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COVID-19 can trigger a systemic inflammatory response that in some cases leads to severe lung involvement, multisystem dysfunction, and death. Dexamethasone therapy, because of its potent anti-inflammatory effects, has been proposed for the management of hospitalized patients with severe COVID-19. The subject of this article is to discuss potential strategies to tackle Strongyloides hyperinfection in hospitalized patients with COVID-19 receiving dexamethasone therapy in low- and middle-income countries. In this context, dexamethasone treatment has been found to be generally safe. However, its use in people coinfected with undetected Strongyloides stercoralis increases the risk for Strongyloides hyperinfection/dissemination a potentially fatal complication. Infection caused by S. stercoralis may remain asymptomatic or with mild symptoms in humans for several years. Early detection and specific treatment prevent a fatal evolution of this complication, but the challenge is to screen before corticosteroid therapy. In some cases, presumptive treatment may be justified. Ivermectin is the gold standard for treatment.

Accurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. Chaetocin However, the complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases.

The aim of this study is to compare the anticancer treatment trajectories and patterns of clinical events according to regimen type using the chemotherapy episodes determined by an algorithm.

We developed an algorithm to extract the regimen-level abstracted chemotherapy episodes from medication records in a conventional Observational Medical Outcomes Partnership (OMOP) common data model (CDM) database. The algorithm was validated on the Ajou University School Of Medicine (AUSOM) database by manual review of clinical notes. Using the algorithm, we extracted episodes of chemotherapy from patients in the EHR database and the claims database. We also developed an application software for visualizing the chemotherapy treatment patterns based on the treatment episodes in thr standard chemotherapy regimens for certain cases. In addition, the anticancer treatment trajectories for 8315 patients were shown, including 62 patients with COVID-19. A comparative analysis of neutropenia showed that its onset in colorectal cancer regimens tended to cluster between days 9-15, whereas it tended to cluster between days 2-8 for certain regimens for breast cancer or lung cancer.

We propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.

We propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.

Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.

This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.

We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series.

The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user s, and prioritizing early policy intervention toward the most prevalent topics.

These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators.

The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator.

We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term leide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

The COVID-19 pandemic has drastically changed the lives of countless members of the general population. Older adults are known to experience loneliness, age discrimination, and excessive worry. It is therefore reasonable to anticipate that they would experience greater negative outcomes related to the COVID-19 pandemic given their increased isolation and risk for complications than younger adults.

This study aims to synthesize the existing research on the impact of the COVID-19 pandemic, and associated isolation and protective measures, on older adults. The secondary objective is to investigate the impact of the COVID-19 pandemic, and associated isolation and protective measures, on older adults with Alzheimer disease and related dementias.

A rapid review of the published literature was conducted on October 6, 2020, through a search of 6 online databases to synthesize results from published original studies regarding the impact of the COVID-19 pandemic on older adults. The Human Development Model concepcurrent pandemic. Future studies should focus on specific consequences and needs of more at-risk older adults to ensure their inclusion, both in public health recommendations and considerations made by policy makers.Automatic crack detection is vital for efficient and economical road maintenance. link2 With the explosive development of convolutional neural networks (CNNs), recent crack detection methods are mostly based on CNNs. In this article, we propose a deeply supervised convolutional neural network for crack detection via a novel multiscale convolutional feature fusion module. Within this multiscale feature fusion module, the high-level features are introduced directly into the low-level features at different convolutional stages. Besides, deep supervision provides integrated direct supervision for convolutional feature fusion, which is helpful to improve model convergency and final performance of crack detection. Multiscale convolutional features learned at different convolution stages are fused together to robustly represent cracks, whose geometric structures are complicated and hardly captured by single-scale features. To demonstrate its superiority and generalizability, we evaluate the proposed network on three public crack data sets, respectively. Sufficient experimental results demonstrate that our method outperforms other state-of-the-art crack detection, edge detection, and image segmentation methods in terms of F1-score and mean IU.Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations of skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial recalibration method named memory attention networks (MANs) and deploy MANs using the temporal attention recalibration module (TARM) and spatiotemporal convolution module (STCM). In the TARM, a novel temporal attention mechanism is built based on residual learning to recalibrate frames of skeleton data temporally. In the STCM, the recalibrated sequence is transformed or encoded as the input of CNNs to further model the spatiotemporal information of skeleton sequence. Based on MANs, a new collaborative memory fusion module (CMFM) is proposed to further improve the efficiency, leading to the collaborative MANs (C-MANs), trained with two streams of base MANs. TARM, STCM, and CMFM form a single network seamlessly and enable the whole network to be trained in an end-to-end fashion. Comparing with the state-of-the-art methods, MANs and C-MANs improve the performance significantly and achieve the best results on six data sets for action recognition. The source code has been made publicly available at https//github.com/memory-attention-networks.Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. link3 Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named "TopGene" to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github https//github.com/NeuroSyd/breast-cancer-sub-types.