Search

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Infections by SARS CoV2 - COVID-19 have become in a short time a worldwide health emergency. Due to cardiovascular implications of COVID-19 and to very frequent previous cardiovascular disorders of COVID-19 patients, it is presently crucial that Cardiologists are fully aware of COVID-19 related epidemiological, pathophysiological and therapeutic problems, in order to manage at best the present emergency by appropriate protocols developed on the basis of the competences acquired and shared on the field. The aim of this document is to propose algorithms for the management of cardiovascular diseases during COVID-19 emergency with the objective of providing patients with optimal care, minimizing contagion risk and appropriately managing personal protective equipment.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions.

This paper provides an update on the European Society of Cardiology task force report on the management of chest pain. Its main purpose is to provide an update on the decision algorithms and diagnostic pathways to be used in the emergency department for the assessment and triage of patients with chest pain symptoms suggestive of acute coronary syndromes.

The ACR Committee on Incidental Findings presents recommendations for managing liver lesions that are incidentally detected on CT. These recommendations represent an update from the liver component of the ACR 2010 white paper on managing incidental findings in the pancreas, adrenal glands, kidneys, and liver. The Liver Subcommittee-which included five abdominal radiologists, one hepatologist, and one hepatobiliary surgeon-developed this algorithm. The recommendations draw from published evidence and expert opinion and were finalized by informal iterative consensus. Algorithm branches categorize liver lesions on the basis of patient characteristics and imaging features. They terminate with an assessment of benignity or a specific follow-up recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. The goal is to improve the quality of care by providing guidance on how to manage incidentally detected liver lesions.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Children form a specific group of patients, as there are significant differences between children and adults in both anatomy and physiology. Difficult airway may be unanticipated or anticipated. Difficulties encountered during intubation may cause hypoxia, hypoxic brain injury and, in extreme situations, may result in the patient’s death. There are few paediatric difficult-airway guidelines available in the current literature, and some of these have significant limitations. This position paper, intended for unanticipated difficult airway, was elaborated by the panel of specialists representing the Polish Society of Anaesthesiology and Intensive Care as well as the Polish Neonatal Society. It covers both elective intubation and emergency situations in children in all age groups. An integral part of the paper is an algorithm. The paper describes in detail all stages of the algorithm considering some modification in specific age groups, i.e. neonates.

International audience; The objective of this article was to evaluate the evidence currently available about the clinical value of artificial intelligence (AI) in breast imaging. Nine experts from the disciplines involved in breast disease management – including physicists and radiologists – convened a meeting on June 3, 2019 to discuss the evidence for the use of this technology in plenary and focused sessions. Prior to the meeting, the group performed a literature review on predefined topics. This paper presents the consensus reached by this working group on recommendations for the future use of AI in breast screening and related research topics.

The introduction of new therapeutic agents in chronic lymphocytic leukemia (CLL) and follicular lymphoma (FL), including the new kinase inhibitor idelalisib, has changed the therapeutic landscape of these diseases. However, the use of idelalisib is associated with a peculiar profile of side effects, which require an optimization of the current approach to prophylaxis and supportive treatment. Moving from the recognition that the abovementioned issue represents an unmet need in CLL and FL, a multidisciplinary panel of experts was convened to produce a consensus document aiming to provide practical recommendations for the management of the side effects during idelalisib therapy for CLL and FL. The present publication represents a consensus document from a series of meetings held during 2017. The Panel generated clinical key questions using the criterion of clinical relevance through a Delphi process and explored 4 domains, ie, diarrhea/colitis, transaminitis, pneumonitis, and infectious complications. Using the consensus method, the Panel was able to shape recommendations which may assist hematologist to minimize adverse events and guarantee adherence to treatment in patients with CLL and FL candidate to receive idelalisib.

NSAID-exacerbated respiratory disease (N-ERD) is a chronic eosinophilic, inflammatory disorder of the respiratory tract occurring in patients with asthma and/or chronic rhinosinusitis with nasal polyps (CRSwNP), symptoms of which are exacerbated by NSAIDs, including aspirin. Despite some progress in understanding of the pathophysiology of the syndrome, which affects 1/10 of patients with asthma and rhinosinusitis, it remains a diagnostic and therapeutic challenge. In order to provide evidence-based recommendations for the diagnosis and management of N-ERD, a panel of international experts was called by the EAACI Asthma Section. The document summarizes current knowledge on the pathophysiology and clinical presentation of N-ERD pointing at significant heterogeneity of this syndrome. Critically evaluating the usefulness of diagnostic tools available, the paper offers practical algorithm for the diagnosis of N-ERD. Recommendations for the most effective management of a patient with N-ERD stressing the potential high morbidity and severity of the underlying asthma and rhinosinusitis are discussed and proposed. Newly described sub-phenotypes and emerging sub-endotypes of N-ERD are potentially relevant for new and more specific (eg, biological) treatment modalities. Finally, the document defines major gaps in our knowledge on N-ERD and unmet needs, which should be addressed in the future.

