Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), visual information fidelity (VIF), Frechet Inception Distance (FID), and translation accuracy. The experimental results indicate that the synthetic images generated either by the Cycle GAN or by the Ad CycleGAN have lower MSE and RMSE, and higher scores in PSNR, UIQI, and VIF in homogenous image translation (i.e., Y → Y) compared to the heterogenous image translation process (i.e., X → Y). The synthetic images by Ad CycleGAN through the heterogeneous image translation have significantly higher FID score compared to Cycle GAN (p < 0.01). The image translation accuracy of Ad CycleGAN is higher than that of Cycle GAN when normal images are converted to COVID-19 positive images (p < 0.01). Therefore, we conclude that the Ad CycleGAN with the independent criterion can improve the accuracy of GAN image translation. The new architecture has more control on image synthesis and can help address the common class imbalance issue in machine learning methods and artificial intelligence applications with medical images.

GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task.

Automatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta.

Our work facilitates the identification of veterans who may be at risk for abdominal aortic aneurysms (AAA) based on the 2007 mandate to screen all veteran patients that meet the screening criteria. The main research objective is to automatically index three clinical conditions: pertinent negative AAA, pertinent positive AAA, and visually unacceptable image exams. We developed and evaluated a ConText-based algorithm with the GATE (General Architecture for Text Engineering) development system to automatically classify 1402 ultrasound radiology reports for AAA screening. Using the results from JAPE (Java Annotation Pattern Engine) transducer rules, we developed a feature vector to classify the radiology reports with a decision table classifier. We found that ConText performed optimally on precision and recall for pertinent negative (0.99 (0.98-0.99), 0.99 (0.99-1.00)) and pertinent positive AAA detection (0.98 (0.95-1.00), 0.97 (0.92-1.00)), and respectably for determination of non-diagnostic image studies (0.85 (0.77-0.91), 0.96 (0.91-0.99)). In addition, our algorithm can determine the AAA size measurements for further characterization of abnormality. We developed and evaluated a regular expression based algorithm using GATE for determining the three contextual conditions: pertinent negative, pertinent positive, and non-diagnostic from radiology reports obtained for evaluating the presence or absence of abdominal aortic aneurysm. ConText performed very well at identifying the contextual features. Our study also discovered contextual trigger terms to detect sub-standard ultrasound image quality. Limitations of performance included unknown dictionary terms, complex sentences, and vague findings that were difficult to classify and properly code.

Rapid Electrochemical Detection of New Delhi Metallo-beta-lactamase Genes To Enable Point-of-Care Testing of Carbapenem-Resistant Enterobacteriaceae.

The alarming rate at which antibiotic resistance is occurring in human pathogens causes a pressing need for improved diagnostic technologies aimed at rapid detection and point-of-care testing to support quick decision making regarding antibiotic therapy and patient management. Here, we report the successful development of an electrochemical biosensor to detect bla(NDM), the gene encoding the emerging New Delhi metallo-beta-lactamase, using label-free electrochemical impedance spectroscopy (EIS). The presence of this gene is of critical concern because organisms harboring bla(NDM) tend to be multiresistant, leaving very few treatment options. For the EIS assay, we used a bla(NDM)-specific PNA probe that was designed by applying a new approach that combines in silico probe design and fluorescence-based DNA microarray validation with electrochemical testing on gold screen-printed electrodes. The assay was successfully demonstrated for synthetic targets (LOD = 10 nM), PCR products (LOD = 100 pM), and direct, amplification-free detection from a bla(NDM)-harboring plasmid. The biosensor's specificity, preanalytical requirements, and performance under ambient conditions were demonstrated and successfully proved its suitability for further point-of-care test development.

Learning to rank diversified results for biomedical information retrieval from multiple features.

BACKGROUND: Different from traditional information retrieval (IR), promoting diversity in IR takes consideration of relationship between documents in order to promote novelty and reduce redundancy thus to provide diversified results to satisfy various user intents. Diversity IR in biomedical domain is especially important as biologists sometimes want diversified results pertinent to their query. METHODS: A combined learning-to-rank (LTR) framework is learned through a general ranking model (gLTR) and a diversity-biased model. The former is learned from general ranking features by a conventional learning-to-rank approach; the latter is constructed with diversity-indicating features added, which are extracted based on the retrieved passages' topics detected using Wikipedia and ranking order produced by the general learning-to-rank model; final ranking results are given by combination of both models. RESULTS: Compared with baselines BM25 and DirKL on 2006 and 2007 collections, the gLTR has 0.2292 (+16.23% and +44.1% improvement over BM25 and DirKL respectively) and 0.1873 (+15.78% and +39.0% improvement over BM25 and DirKL respectively) in terms of aspect level of mean average precision (Aspect MAP). The LTR method outperforms gLTR on 2006 and 2007 collections with 4.7% and 2.4% improvement in terms of Aspect MAP. CONCLUSIONS: The learning-to-rank method is an efficient way for biomedical information retrieval and the diversity-biased features are beneficial for promoting diversity in ranking results.

