on-chips. The original polymeric membranes made of PDMS are being replaced by hydrogel membranes made of proteins from the lung extracellular matrix, whose chemical and physical properties exceed those of the original membranes. Other aspects of the alveolar environment are replicated, such as the size of the alveoli, their three-dimensional structure, and their arrangement. By tuning the properties of this environment, the phenotype of alveolar cells can be tuned, and the functions of the air-blood barrier can be reproduced, allowing complex biological processes to be mimicked. Lung-on-a-chip technologies also provide the possibility of obtaining biological information that was not possible with conventional in vitro systems. Pulmonary edema leaking through a damaged alveolar barrier and barrier stiffening due to excessive accumulation of extracellular matrix proteins can now be reproduced. Provided that the challenges of this young technology are overcome, there is no doubt that many application areas will benefit greatly.

Examining The Clinical Symptoms Of The Oral Cavity And Its Severity In Patients With COVID-19 Referred To Imam Reza Hospital In 2019

Introduction: COVID-19 is a viral infection caused by the acute respiratory syndrome virus (SARS-CoV-2), which clinically affects several human body organs. The present study aims to investigate the clinical symptoms of the oral cavity and its severity in patients with COVID-19 referred to Imam Reza Hospital in Tabriz in 1401. Materials and methods: In this descriptive cross-sectional study, 89 patients with COVID-19 positive PCR tests and 89 healthy individuals who met the conditions for inclusion were examined. Patient demographic information, including age, gender, history of underlying diseases, and use of toothbrushes, was recorded. Then the patients were examined by a dentist, and various oral lesions were recorded. Saliva flow rate, lesion intensity, and gingival inflammation rate (based on BOP) were recorded. The results were analyzed by SPSS24 statistical analysis software and the Marie Chi-square test. Results: Oral lesions, including painful and non-painful ulcers in the mucosa, small blisters in the oral mucosa, the presence of petechiae, and the amount of taste disorder in patients with COVID-19 were significantly higher than in healthy individuals. The severity of gingivitis in patients with COVID-19 was significantly higher than in healthy people. The difference in the frequency of oral lesions was observed in both the sick and healthy groups in both men and women and different age groups. Conclusion: COVID-19 can cause aggravation of oral lesions. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Garden of Marvels: Tales of Wonder from Early Medieval China by Robert Ford Campany (review)

The category of zhiguai (accounts of the strange) texts is diverse, encompassing a wide variety of anecdotes, historical records, memoirs, letters, temple inscriptions, and biographies, among others, that recount encounters with sacred, ordinary, and apotropaic objects, shapeshifting animals, ghosts, demons, local gods, and numinous beings such as Daoist transcendents or the Buddha, Buddhist practitioners, deities and supernatural creatures;visits to otherworldly places such as the court of judgment in the afterlife, hidden villages of immortals or enlightened beings à la James Hilton's Shangri-la or the Tibetan mythical kingdom of Shambhala, or even heaven or hell;and unaccountable phenomena such as bizarre dreams, premonitions, and miraculous occurrences, including surviving entombment and the return from death (xxviii). Mordicai Gerstein's children's book Carolinda Clatter (2005), with its description of a giant's sleeping body becoming a mountain with forests, caves, and waterfalls, mirrors the cosmogonic myth of Pangu, whose body parts become the world in item 85 (58 and 59). The eerie feel of the scene in C. S. Lewis's The Magician's Nephew (1955), where Digory Kirke enters the Garden to pluck an apple from the Tree of Knowledge to protect Narnia, is highly reminiscent of item 47 (35), where uninvited intruders eat their fill of otherworldly fruit from a remote orchard but are admonished by an unseen voice in midair to drop the fruit they intended to take with them.