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral “washout”, and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

Highlights • In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead., In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

The Canadian Association of Radiologists Incidental Findings Working Group consists of both academic subspecialty and general radiologists and is tasked with adapting and expanding upon the American College of Radiology incidental findings white papers to more closely apply to Canadian practice patterns, particularly more comprehensively dealing with the role of ultrasound and pursuing more cost-effective approaches to the workup of incidental findings without compromising patient care. Presented here are the 2020 Canadian guidelines for the management of hepatobiliary incidental findings. Topics covered include initial assessment of hepatic steatosis and cirrhosis, the workup of incidental liver masses identified on ultrasound and computed tomography (with algorithms presented), incidental gallbladder findings (wall thickening, calcification, and polyps), and management of incidental biliary dilatation.

Objective Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support. Methods Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab ® —a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated. Results The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4–6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1–4.6). Discussion Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab ® , user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant. Conclusion Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the digitization problem as a segmentation problem and proposed a deep learning method to digitize highly noisy ECG scans. Our method extracts the ECG signal in an end-to-end manner and can handle different paper record layouts. In the experiment, our model clearly extracted the ECG waveform with a Dice coefficient of 0.85 and accurately measured the common ECG parameters with more than 0.90 Pearson's correlation. We showed that the end-to-end approach with deep learning can be powerful in ECG digitization. To the best of our knowledge, we provide the first approach to digitize the least informative noisy binary ECG scans and potentially be generalized to digitize various ECG records.

In this article we show the development process to create a programming course for the first-year students. These young people are very prompt to be influenced by technology, they are constantly immersed in smartphones, connected 24/7. This raises the question; how can we interest a well-connected student to learn programming? Some years ago, we used to have paper and pencil to teach ”algorithms” while trying to bring a quick start to programming activities on basic science and engineering. Nowadays this approach is not viable, we need to use the same tools and platforms used by the students to access social media and the Internet in general, we need to use the computer directly to show them a programming language and simple tools for development. This paper describes the main goal of this course, bring the tools a student needs from simple to complex stage, based in case study methodology. In the first sessions, students are being monitored and guided by the teacher at all times. Later on, and progressively reaching the end of the course, the students will have less time with the teachers and more independence in order to develop self-learning skills.

Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.

Interferon (IFN)-γ release assays (IGRAs) are probably the most accurate tests for the detection of latent Mycobacterium tuberculosis infection, but IGRAs are labour intensive and the transport of samples over longer distances is difficult. IFN-γ-induced protein (IP)-10 is expressed at 100-fold higher levels than IFN-γ, and IP-10 release assays have comparable performance to IGRAs. The aim of this study was to explore the diagnostic potential of a novel IP-10 release assay based on dried plasma spots (DPS). The presence of IP-10 and IFN-γ was determined in plasma and in DPS by ELISA. Diagnostic algorithms for plasma and DPS tests for IP-10 were developed on a training cohort comprising 60 tuberculosis (TB) patients and 59 healthy controls. Diagnostic accuracy was assessed in a validation cohort comprising 78 TB patients and 98 healthy controls. Plasma was measured in Spain and DPS samples were sent to Denmark using the conventional postal service for analysis. IP-10 was readily detectable in both plasma and DPS, and correlation was excellent (r2 = 0.95). QuantiFERON-TB Gold In-Tube (QFT-TB) and IP-10 in DPS and plasma rendered comparable sensitivity (78%, 82% and 84%, respectively), specificity (100%, 97% and 97%, respectively) and indeterminate rates (p>0.55). The DPS-based IP-10 test has comparable diagnostic accuracy to the QFT-TB and samples can be sent via conventional mail over long distances for analysis without affecting the results.