StructmRNA a BERT based model with dual level and conditional masking for mRNA representation.

In this study, we introduce StructmRNA, a new BERT-based model that was designed for the detailed analysis of mRNA sequences and structures. The success of DNABERT in understanding the intricate language of non-coding DNA with bidirectional encoder representations is extended to mRNA with StructmRNA. This new model uses a special dual-level masking technique that covers both sequence and structure, along with conditional masking. This enables StructmRNA to adeptly generate meaningful embeddings for mRNA sequences, even in the absence of explicit structural data, by capitalizing on the intricate sequence-structure correlations learned during extensive pre-training on vast datasets. Compared to well-known models like those in the Stanford OpenVaccine project, StructmRNA performs better in important tasks such as predicting RNA degradation. Thus, StructmRNA can inform better RNA-based treatments by predicting the secondary structures and biological functions of unseen mRNA sequences. The proficiency of this model is further confirmed by rigorous evaluations, revealing its unprecedented ability to generalize across various organisms and conditions, thereby marking a significant advance in the predictive analysis of mRNA for therapeutic design. With this work, we aim to set a new standard for mRNA analysis, contributing to the broader field of genomics and therapeutic development.

PAAD: Panelization algorithm for architectural designs.

Due to the competitive nature of the construction industry, the efficiency of requirement analysis is important in enhancing client satisfaction and a company's reputation. For example, determining the optimal configuration of panels (generally called panelization) that form the structure of a building is one aspect of cost estimation. However, existing methods typically rely on rule-based approaches that may lead to suboptimal material usage, particularly in complex designs featuring angled walls and openings. Such inefficiency can increase costs and environmental impact due to unnecessary material waste. To address these challenges, this research proposes a Panelization Algorithm for Architectural Designs, referred to as PAAD, which utilizes a genetic evolutionary strategy built on the 2D bin packing problem. This method is designed to balance between strict adherence to manufacturing constraints and the objective of optimizing material usage. PAAD starts with multiple potential solutions within the predefined problem space, facilitating dynamic exploration of panel configurations. It approaches structural rules as flexible constraints, making necessary corrections in post-processing, and through iterative developments, the algorithm refines panel sets to minimize material use. The methodology is validated through an analysis against an industry implementation and expert-derived solutions, highlighting PAAD's ability to surpass existing results and reduce the need for manual corrections. Additionally, to motivate future research, a synthetic data generator, the architectural drawing encodings used, and a preliminary interface are also introduced. This not only highlights the algorithm's practical applicability but also encourages its use in real-world scenarios.

A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks.

Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.

Motivations for investigating health inequities in observational epidemiology: a content analysis of 320 studies.

OBJECTIVES: To enhance equity in clinical and epidemiological research, it is crucial to understand researcher motivations for conducting equity-relevant studies. Therefore, we evaluated author motivations in a randomly selected sample of equity-relevant observational studies published during the COVID-19 pandemic. STUDY DESIGN AND SETTING: We searched MEDLINE for studies from 2020 to 2022, resulting in 16,828 references. We randomly selected 320 studies purposefully sampled across income setting (high vs low-middle-income), COVID-19 topic (vs non-COVID-19), and focus on populations experiencing inequities. Of those, 206 explicitly mentioned motivations which we analyzed thematically. We used discourse analysis to investigate the reasons behind emerging motivations. RESULTS: We identified the following motivations: (1) examining health disparities, (2) tackling social determinants to improve access, and (3) addressing knowledge gaps in health equity. Discourse analysis showed motivations stem from commitments to social justice and recognizing the importance of highlighting it in research. Other discourses included aspiring to improve health-care efficiency, wanting to understand cause-effect relationships, and seeking to contribute to an equitable evidence base. CONCLUSION: Understanding researchers' motivations for assessing health equity can aid in developing guidance that tailors to their needs. We will consider these motivations in developing and sharing equity guidance to better meet researchers' needs.

