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.

MR.Brick: Designing A Remote Mixed-reality Educational Game System for Promoting Children's Social & Collaborative Skills

Children are one of the groups most influenced by COVID-19-related social distancing, and a lack of contact with peers can limit their opportunities to develop social and collaborative skills. However, remote socialization and collaboration as an alternative approach is still a great challenge for children. This paper presents MR.Brick, a Mixed Reality (MR) educational game system that helps children adapt to remote collaboration. A controlled experimental study involving 24 children aged six to ten was conducted to compare MR.Brick with the traditional video game by measuring their social and collaborative skills and analyzing their multi-modal playing behaviours. The results showed that MR.Brick was more conducive to children's remote collaboration experience than the traditional video game. Given the lack of training systems designed for children to collaborate remotely, this study may inspire interaction design and educational research in related fields. © 2023 ACM.

Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction

This study proposes a graph convolutional neural networks (GCN) architecture for fusion of radiological imaging and non-imaging tabular electronic health records (EHR) for the purpose of clinical event prediction. We focused on a cohort of hospitalized patients with positive RT-PCR test for COVID-19 and developed GCN based models to predict three dependent clinical events (discharge from hospital, admission into ICU, and mortality) using demographics, billing codes for procedures and diagnoses and chest X-rays. We hypothesized that the two-fold learning opportunity provided by the GCN is ideal for fusion of imaging information and tabular data as node and edge features, respectively. Our experiments indicate the validity of our hypothesis where GCN based predictive models outperform single modality and traditional fusion models. We compared the proposed models against two variations of imaging-based models, including DenseNet-121 architecture with learnable classification layers and Random Forest classifiers using disease severity score estimated by pre-trained convolutional neural network. GCN based model outperforms both imaging-only methods. We also validated our models on an external dataset where GCN showed valuable generalization capabilities. We noticed that edge-formation function can be adapted even after training the GCN model without limiting application scope of the model. Our models take advantage of this fact for generalization to external data. © 2023 IEEE.

Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes. © 2023 Association for Computational Linguistics.

A comprehensive overview on COVID-associated mucormycosis

In recent times, numerous reports of systemic fungal infections have been a major concern. The angioinvasive fungal infection, mucormycosis has surged in patients with COVID-19 due to various factors, mainly uncontrolled diabetes and inappropriate corticosteroid use. The prevalence of this acute and fatal fungal infection caused by Mucorales-related fungal species has been highest in the Indian population. COVID-associated mucormycosis (CAM) has a propensity for contiguous spread, and exhibits high morbidity as well as mortality. Unless promptly detected and treated, it is associated with a poor prognosis. A high index of suspicion, aggressive surgical debridement and use of systemic antifungal agents continue to be the standard of care for CAM. Moreover, there is an imperative need to address this public health issue by increasing public awareness and education. This article provides a comprehensive overview on the emergence of CAM during the pandemic, the current burden, pathophysiology, diagnostic interventions and management of CAM in Indian clinical practice.

Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word 'Metaverse' especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education. © 2023 IEEE.

A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

Multiliteracies in the Analysis of a Multimodal Tweet from a Former Brazilian Minister of Education

The COVID-19 pandemic abruptly led millions of teachers and students in Brazil to migrate massively, quickly, and at scale to online remote teaching. This created a strong tension between different sectors of society and rekindled (old) beliefs, values, and prejudices related to the use of new technologies in education. On the one hand are vehement defenders for adoption of these technologies at schools;on the other are those who reject them, as they consider that transitioning from in-presence to online teaching at scale is a very difficult and highly complex undertaking for education systems. In this chapter, one seeks to discuss how the perspective of multiliteracies, updated for the currently pervasively digital age, can contribute to understanding the clash between defense and resistance to new technologies at schools. To do so, first, this article will explore the main features and concepts of the theory of multiliteracies. Second, in order to highlight the close relationship between multiliteracies and education, the article analyzes an example of a multimodal tweet posted on Twitter by a former Minister of Education in Brazil, addressing the Brazilian public school setting of online remote teaching.

COVID-VTS: Fact Extraction and Verification on Short Video Platforms

We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19-focused information from both the real world and machine generation. We propose, TwtrDetective, an effective model incorporating cross-media consistency checking to detect token-level malicious tampering in different modalities, and generate explanations. Due to the scarcity of training data, we also develop an efficient and scalable approach to automatically generate misleading video posts by event manipulation or adversarial matching. We investigate several state-of-the-art models and demonstrate the superiority of TwtrDetective. © 2023 Association for Computational Linguistics.