Background Commissioning of treatment planning systems (TPS) and beam delivery for scanned light ion beams is an important quality assurance task. This requires measurement of large sets of high quality dosimetric data in anthropomorphic phantoms to benchmark the TPS and dose delivery under realistic conditions. Method A novel measurement setup is described, which allows for an efficient collection of a large set of accurate dose data in complex phantom geometries. This setup allows dose measurements based on a set of 24 small volume ionization chambers calibrated in dose to water and mounted in a holder, which can be freely positioned in a water phantom with various phantoms mounted in front of the water tank. The phantoms can be scanned in a CT and a CT-based treatment planning can be performed for a direct benchmark of the dose calculation algorithm in various situations. Results The system has been used for acceptance testing in scanned light ion beam therapy at Heidelberg Ion Beam Therapy Center for scanned proton and carbon ion beams. It demonstrated to be useful to collect large amounts of high quality data for comparison with the TPS calculation using various phantom geometries. Conclusion The setup is an efficient tool for commissioning and verification of treatment planning systems. It is especially suited for dynamic beam delivery, as many data points can be obtained during a single plan delivery, but can be adapted also for other dynamic therapies, like rotational IMRT.

In the present study, plant-scale process integration was applied to an Indian paper mill using the water cascade analysis (WCA) technique. Three limiting constraints, chemical oxygen demand (COD), total dissolved solids (TDS), and adsorbable organic halides (AOX), were considered for the study. A nearest neighbor algorithm was used to distribute the freshwater and recycled water among the plant operations. It was found that the limiting critical constraint depends upon the types of processes and streams involved in the integration. The limiting critical constraint can differ for different sections of the same industry, and can differ in different schemes of integration. After process integration, a 55.6% reduction in effluent flow, a 36% reduction in COD, and a 73% reduction in AOX were observed. After process integration, a 35.21% reduction in pollution costs can be achieved and, assuming the average production of the mill to be 225 tons per day, a savings of Indian rupees (INR) 1.73 per kg of paper produced can be achieved by employing process integration. The water cess was calculated as INR 3024.77 per day without integration for the sections that were considered for integration, while after integration, a 41.53% savings in the form of water cess was calculated.

Cardiac patients after an acute event and/or with chronic heart disease deserve special attention to restore their quality of life and to maintain or improve functional capacity. They require counselling to avoid recurrence through a combination of adherence to a medication plan and adoption of a healthy lifestyle. These secondary prevention targets are included in the overall goal of cardiac rehabilitation (CR). Cardiac rehabilitation can be viewed as the clinical application of preventive care by means of a professional multi-disciplinary integrated approach for comprehensive risk reduction and global long-term care of cardiac patients. The CR approach is delivered in tandem with a flexible follow-up strategy and easy access to a specialized team. To promote implementation of cardiac prevention and rehabilitation, the CR Section of the EACPR (European Association of Cardiovascular Prevention and Rehabilitation) has recently completed a Position Paper, entitled 'Secondary prevention through cardiac rehabilitation: A condition-oriented approach'. Components of multidisciplinary CR for seven clinical presentations have been addressed. Components include patient assessment, physical activity counselling, exercise training, diet/nutritional counselling, weight control management, lipid management, blood pressure monitoring, smoking cessation, and psychosocial management. Cardiac rehabilitation services are by definition multi-factorial and comprehensive, with physical activity counselling and exercise training as central components in all rehabilitation and preventive interventions. Many of the risk factor improvements occurring in CR can be mediated through exercise training programmes. This call-for-action paper presents the key components of a CR programme: physical activity counselling and exercise training. It summarizes current evidence-based best practice for the wide range of patient presentations of interest to the general cardiology community.

Insulin infusions are commonly utilized to control hyperglycemia in critically ill patients and decrease hyperglycemia associated complications. Safety concerns have been raised in trials evaluating methods of glycemic control regarding the incidence of hypoglycemia and its relationship to increased mortality. Electronic glycemic management systems (eGMS) may result in less variable blood glucose (BG) control and less hypoglycemia. This study aimed to compare BG control, time in target BG range, and the rate of hypoglycemia when critically ill patients were managed with an insulin infusion guided by paper-based protocol (PBP) versus eGMS.This retrospective review compared critically ill patients ≥ 18 years old that received insulin infusion from March to May 2015 (PBP group) and October to January 2017 (eGMS group). The primary outcome was the incidence of hypoglycemia. Secondary outcomes included frequency and severity of hypoglycemia, duration in glycemic target, length of insulin therapy, as well as ICU and hospital length of stay.Fifty-four patients were evaluated, 27 in each group. Percentage of days with BG70 mg/dL was significantly reduced after eGMS implementation (21.5% v 1.3%, P.0001) including the frequency of severe hypoglycemia (BG40 mg/dL) (5.4% v 0.01%, P.0001). Patients in the eGMS group spent a greater amount of time in target BG range (31.5% v 63.7%, P.0001).An eGMS has the potential to address many of the unmet needs of an optimal glycemic control strategy, minimizing hypoglycemia, and glycemic variability in a heterogeneous critically ill population.