Reporting of equity in observational epidemiology: A methodological review.

Background: Observational studies can inform how we understand and address persisting health inequities through the collection, reporting and analysis of health equity factors. However, the extent to which the analysis and reporting of equity-relevant aspects in observational research are generally unknown. Thus, we aimed to systematically evaluate how equity-relevant observational studies reported equity considerations in the study design and analyses. Methods: We searched MEDLINE for health equity-relevant observational studies from January 2020 to March 2022, resulting in 16 828 articles. We randomly selected 320 studies, ensuring a balance in focus on populations experiencing inequities, country income settings, and coronavirus disease 2019 (COVID-19) topic. We extracted information on study design and analysis methods. Results: The bulk of the studies were conducted in North America (n = 95, 30%), followed by Europe and Central Asia (n = 55, 17%). Half of the studies (n = 171, 53%) addressed general health and well-being, while 49 (15%) focused on mental health conditions. Two-thirds of the studies (n = 220, 69%) were cross-sectional. Eight (3%) engaged with populations experiencing inequities, while 22 (29%) adapted recruitment methods to reach these populations. Further, 67 studies (21%) examined interaction effects primarily related to race or ethnicity (48%). Two-thirds of the studies (72%) adjusted for characteristics associated with inequities, and 18 studies (6%) used flow diagrams to depict how populations experiencing inequities progressed throughout the studies. Conclusions: Despite over 80% of the equity-focused observational studies providing a rationale for a focus on health equity, reporting of study design features relevant to health equity ranged from 0-95%, with over half of the items reported by less than one-quarter of studies. This methodological study is a baseline assessment to inform the development of an equity-focussed reporting guideline for observational studies as an extension of the well-known Strengthening Reporting of Observational Studies in Epidemiology (STROBE) guideline.

BPLT+: a Bayesian-based personalized recommendation model for health care.

In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation (BPLT(+)) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate BPLT(+) model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed BPLT(+) model.

Term dependency extraction using rule-based Bayesian Network for medical image retrieval.

Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in words, hence negatively affect the retrieval performance. One of the solutions offered in the literature is to form a Bayesian Network thesaurus taking advantage of some medical terms extracted from the image datasets. Despite the interestingness of this solution, it is not efficient as it is highly related to the co-occurrence measure, the layer arrangement and the arc directions. A significant drawback of the co-occurrence measure is the generation of a lot of uninteresting co-occurring terms. Several studies applied the association rules mining and its measures to discover the correlation between the terms. In this paper, we propose a new efficient association Rule Based Bayesian Network (R2BN) model for TBMIR using updated medically-dependent features (MDF) based on Unified Medical Language System (UMLS). The MDF are a set of medical terms that refers to the imaging modalities, the image color, the searched object dimension, etc. The proposed model presents the association rules mined from MDF in the form of Bayesian Network model. Then, it exploits the association rule measures (support, confidence, and lift) to prune the Bayesian Network model for efficient computation. The proposed R2BN model is combined with a literature probabilistic model to predict the relevance of an image to a given query. Experiments are carried out with ImageCLEF medical retrieval task collections from 2009 to 2013. Results show that our proposed model enhances significantly the image retrieval accuracy compared to the state-of-the-art retrieval models.

Digital interventions to reduce social isolation and loneliness in older adults: An evidence and gap map.