A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

Achieving co-presence when together and apart: Hybrid engagements and multi-modal collaborative research with urban indigenous youth

This article reflects on collaborative research carried out during the COVID-19 pandemic involving indigenous youth co-investigators from different urban settings in Bolivia and a UK- and Bolivia-based research coordination team. Unlike previous studies that highlight the potential of generating a shared co-presence via virtual engagements and digital methods when face-to-face interactions seem less desirable, this article offers a more cautious account. We question the existence of a shared co-presence and, instead, posit co-presence as fragmented and not necessarily mutual, requiring careful engagement with power imbalances, distinct socio-economic and space-time positionings, and diverse priorities around knowledge generation among team members. These considerations led us to iteratively configure a hybrid research approach that combines synchronous and asynchronous virtual and face-to-face interactions with multi-modal methods. We demonstrate how this approach enabled us to generate a sense of co-presence in a context where collaborator access to a shared space-time was limited, differentiated, or displaced. [ FROM AUTHOR] Copyright of Qualitative Research is the property of Sage Publications Inc. 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 Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

Multivariate count time series segmentation with "sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub

This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal transport hub. These time series are very likely to evolve depending on various periods characterized by strikes, maintenance works, or health measures against the Covid19 pandemic. In addition, exogenous one-off factors, such as concerts and transport disruptions, can also impact mobility. Our approach flexibly detects time segments within which the very noisy count data is synthesized into regular spatio-temporal mobility profiles. At the upper level of the modeling, evolving mixing weights are designed to detect segments properly. At the lower level, segment-specific count regression models take into account correlations between series and overdispersion as well as the impact of exogenous factors. For this purpose, we set up and compare two promising strategies that can address this issue, namely the "sums and shares” and "Poisson log-normal” models. The proposed methodologies are applied to actual data collected within a multimodal transport hub in the Paris region. Ticketing logs and pedestrian counts provided by stereo cameras are considered here. Experiments are carried out to show the ability of the statistical models to highlight mobility patterns within the transport hub. One model is chosen based on its ability to detect the most continuous segments possible while fitting the count time series well. An in-depth analysis of the time segmentation, mobility patterns, and impact of exogenous factors obtained with the chosen model is finally performed. © 2023, Springer-Verlag GmbH Germany, part of Springer Nature.

Explainable AI for COVID-19 prognosis from early Chest X-ray and clinical data in the context of the COVID-CXR international hackathon

Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.

Implications of COVID-19 Pandemic on Ovarian Cancer Mortality

Ovarian cancers are the most prevalent cancers with the highest mortality among women. Most women with advanced stages require multimodal therapy, including surgery, radiotherapy, and chemotherapy. The advent of the coronavirus disease in the 2019 has affected the entire system of healthcare delivery in majority of patients suffering from cancer. During these tough times, patients suffering from ovarian cancer face mental trauma, which involves delays in diagnosis and prognosis, surgeries, chemotherapy, and radiotherapy. Instead of in-person visits, tele consultations were performed with a fear of being infected with the pandemic. This review, have prioritized the repercussions of COVID-19 on patients with ovarian cancer, Monitoring of CA125 trend in patients of ovarian cancer with COVID-19 and how COVID-19 affects the rate of mortality in cancer patients. © 2023 Bharati Vidyapeeth, New Delhi.

A case of APMPPE-like panuveitis presenting with extensive outer retinal layer impairment following COVID-19 vaccination.

BACKGROUND: Vaccination against the worldwide pandemic coronavirus disease 2019 (COVID-19) is underway; however, some cases of new onset uveitis after vaccination have been reported. We report a case of bilateral acute posterior multifocal placoid pigment epitheliopathy-like (AMPPE-like) panuveitis after COVID-19 vaccination in which the patient's pathological condition was evaluated using multimodal imaging. CASE PRESENTATION: A 31-year-old woman experienced bilateral hyperemia and blurred vision starting 6 days after her second inoculation of the COVID-19 vaccination. At her first visit, her visual acuity was decreased bilaterally, and severe bilateral anterior chamber inflammation and bilateral scattering of cream-white placoid lesions on the fundus were detected. Optical coherence tomography (OCT) showed serous retinal detachment (SRD) and choroidal thickening in both eyes (OU). Fluorescein angiography (FA) revealed hypofluorescence in the early phase and hyperfluorescence in the late phase corresponding to the placoid legions. Indocyanine green angiography (ICGA) showed sharply marginated hypofluorescent dots of various sizes throughout the mid-venous and late phases OU. The patient was diagnosed with APMPPE and was observed without any medications. Three days later, her SRD disappeared spontaneously. However, her anterior chamber inflammation continued, and oral prednisolone (PSL) was given to her. Seven days after the patient's first visit, the hyperfluorescent lesions on FA and hypofluorescent dots on ICGA partially improved; however, the patient's best corrected visual acuity (BCVA) recovered only to 0.7 OD and 0.6 OS, and the impairment of the outer retinal layer was broadly detected as hyperautofluorescent lesions on fundus autofluorescence (FAF) examination and as irregularity in or disappearance of the ellipsoid and interdigitation zones on OCT, which were quite atypical for the findings of APMPPE. Steroid pulse therapy was performed. Five days later, the hyperfluorescence on FAF had disappeared, and the outer retinal layer improved on OCT. Moreover, the patient's BCVA recovered to 1.0 OU. Twelve months after the end of treatment, the patient did not show any recurrences. CONCLUSIONS: We observed a case of APMPPE-like panuveitis after COVID-19 vaccination featuring some atypical findings for APMPPE. COVID-19 vaccination may induce not only known uveitis but also atypical uveitis, and appropriate treatment is required for each case.