Good cross-directional control of sheet variables in a paper machine depends on a number of factors. One such factor is the correct alignment of actuators with their corresponding responses. The alignment changes with time either locally or across the whole paper sheet due to a variety of reasons. In this paper, we provide an online mapping technique based on estimating the cross-correlation between actuator movement and the measurements. This procedure involves two steps–in the first step (called alignment monitoring) we determine if the current mapping is wrong at any CD position, and in the second step (called re-mapping), the alignment of actuators is corrected. The main advantage of this method is in its ability to re-align under closed loop conditions with minimal operational disruption.

The aim of this study was to compare the depiction ability of small grayscale contrasts in ink-jet printouts of digital radiographs on different print media with CRT monitor. A CCD-based digital cephalometric image of a stepless aluminum wedge containing 50 bur holes of different depth was cut into 100 isometric images. Each image was printed on glossy paper and on transparent film by means of a high-resolution desktop inkjet printer at specific settings. The printed images were viewed under standardized conditions, and the perceptibility of the bur holes was evaluated and compared to the perceptibility on a 17-in CRT monitor. Thirty observers stated their blinded decision on a five-point confidence scale. Areas (Az) under receiver operating characteristics curves were calculated and compared using the pair wise sign tests. Overall agreement was estimated using Cohen’s kappa device and observer bias using McNemar’s test. Glossy paper prints and monitor display revealed significantly higher (P

Many primary or secondary diseases of the myocardium are accompanied with complex remodelling of the cardiac tissue that results in increased heart mass, often identified as cardiac 'hypertrophy'. Although there have been numerous attempts at defining such 'hypertrophy', the present paper delineates the reasons as to why current definitions of cardiac hypertrophy remain unsatisfying. Based on a brief review of the underlying pathophysiology and tissue and cellular events driving myocardial remodelling with or without changes in heart dimensions, as well as current techniques to detect such changes, we propose to restrict the use of the currently popular term 'hypertrophy' to cardiac myocytes that may or may not accompany the more complex tissue rearrangements leading to changes in shape or size of the ventricles, more broadly referred to as 'remodelling'. We also discuss the great potential of genetically modified (mouse) models as tools to define the molecular pathways leading to the different forms of left ventricle remodelling. Finally, we present an algorithm for the stepwise assessment of myocardial phenotypes applicable to animal models using well-established imaging techniques and propose a list of parameters most suited for a critical evaluation of such pathophysiological phenomena in mouse models. We believe that this effort is the first step towards a much auspicated unification of the terminology between the experimental and the clinical cardiologists. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2011. For permissions please email: journals.permissionsoup.com.2011 © Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2011. For permissions please email: journals.permissionsoup.com.

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs.

Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the paper’s contributions, and to integrate these contributions into a broader context. However, many scientists struggle with producing high-quality manuscripts and typically get little training in paper writing. Focusing on how readers consume information, we present a set of 10 simple rules to help you get across the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.

This report updates concepts on hypertension management in patients with diabetes. It focuses on clinical outcomes literature published within the last 3 years and incorporates these observations into modifications of established guidelines. While the fundamentals of treatment and goal blood pressures remain unchanged, approaches to specific patient-related issues has changed. This update focuses on questions such as what to do when a patient has an elevated potassium level when therapy is initiated and whether combinations of agents that block the renin-angiotensin system still be used. In addition, there are updates from trials, just published and in press, that focus on related management issues influencing cardiovascular outcomes in persons with diabetes. Last, an updated algorithm is provided that incorporates many of the new findings and is suggested as a starting point to achieve blood pressure goals.

Idiopathic pulmonary fibrosis (IPF) is a fatal progressive lung disease occurring in adults. In the last decade, the results of a number of clinical trials based on the updated disease classification have been published. The registration of pirfenidone and nintedanib, the first two pharmacological treatment options approved for IPF, marks a new chapter in the management of patients with this disease. Other nonpharmacological treatments such as lung transplantation, rehabilitation and palliation have also been shown to be beneficial for these patients. In this review, past and present management is discussed based on a comprehensive literature search. A treatment algorithm is presented based on available evidence and our overall clinical experience. In addition, unmet needs with regard to treatment are highlighted and discussed. We describe the development of various treatment options for IPF from the first consensus to recent guidelines based on evidence from large-scale, multinational, randomized clinical trials, which have led to registration of the first drugs for IPF.

