The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual ...examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.
The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). ...Data include longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data including computed tomography (CT) scans and pathology slides. A subset of the repository is available at the Genomic Data Commons (GDC) and The Cancer Imaging Archive (TCIA), and the full repository is available through the Veterans Precision Oncology Data Commons (VPODC). By releasing this de-identified dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.
•Presents a large-scale repository on cancer patients with EHR, imaging, and genomic data•Describes a method for de-identification of different types of health data•Enables researchers to use data science tools to apply to the data
Accelerating the speed of innovation and discoveries in health care requires the liberation of real-world data from their silos. One of the greatest challenges in the application of artificial intelligence and machine learning to health care is the validation of new algorithms beyond where they were created. We present the Veterans Affairs Precision Oncology Data Repository (VA-PODR), a large-scale repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). VA-PODR includes longitudinal clinical, genomic, and imaging data originating from the VA's electronic health record system, the VA Central Cancer Registry, and other sources. VA-PODR enables researchers around the world to validate their algorithms and advance cancer research and health care in general. In addition, VA-PODR enhances Veterans' health care by facilitating development of algorithms that are well tuned to the Veteran population and ready for deployment inside the VA.
The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of real-world de-identified data on patients diagnosed with cancer at the Department of Veteran's Affairs. The repository contains longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data. By releasing this dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.
Veterans Health Administration (VHA) services are most frequently used by patients 65 years and older, an age group that is disproportionally affected by COVID-19. Here we describe a modular Clinical ...Trial Informatics Solution (CTIS) that was rapidly developed and deployed to support a multi-hospital embedded pragmatic clinical trial in COVID-19 patients within the VHA. Our CTIS includes tools for patient eligibility screening, informed consent tracking, treatment randomization, EHR data transformation for reporting and interfaces for patient outcome and adverse event tracking. We hope our CTIS component descriptions and practical lessons learned will serve as a useful building block for others creating their own clinical trial tools and have made application and database code publicly available.
Activity of the Ca2+-dependent protease calpain is increased in neurons after global and focal brain ischemia, and may contribute to postischemic injury cascades. Understanding the time course and ...location of calpain activity in the post-ischemic brain is essential to establishing causality and optimizing therapeutic interventions. This study examined the temporal and spatial characteristics of brain calpain activity after transient forebrain ischemia (TFI) in rats. Male Long Evans rats underwent 10 min of normothermic TFI induced by bilateral carotid occlusion with hypovolemic hypotension (MABP 30 mm Hg). Brain calpain activitywas examined between 1 and 72 h after reperfusion. Western blot analysis of regional brain homogenates demonstrated a bimodal pattern of calpain-mediated α-spectrin degradation in the hippocampus, cortex, and striatum with an initial increase at 1 h followed by a more prominent secondary increase at 36 h after reperfusion. Immunohistochemical analysis revealed that calpain activity was primarily localized to dendritic fields of selectively vulnerable neurons at one hour after reperfusion. Between 24 and 48 h after reperfusion neuronal calpain activity progressed from the dorsal to ventral striatum, medial to lateral CA1 hippocampus, and centripetally expanded from watershed foci in the cerebral cortex. This progression was associated with fragmentation of dendritic processes, calpain activation in the neuronal soma and subsequent neuronal degeneration. These observations demonstrate a clear association between calpain activation and subsequent delayed neuronal death and suggest broad therapeutic window for interventions aimed at preventing delayed intracellular Ca2+ overload and pathologic calpain activation.
Objective Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is ...generally difficult to derive patterns that achieve acceptable levels of recall while also remaining highly precise.
Materials and Methods We present a multiple sequence alignment (MSA)-based technique that automatically generates patterns, thereby leveraging language usage to determine the context of words that influence a given target. MSAs capture the commonalities among word sequences and are able to reveal areas of linguistic stability and variation. In this way, MSAs provide a systemic approach to generating lexical patterns that are generalizable, which will both increase recall levels and maintain high levels of precision.
Results The MSA-generated patterns exhibited consistent F1-, F.5-, and F2- scores compared to two baseline techniques for IE across four different tasks. Both baseline techniques performed well for some tasks and less well for others, but MSA was found to consistently perform at a high level for all four tasks.
Discussion The performance of MSA on the four extraction tasks indicates the method’s versatility. The results show that the MSA-based patterns are able to handle the extraction of individual data elements as well as relations between two concepts without the need for large amounts of manual intervention.
Conclusion We presented an MSA-based framework for generating lexical patterns that showed consistently high levels of both performance and recall over four different extraction tasks when compared to baseline methods.