Research on patient-centered design for post-stroke depression patients based on SEM and comprehensive evaluation.

Introduction: Since COVID-19, medical resources have been tight, making it inconvenient to go offline for the sequelae of diseases such as post-stroke depression (PSD) that require long-term follow-up. As a new digital therapy, VRTL began to gain popularity. Method: The research is divided into two parts: pre-test and post-test. In the pre-test, an evaluation method integrating reality-based interaction (RBI), structural equation model (SEM), analytic hierarchy process (AHP), and entropy weight method is proposed. In the post-test the patients' physiological indicators (Diastolic blood pressure, systolic blood pressure and heart rate) are measured to verify the effectiveness of RBI-SEM model using T-test method. Results: In the pre-test, using SEM, it was confirmed that Pi physical awareness, Bi body awareness, Ei environmental awareness, and Si social awareness were significantly correlated and positively affected VRTL satisfaction (p >> F 0.217; B >> F 0.130; E >> F 0.243; S >> F 0.122). The comprehensive weight ranking based on RBI-SEM considered light environment (0.665), vegetation diversity (0.667), accessible roaming space (0.550) et al. relatively of importance. And T-tset in the post-test experiment considered that the data of the two measurements before and after the VRTL experience, systolic blood pressure (p < 0.01), diastolic blood pressure (p < 0.01), and blood pressure (p < 0.01) were significantly decreased; one-way ANOVA concluded that there was no significant difference in the changes of blood pressure and heart rate among participants of different ages and genders (p > 0.01). Conclusion: This research validated the effectiveness of RBI theory for VRTL design guidelines, established an RBI-SEM based VRTL evaluation model, and the output VRTL for PSD in the older adults was confirmed to have significant therapeutic benefits. This lays the foundation for designers to decompose design tasks and integrate VRTL into traditional clinical treatment systems. Contribution from the public or patients: Four public health department employees helped to improve the research's content.

Tumor microenvironment-responsive gold nanodendrites for nanoprobe-based single-cell Raman imaging and tumor-targeted chemo-photothermal therapy

Nanodendrite particles (NDs) with densely branched structures and biomimetic architectures have exhibited great promise in tumor therapy owing to their prolonged in vivo circulation time and exceptional photothermal efficiency. Nevertheless, traditional NDs are deficient in terms of specific surface modification and targeting tumors, which restricts their potential for broader clinical applications. Here, we developed coronavirus-like gold NDs through a seed-mediated approach and using silk fibroin (SF) as a capping agent. Our results demonstrate that these NDs have a favorable drug-loading capacity (∼65.25%) and light-triggered release characteristics of doxorubicin hydrochloride (DOX). Additionally, NDs functionalized with specific probes exhibited exceptional surface-enhanced Raman scattering (SERS) characteristics, enabling high-sensitivity Raman imaging of unstained single cells. Moreover, these NDs allowed for real-time monitoring of endocytic NDs for over 24 h. Furthermore, ND@DOX conjugated with tumor-targeting peptides exhibited mild hyperthermia, minimal cytotoxicity, and effective targeting towards cancer cells in vitro, as well as responsiveness to the tumor microenvironment (TME) in vivo. These unique properties led to the highest level of synergistic tumor-killing efficiency when stimulated by a near-infrared (NIR) laser at 808 nm. Therefore, our virus-like ND functionalized with SF presents a novel type of nanocarrier that exhibits significant potential for synergistic applications in precision medicine.

Videoed storytelling in primary education EFL: exploring trainees' digital shift

This study explores the challenges and benefits primary education EFL trainees (N = 28) reported when designing and videoing a storytelling session originally intended to be conducted offline with young learners. This change of scenario was caused by the COVID-19 crisis. The data for the study were derived from the trainees' written reflections, focus group interviews, videos of instructional sessions and student-authored multimodal videos, which were explored to interpret trainees' creative processes while engaged in multimodal composing. The results indicate that trainees hold videoed storytelling to have a similar number of challenges and benefits as face-to-face storytelling. However, two of the reported advantages, enhanced creativity and self-confidence, sit at misconceptions based on trainees' limited knowledge of the pedagogical potential of multimodal resources. The findings have important educational implications in helping develop a pedagogy of videoed storytelling, while also highlighting the need for teacher training programs to specifically target the development of teachers' competence in multimodal pedagogy.