Objective To evaluate the design characteristics of studies that evaluated the performance of artificial intelligence (AI) algorithms for the diagnostic analysis of medical images. Materials and methods PubMed MEDLINE and Embase databases were searched to identify original research articles published between January 1, 2018 and August 17, 2018 that investigated the performance of AI algorithms that analyze medical images to provide diagnostic decisions. Eligible articles were evaluated to determine 1) whether the study used external validation rather than internal validation, and in case of external validation, whether the data for validation were collected, 2) with diagnostic cohort design instead of diagnostic case-control design, 3) from multiple institutions, and 4) in a prospective manner. These are fundamental methodologic features recommended for clinical validation of AI performance in real-world practice. The studies that fulfilled the above criteria were identified. We classified the publishing journals into medical vs. non-medical journal groups. Then, the results were compared between medical and non-medical journals. Results Of 516 eligible published studies, only 6% (31 studies) performed external validation. None of the 31 studies adopted all three design features: diagnostic cohort design, the inclusion of multiple institutions, and prospective data collection for external validation. No significant difference was found between medical and non-medical journals. Conclusion Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms.

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

BACKGROUND Surveillance of multiple congenital anomalies is considered to be more sensitive for the detection of new teratogens than surveillance of all or isolated congenital anomalies. Current literature proposes the manual review of all cases for classification into isolated or multiple congenital anomalies. METHODS Multiple anomalies were defined as two or more major congenital anomalies, excluding sequences and syndromes. A computer algorithm for classification of major congenital anomaly cases in the EUROCAT database according to International Classification of Diseases (ICD)v10 codes was programmed, further developed, and implemented for 1 year's data (2004) from 25 registries. The group of cases classified with potential multiple congenital anomalies were manually reviewed by three geneticists to reach a final agreement of classification as “multiple congenital anomaly” cases. RESULTS A total of 17,733 cases with major congenital anomalies were reported giving an overall prevalence of major congenital anomalies at 2.17%. The computer algorithm classified 10.5% of all cases as “potentially multiple congenital anomalies”. After manual review of these cases, 7% were agreed to have true multiple congenital anomalies. Furthermore, the algorithm classified 15% of all cases as having chromosomal anomalies, 2% as monogenic syndromes, and 76% as isolated congenital anomalies. The proportion of multiple anomalies varies by congenital anomaly subgroup with up to 35% of cases with bilateral renal agenesis. CONCLUSIONS The implementation of the EUROCAT computer algorithm is a feasible, efficient, and transparent way to improve classification of congenital anomalies for surveillance and research. Birth Defects Research (Part A), 2011. © 2011 Wiley-Liss, Inc.

Improved medical care of congenital heart disease patients increased survival into adulthood from 15% in the 1960s to over 85% in the current era. As a consequence, the prevalence of adult congenital heart disease (ACHD) increased rapidly,1 which is estimated to be >1 million ACHD patients in North America and 1.2 million in Europe. The growing number and aging of ACHD patients led to an overall increase in hospitalizations over the last decade and a substantial increase in patients presenting with heart failure (HF) (∼20%).2 The incidence of first HF-admission was 1.2 per 1000 patient-years in the Dutch national ‘CONCOR’ registry. Patients admitted with HF had a five-fold higher risk of death than those not admitted. From the same registry, the mortality was 2.8% during a follow-up period of 24 865 patient-years. Chronic HF (26%) and sudden death (19%) were recorded most frequently. The median age at death from HF was 51.0 years (range: 20.3–91.2 years).3 In another ACHD cohort, sudden death (26%) was the most common cause of death, followed by progressive HF (21%) and perioperative death (18%).4 Although patients with ACHD may not readily report symptoms, clinical HF is documented in 22.2% of patients with a Mustard repair for transposition of the great arteries (TGAs), 32.3% with congenitally corrected transposition of the great arteries (ccTGA), and 40% of patients after Fontan palliation. ### Heart failure with impaired systolic ventricular function The aetiology and triggers of impaired systolic ventricular function in ACHD patients are summarized in Table 1 . View this table: Table 1 Pathophysiology of heart failure with impaired systolic function: triggers (examples) ### Heart failure with preserved systolic ventricular function This occurs less often in ACHD patients, but is associated with specific conditions such as Shone complex and restrictive …