Background: Social isolation and loneliness are more common in older adults and are associated with a serious impact on their well-being, mental health, physical health, and longevity. They are a public health concern highlighted by the COVID-19 pandemic restrictions, hence the need for digital technology tools to enable remotely delivered interventions to alleviate the impact of social isolation and loneliness during the COVID-19 restrictions. Objectives: To map available evidence on the effects of digital interventions to mitigate social isolation and/or loneliness in older adults in all settings except hospital settings. Search Methods: We searched the following databases from inception to May 16, 2021, with no language restrictions. Ovid MEDLINE, Embase, APA PsycInfo via Ovid, CINAHL via EBSCO, Web of Science via Clarivate, ProQuest (all databases), International Bibliography of the Social Sciences (IBSS) via ProQuest, EBSCO (all databases except CINAHL), Global Index Medicus, and Epistemonikos. Selection Criteria: Titles and abstracts and full text of potentially eligible articles were independently screened in duplicate following the eligibility criteria. Data Collection and Analysis: We developed and pilot tested a data extraction code set in Eppi-Reviewer and data were individually extracted and coded based on an intervention-outcome framework which was also used to define the dimensions of the evidence and gap map. Main Results: We included 200 articles (103 primary studies and 97 systematic reviews) that assessed the effects of digital interventions to reduce social isolation and/or loneliness in older adults. Most of the systematic reviews (72%) were classified as critically low quality, only 2% as high quality and 25% were published since the COVID-19 pandemic. The evidence is unevenly distributed with clusters predominantly in high-income countries and none in low-income countries. The most common interventions identified are digital interventions to enhance social interactions with family and friends and the community via videoconferencing and telephone calls. Digital interventions to enhance social support, particularly socially assistive robots, and virtual pets were also common. Most interventions focused on reducing loneliness and depression and improving quality of life of older adults. Major gaps were identified in community level outcomes and process indicators. No included studies or reviews assessed affordability or digital divide although the value of accessibility and barriers caused by digital divide were discussed in three primary studies and three reviews. Adverse effects were reported in only two studies and six reviews. No study or review included participants from the LGBTQIA2S+ community and only one study restricted participants to 80 years and older. Very few described how at-risk populations were recruited or conducted any equity analysis to assess differences in effects for populations experiencing inequities across PROGRESS-Plus categories. Authors' Conclusions: The restrictions placed on people during the pandemic have shone a spotlight onto social isolation and loneliness, particularly for older adults. This evidence and gap map shows available evidence on the effectiveness of digital interventions for reducing social isolation or loneliness in older adults. Although the evidence is relatively large and recent, it is unevenly distributed and there is need for more high-quality research. This map can guide researchers and funders to consider areas of major gaps as priorities for further research.

A scoping review establishes need for consensus guidance on reporting health equity in observational studies.

OBJECTIVES: To evaluate the support from the available guidance on reporting of health equity in research for our candidate items and to identify additional items for the Strengthening Reporting of Observational studies in Epidemiology-Equity extension. STUDY DESIGN AND SETTING: We conducted a scoping review by searching Embase, MEDLINE, CINAHL, Cochrane Methodology Register, LILACS, and Caribbean Center on Health Sciences Information up to January 2022. We also searched reference lists and gray literature for additional resources. We included guidance and assessments (hereafter termed "resources") related to conduct and/or reporting for any type of health research with or about people experiencing health inequity. RESULTS: We included 34 resources, which supported one or more candidate items or contributed to new items about health equity reporting in observational research. Each candidate item was supported by a median of six (range: 1-15) resources. In addition, 12 resources suggested 13 new items, such as "report the background of investigators". CONCLUSION: Existing resources for reporting health equity in observational studies aligned with our interim checklist of candidate items. We also identified additional items that will be considered in the development of a consensus-based and evidence-based guideline for reporting health equity in observational studies.

Modeling and mining term association for improving biomedical information retrieval performance.

BACKGROUND: The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. RESULTS: We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. CONCLUSIONS: First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

BACKGROUND: In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. RESULTS: In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. CONCLUSIONS: The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.

CycleGAN with Dynamic Criterion for Malaria Blood Cell Image Synthetization.

We present a cycle-consistent adversarial network (Cycle GAN) with dynamic criterion to synthesize blood cells parasitized by malaria plasmodia. The result shows 100% of the synthetic images are correctly classified by the pretrained classifier compared to 99.61% of the real images, 76.6% generated by the Cycle GAN without the dynamic criterion. The average score of Frechet Inception Distance (FID) of the generated images by the enhanced Cycle GAN is 0.0043 (Std=0.0005), which is significantly lower than the FID score of the variational autoencoder (VAE) model (0.0085 (Std=0.0007)). We conclude that the new Cycle GAN model with dynamic criterion can generate high quality malaria infected blood cell images with good diversity. The new method provides new augmentation technique to enhance the image diversity where the acquisition of well-annotated images is highly restricted, and to improve the robustness of medical image automatic processing by deep neural networks.

Incidence of Venous Thromboembolism in Hospitalized COVID-19 Patients Receiving Thromboprophylaxis.