Abstract Objectives Employ differential scanning calorimetry (DSC) and temperature-modulated DSC (TMDSC) to investigate thermal transformations in three mouthguard materials and provide insight into ...their previously investigated energy absorption. Methods Samples (13–21 mg) were obtained from (a) conventional ethylene vinyl acetate (EVA), (b) Pro-form™, another EVA polymer, and (c) PolyShok™, an EVA polymer containing polyurethane. Conventional DSC ( n = 5) was first performed from −80 to 150 °C at a heating rate of 10 °C/min to determine the temperature range for structural transformations. Subsequently, TMDSC ( n = 5) was performed from −20 to 150 °C at a heating rate of 1 °C/min. Onset and peak temperatures were compared using ANOVA and the Tukey–Kramer HSD test. Other samples were coated with a gold–palladium film and examined with an SEM. Results DSC and TMDSC curves were similar for both conventional EVA and Pro-form™, showing two endothermic peaks suggestive of melting processes, with crystallization after the higher-temperature peak. Evidence for crystallization and the second endothermic peak were much less prominent for PolyShok™, which had no peaks associated with the polyurethane constituent. The onset of the lower-temperature endothermic transformation is near body temperature. No glass transitions were observed in the materials. SEM examination revealed different surface morphology and possible cushioning effect for PolyShok™, compared to Pro-form™ and EVA. Significance The difference in thermal behavior for PolyShok™ is tentatively attributed to disruption of EVA crystal formation, which may contribute to its superior impact resistance. The lower-temperature endothermic peak suggests that impact testing of these materials should be performed at 37 °C.
In March 2020, Veterans Health Administration (VHA) enacted policies to expand treatment for Veterans with opioid use disorder (OUD) during COVID-19. In this study, we evaluate whether COVID-19 and ...subsequent OUD treatment policies impacted receipt of therapy/counseling and medication for OUD (MOUD).
Using VHA’s nationwide electronic health record data, we compared outcomes between a comparison cohort derived using data from prior to COVID-19 (October 2017-December 2019) and a pandemic-exposed cohort (January 2019-March 2021). Primary outcomes included receipt of therapy/counseling or any MOUD (any/none); secondary outcomes included the number of therapy/counseling sessions attended, and the average percentage of days covered (PDC) by, and months prescribed, each MOUD in a year.
Veterans were less likely to receive therapy/counseling over time, especially post-pandemic onset, and despite substantial increases in teletherapy. The likelihood of receiving buprenorphine, methadone, and naltrexone was reduced post-pandemic onset. PDC on MOUD generally decreased over time, especially methadone PDC post-pandemic onset, whereas buprenorphine PDC was less impacted during COVID-19. The number of months prescribed methadone and buprenorphine represented relative improvements compared to prior years.
We observed important disparities across Veteran demographics.
Receipt of treatment was negatively impacted during the pandemic. However, there was some evidence that coverage on methadone and buprenorphine may have improved among some veterans who received them. These medication effects are consistent with expected COVID-19 treatment disruptions, while improvements regarding access to therapy/counseling via telehealth, as well as coverage on MOUD during the pandemic, are consistent with the aims of MOUD policy exemptions.
•The likelihood of receiving MOUD decreased during COVID-19•Despite telehealth increases, therapy/counseling receipt decreased during COVID-19•Among Veterans on MOUD, coverage on buprenorphine increased during COVID-19•Among those on MOUD, months prescribed buprenorphine or methadone increased
The bonding hypothesis posits that a firm may improve its governance practices by listing in a foreign developed stock market, thereby subjecting itself to better legal and regulatory rules of the ...foreign market as well as to a superior level of scrutiny by gatekeepers in the market, which are unavailable in the home market. Previous studies have shown that the bonding effect has occurred on Chinese companies listed in Hong Kong. This article specifically questions whether the effect is accrued in the domain of gatekeeper scrutiny. First, it examines the role of four alleged gatekeepers in the stock markets of China and Hong Kong, i.e., sponsor, corporate attorney, credit rating agency, and auditor. Then, it proceeds to consider whether and to what extent Chinese companies are subject to a superior level of scrutiny by these gatekeepers on account of their being listed in Hong Kong.
The Department of Veterans Affairs (VA) has a strong track record providing high-quality, evidence-based care to cancer patients. In order to accelerate discoveries that will further improve care for ...Veterans with cancer, the VA has partnered with the Center for Translational Data Science at the University of Chicago and the Open Commons Consortium to establish a data sharing platform, the Veterans Precision Oncology Data Commons (VPODC). The VPODC makes clinical, genomic, and imaging data from the VA available to the research community at large. In this paper, we detail our motivation for data sharing, describe the VPODC, and outline our collaboration model. By transforming VA data into a national resource for research in precision oncology, the VPODC seeks to foster innovation through collaboration and resource sharing that will ultimately lead to improved care for Veterans with cancer.
The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there ...are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.
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