Motivation: Converting the vast quantity of free-format text found in journals into a concise, structured format makes the researcher's quest for information easier. Recently, several information extraction systems have been developed that attempt to simplify the retrieval and analysis of biological and medical data. Most of this work has used the abstract alone, owing to the convenience of access and the quality of data. Abstracts are generally available through central collections with easy direct access (e.g. PubMed). The full-text papers contain more information, but are distributed across many locations (e.g. publishers' web sites, journal web sites and local repositories), making access more difficult. In this paper, we present BioRAT, a new information extraction (IE) tool, specifically designed to perform biomedical IE, and which is able to locate and analyse both abstracts and full-length papers. BioRAT is a Biological Research Assistant for Text mining, and incorporates a document search ability with domain-specific IE. Results: We show first, that BioRAT performs as well as existing systems, when applied to abstracts; and second, that significantly more information is available to BioRAT through the full-length papers than via the abstracts alone. Typically, less than half of the available information is extracted from the abstract, with the majority coming from the body of each paper. Overall, BioRAT recalled 20.31% of the target facts from the abstracts with 55.07% precision, and achieved 43.6% recall with 51.25% precision on full-length papers. Availability: The software and documentation can be found at http://bioinf.cs.ucl.ac.uk/biorat

We explore the application of terahertz spectroscopic techniques for the remote determination of the water content of paper. The aim is the development of a rapid diagnostic imaging tool applicable in paper fabrication processes. THz radiation offers a high sensitivity for water, a good spatial resolution, and insensitivity to scattering at the paper surface. The advent of THz cameras makes fast large-area image detectors feasible. In this paper, we show for the case of a 0.6-THz fixed-frequency system, that the water content of paper can be determined with high accuracy. We demonstrate a quantitative (calibrated) method for determining the moisture content in paper based on extinction and phase measurements in the lower THz range with a spatial resolution in the mm-range and scanning times below two minutes.

WOS: 000356799600005, PubMed ID: 25999021, Acute heart failure is a fatal syndrome. Emergency physicians, cardiologists, intensivists, nurses and other health care providers have to cooperate to provide optimal benefit. However, many treatment decisions are opinion-based and few are evidenced-based. This consensus paper provides guidance to practicing physicians and nurses to manage acute heart failure in the pre-hospital and hospital setting. Criteria of hospitalization and of discharge are described. Gaps in knowledge and perspectives in the management of acute heart failure are also detailed. This consensus paper on acute heart failure might help enable contiguous practice., Bayer; Cardiorentis; Medicine Company; Critical Diagnostics; Novartis; Steering Committee member of Cardiorentis; TUBITAK; Medicines Company; Cornerstone Therapeutics; Otsuka; Janssen; Apex Innovations; Inte-Section Medical; Trevena; Vifor Pharma Ltd; Amgen; Servier; Abbott Vascular; Coridea; Respicardia; Swiss National Science Foundation; Swiss Heart Foundation; Cardiovascular Research Foundation Basel; 8sense; Abbott; ALERE; Brahms; Nanosphere; Roche; Siemens; University Hospital Basel; Astra Zeneca; BG medicine; Biomerieux; Lilly; Orion; Resmed; Roche Diagnostics; Ratiopharm; BMS; Boehringer-Ingelheim; Pfizer; Daiichi Sankyo; Boehinger Ingelheim; AstraZeneca; European Union, A.M. received speaker's honoraria from Alere, Bayer, Edwards Life Sciences, The Medicines Company, Novartis, Orion, Servier, Thermofisher, Vifor Pharma and also received fee as member of advisory board and/or Steering Committee from Bayer, Cardiorentis, The Medicine Company, Critical Diagnostics.; M.B.Y. received speaker's honoraria and research fee from Novartis and received fee as Steering Committee member of Cardiorentis, and is supported by TUBITAK.; P.L. received speaker's honoraria from Beckman Coulter and Novartis and also received fees as a member of advisory board and/or Steering Committee from Bayer, Cardiorentis, The Medicines Company, Cornerstone Therapeutics, Novartis, Otsuka, Janssen, Apex Innovations, Inte-Section Medical, and Trevena.; P.P. received speaker's honoraria from Bayer, Novartis, Servier, Vifor Pharma, Amgen, Pfizer, Cardiorentis, Merck-Serono, Abbott Vascular and Respicardia and also received fee as member of advisory board and/or Steering Committee from Bayer, Cardiorentis, Novartis, Vifor Pharma Ltd, Amgen, Servier, Abbott Vascular, Coridea and Respicardia.; E.L. received consultancy fee from Novartis.; J.M. received honoraria for speaker or advisor from Abbott, Novartis, Orion, Otsuka, and Sanofi and fee as a member of Steering Committee from Corthera, Novartis, and Cardiorentis.; C.M. received research grants from the Swiss National Science Foundation and the Swiss Heart Foundation, the Cardiovascular Research Foundation Basel, 8sense, Abbott, ALERE, Brahms, Critical Diagnostics, Nanosphere, Roche, Siemens, and the University Hospital Basel, as well as speaker/consulting honoraria from Astra Zeneca, Abbott, ALERE, BG medicine, Biomerieux, Brahms, Cardiorentis, Lilly, Novartis, Pfizer, Roche, and Siemens.; V.P.H. received speaker's fee: Bayer, Orion, Resmed, Roche Diagnostics, Ratiopharm; consultation fees: Bayer, BMS, Boehringer-Ingelheim, Novartis, Pfizer, Roche Diagnostics, Servier. H.A.T. received speaker's honoraria from Daiichi Sankyo, Lilly, Medicines Company, AstraZeneca, Boehinger Ingelheim. Advisory board for Maquet Cardiovascular. Institutional research support by Maquet Cardiovascular, Teleflex, Terumo, Lilly, The Medicine Company.; G.F. has received research grants and/or has served as Committee member or Cochair of studies sponsored by Bayer, Novartis, Cardiorentis, Vifor Pharma, and the European Union.