Background: The purpose of this study was to investigate the association between anticoagulant dosing intensity in coronavirus disease 2019 (COVID-19) infected patients and its outcomes on venous thromboembolism (VTE) and all-cause mortality. Methods: This is a retrospective observational study that examined different anticoagulation regimens among COVID-19 patients for prophylaxis of VTE. Primary outcomes of the study were VTE incidence and all-cause mortality for patients receiving prophylaxis-intensity (PPX) and therapeutic-intensity (TX) anticoagulation. Secondary outcomes were incidence of hemorrhagic events and hospital length of stay. Patients were matched (1:1) based on age and Charlson comorbidity score. Sub-group analyses evaluated outcomes within critically ill patients, between specific anticoagulant agents and comorbid conditions. Results: The primary outcome of VTE occurred in six patients within the prophylactic dose group and eight patients in the therapeutic-intensity dose group (risk ratio (RR): 2.02 (95% confidence interval (CI): 0.7 - 5.2); P = 0.2). Bleeding occurred in 15 (11%) patients in the prophylactic group and 27 (19%) patients in the therapeutic group (RR: 0.5 (95% CI: 0.3 - 1.0); P < 0.049). Hospital length of stay was shorter by 4 days in those treated with prophylactic-intensity anticoagulation (P = 0.003). Intensive care unit admission and ventilation were negatively correlated with mortality in a multivariate analysis. Conclusions: Among hospitalized COVID-19 patients, the use of therapeutic-intensity anticoagulation did not show any benefits in reducing the occurrence of VTE. An increase in mortality and in the incidence of hemorrhagic events was statistically significant in the therapeutic-intensity group. Future prospective studies are warranted to evaluate anticoagulation therapy in COVID-19 infected patients.

MR angiographic findings of aberrant right subclavian artery with agenesis of the left common carotid artery and anomalous origin of the vertebral arteries.

INTRODUCTION: Congenital aortic arch anomalies and variants have been extensively characterized in the medical literature. Proper identification of these anomalies is important when surgical or percutaneous interventions are indicated. CASE PRESENTATION: We present a case of a 48-year old male who presented to the emergency department with altered mental status. Magnetic resonance angiography (MRA) findings revealed an aberrant right subclavian artery (ARSA), early bifurcation of the right common carotid artery (CCA) with anomalous origin of the right vertebral artery (VA) from the right common carotid artery bifurcation, anomalous left vertebral artery originating from the aortic arch, and absent left common carotid artery with independent origins of the left external carotid artery (ECA) and internal carotid artery (ICA). No other abnormalities were identified, and the patient demonstrated no symptoms attributable to his vascular anomalies. CONCLUSION: To our knowledge, this unique combination of anomalies has never been reported in the literature. With an understanding of embryological pathways, even exceedingly rare anomalies like this one can be explained.

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study.

BACKGROUND: Shock wave lithotripsy (SWL), ureteroscopy, and percutaneous nephrolithotomy are established treatments for renal stones. Historically, SWL has been a predominant and commonly used procedure for treating upper tract renal stones smaller than 20 mm in diameter due to its noninvasive nature. However, the reported failure rate of SWL after one treatment session ranges from 30% to 89%. The failure rate can be reduced by identifying candidates likely to benefit from SWL and manage patients who are likely to fail SWL with other treatment modalities. This would enhance and optimize treatment results for SWL candidates. OBJECTIVE: We proposed to develop a machine learning model that can predict SWL outcomes to assist practitioners in the decision-making process when considering patients for stone treatment. METHODS: A data set including 58,349 SWL procedures performed during 31,569 patient visits for SWL to a single hospital between 1990 and 2016 was used to construct and validate the predictive model. The AdaBoost algorithm was applied to a data set with 17 predictive attributes related to patient demographics and stone characteristics, with success or failure as an outcome. The AdaBoost algorithm was also applied to a training data set. The generated model's performance was compared to that of 5 other machine learning algorithms, namely C4.5 decision tree, naïve Bayes, Bayesian network, K-nearest neighbors, and multilayer perceptron. RESULTS: The developed model was validated with a testing data set and performed significantly better than the models generated by the other 5 predictive algorithms. The sensitivity and specificity of the model were 0.875 and 0.653, respectively, while its positive predictive value was 0.7159 and negative predictive value was 0.839. The C-statistics of the receiver operating characteristic (ROC) analysis was 0.843, which reflects an excellent test. CONCLUSIONS:  We have developed a rigorous machine learning model to assist physicians and decision-makers to choose patients with renal stones who are most likely to have successful SWL treatment based on their demographics and stone characteristics. The proposed machine learning model can assist physicians and decision-makers in planning for SWL treatment and allow for more effective use of limited health care resources and improve patient prognoses.