Objectives. To establish referential values ranges of hyposalivation and normosalivation for the salivary flow rate (SFR) of upper labial (LS) and palatal (PS) mucosa using Schirmer's test strips paper and as a second goal to determine the values ranges of the SFR of palatal (PS) and upper labial (LS) mucosa in subjects with and without xerostomia.Methods. A cross-sectional study was conducted among subjects distributed in three groups according to their unstimulated and stimulated whole saliva.Results. 144 subjects were enrolled in groups as follows: severe hyposalivation (n=24), mild hyposalivation (n=78), and normosalivation (n=42). The mean and the 95% confidence interval for the LS flow rate (μL/cm2/min) were 3.2 (2.46 to 3.94), 5.86 (4.96 to 6.75), and 9.08 (7.63 to 10.53) (P<0.001) for each group, respectively. The PS results were 1.01 (0.68 to 1.34), 1.72 (1.31 to 2.13), and 2.44 (1.66 to 3.22) (P=0.014). Xerostomia complainers presented lower rates of LS (5.17 (4.06 to 6.23)) than non-complainers (7.33 (6.4 to 8.27)) (P=0.003).Conclusions. The test was reliable to provide referential values ranges for LS flow rate measurement and was shown to be valid to distinguish normosalivation from severe and mild hyposalivation and also to predict xerostomia.

Ischaemic postconditioning (brief periods of ischaemia alternating with brief periods of reflow applied at the onset of reperfusion following sustained ischaemia) effectively reduces myocardial infarct size in all species tested so far, including humans. Ischaemic postconditioning is a simple and safe manoeuvre, but because reperfusion injury is initiated within minutes of reflow, postconditioning must be applied at the onset of reperfusion. The mechanisms of protection by postconditioning include: formation and release of several autacoids and cytokines; maintained acidosis during early reperfusion; activation of protein kinases; preservation of mitochondrial function, most strikingly the attenuation of opening of the mitochondrial permeability transition pore (MPTP). Exogenous recruitment of some of the identified signalling steps can induce cardioprotection when applied at the time of reperfusion in animal experiments, but more recently cardioprotection was also observed in a proof-of-concept clinical trial. Indeed, studies in patients with an acute myocardial infarction showed a reduction of infarct size and improved left ventricular function when they underwent ischaemic postconditioning or pharmacological inhibition of MPTP opening during interventional reperfusion. Further animal studies and large-scale human studies are needed to determine whether patients with different co-morbidities and co-medications respond equally to protection by postconditioning. Also, our understanding of the underlying mechanisms must be improved to develop new therapeutic strategies to be applied at reperfusion with the ultimate aim of limiting the burden of ischaemic heart disease and potentially providing protection for other organs at risk of reperfusion injury, such as brain and kidney.

The importance of writing well can never be overstated for a successful professional career, and the ability to write solid papers is an essential trait of a productive researcher. Writing and publishing a paper has its own life cycle; properly following a course of action and avoiding missteps can be vital to the overall success not only of a paper but of the underlying research as well. Here, we offer ten simple rules for writing and publishing research papers. As a caveat, this essay is not about the mechanics of composing a paper, much of which has been covered elsewhere, e.g., [1], [2]. Rather, it is about the principles and attitude that can help guide the process of writing in particular and research in general. In this regard, some of the discussion will complement, extend, and refine some advice given in early articles of this Ten Simple Rules series of PLOS Computational Biology [3]–[8].

Today's real-time systems are characterized by managing large volumes of data. Efficient database management algorithms for accessing and manipulating data are required to satisfy timing constraints of supported applications. Real-time database systems involve a new research area investigating possible ways of applying database systems technology to real-time systems. Management of real-time information through a database system requires the integration of concepts from both real-time systems and database systems. Some new criteria need to be developed to involve timing constraints of real-time applications in many database systems design issues, such as transaction/query processing, data buffering, CPU, and IO scheduling. In this paper, a basic understanding of the issues in real-time database systems is provided and the research efforts in this area are introduced. Different approaches to various problems of real-time database systems are briefly described, and possible future research directions are discussed. © 1995.

We have used Laser Speckle Contrast Imaging (LSCI) to investigate the statistical properties of dynamic speckle reflected from an obscuring scattering surface in order to reveal drawings that are hidden beneath. Here we explore the limitations of this method used with various algorithms when applied to a selection of paper samples. These samples consist of a sketch executed in an assortment of media laid on a base surface that are then hidden beneath a subsequent top layer. The ability to resolve gray scale images was examined as well as the contrast surface temperature relationship. A book with glued pages was investigated in order to demonstrate the technique's applicability to the non-destructive examination of cultural materials. While being shown as a useful tool in revealing obscured drawings the scattering properties of the surface layer present a limitation in its general applicability.

PURPOSE: Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images. METHODS: In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed. RESULTS: Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated. CONCLUSIONS: We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.

The present document is the result of a consensus reached by a panel of experts from European and non-European countries on Occupational Rhinitis (OR), a disease of emerging relevance which has received little attention in comparison to occupational asthma. The document covers the main items of OR including epidemiology, diagnosis, management, socio-economic impact, preventive strategies and medicolegal issues. An operational definition and classification of OR tailored on that of occupational asthma, as well as a diagnostic algorithm based on steps allowing for different levels of diagnostic evidence are proposed. The needs for future research are pointed out. Key messages are issued for each item.

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Motivation Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug’s mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. Results In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet’s ability to use mechanistic insight to identify a drug’s mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug–target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method’s utility in providing mechanistic insight into repurposing predictions it provides. Availability and implementation The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). Supplementary information Supplementary data are available at Bioinformatics online.

Objective: To assess the utility of examining the nigrostriatal system with MRI and dopamine transporter (DAT) imaging for evaluating the preclinical phase of Parkinson’s disease (PD). Methods: The subjects were 32 patients with early PD and a history of probable rapid eye movement sleep behavior disorder (RBD; PD group), 15 patients with idiopathic RBD (RBD group), and 24 age-matched healthy controls (HC group) who underwent neuromelanin and diffusion tensor MRI for analysis of the substantia nigra pars compacta (SNpc). The RBD and PD groups underwent DAT imaging. In the RBD group, totals of 39 MRI and 27 DAT imaging examinations were obtained longitudinally. For each value, intergroup differences and receiver operating characteristic analysis for diagnostic performance were examined statistically. Results: The neuromelanin value was significantly lower and the diffusion tensor values except fractional anisotropy were significantly higher in the RBD and PD groups than in the HC group. The DAT specific binding ratio (SBR) was significantly lower in the PD group than in the RBD group. The areas under the receiver operating characteristic curves (AUCs) for neuromelanin/mean diffusivity value in the SNpc were 0.76/0.82 for diagnosing RBD and 0.83/0.80 for diagnosing PD. The area under the receiver operating characteristic curves for the SBR for discriminating PD from RBD was 0.87. Conclusion: MRI and DAT imaging may be useful for evaluating sequential nigrostriatal changes during the preclinical phase of PD. Advances in knowledge: MRI detects nigrostriatal changes in both RBD and early PD, and DAT imaging detects nigrostriatal changes during the transition to PD in RBD.