Part 2: The Pediatric Hazards of Art Materials.

Children can encounter art materials that contain hazardous substances at school, in the home, and during outside activities. Hazardous substances can be found in both art materials that are made to be used by children and art materials used by adults. Some of these materials can be severe irritants, allergens, carcinogens, or other chronic disease hazards. Many of the most frequently used and potentially most hazardous materials are found in the categories of solvents, pigments, and adhesives. Selected members of these categories and where they may be found in common art materials are briefly discussed. Preventive techniques that specifically address the potential hazards of each category are included. [Pediatr Ann. 2023;52(6):e219-e230.].

Part 1: The Pediatric Hazards of Art Materials.

Hazardous substances can be found in art materials that children encounter at school, at home, and in other settings, and adult behaviors can contribute to children's risks from these hazardous art materials. Some art materials contain severe irritants, allergens, chronic health hazards, and carcinogens. Most of the hazardous substances found in art materials are known from adult exposures in occupational or environmental settings, and few have been specifically studied in children. For many of these hazards, only limited treatments are available, so prevention is imperative. Despite laws focusing on art material labeling and designation as safe for children, there are some concerns about the accuracy of these labels. Children are at special risks from hazardous material exposures because of their developing physiologies and intellects. A wide range of art activities are taught in schools and some can include hazardous materials. Art activities and precautions that are appropriate for children in 6th grade and below and others for 7th grade and older are outlined. Excellent resources are available for further information on hazardous art materials, prevention recommendations, and health and safety programs for schools. [Pediatr Ann. 2023;52(6):e213-e218.].

Campylobacter myocarditis: a rare but potential sequelae of Campylobacter jejuni.

Myocarditis is an inflammatory cardiomyopathy with a diverse range of both infective and non-infective causes. It is an important cause of dilated cardiomyopathy worldwide, with a variable clinical course ranging from a mild self-limiting illness to fulminant cardiogenic shock requiring mechanical circulatory support and cardiac transplantation. Here, we describe a case of acute myocarditis secondary to Campylobacter jejuni infection in a man in his 50s who presented with an acute coronary syndrome following a recent gastrointestinal illness.

Contrasting the potential benefits of early invasive coronary angiography in acute and chronic myocardial injury patterns.

BACKGROUND: In cases of evolving myocardial injury not definitively attributed to coronary ischaemia precipitated by plaque rupture, referral for invasive coronary angiography (ICA) may be influenced by observed troponin profiles. We sought to explore association between early ICA and elevated high-sensitivity troponin T (hs-cTnT) concentrations with and without dynamic changes, to examine if there may be a hs-cTnT threshold associated with benefit from an initial ICA strategy. METHODS: Using published studies (hs-cTnT study n = 1937, RAPID-TnT study n = 3270) and the Fourth Universal Definition of Myocardial Infarction (MI), index presentations of patients with hs-cTnT concentrations 5-14ng/L were classified as 'non-elevated' (NE). Hs-cTnT greater than upper reference limit (14ng/L) were classified as 'elevated hs-cTnT with dynamic change' (encompassing acute myocardial injury, Type 1 MI, and Type 2 MI), or 'non-dynamic hs-cTnT elevation' (chronic myocardial injury). Patients with hs-cTnT <5ng/L and/or eGFR<15mmol/L/1.73m2 were excluded. ICA was performed within 30 days of admission. Primary outcome was defined as composite endpoint of death, MI, or unstable angina at 12 months. RESULTS: Altogether, 3620 patients comprising 837 (23.1%) with non-dynamic hs-cTnT elevations and 332 (9.2%) with dynamic hs-cTnT elevations were included. Primary outcome was significantly higher with dynamic and non-dynamic hs-cTnT elevations (Dynamic: HR: 4.13 95%CI:2.92-5.82; p<0.001 Non-dynamic: HR: 2.39 95% confidence interval [CI]:1.74-3.28, p<0.001). Hs-cTnT thresholds where benefit from initial ICA strategy appeared to emerge was observed at 110ng/L and 50ng/L in dynamic and non-dynamic elevations, respectively. CONCLUSION: Early ICA appears to portend benefit in hs-cTnT elevations with and without dynamic changes, and at lower hs-cTnT threshold in non-dynamic hs-cTnT elevation. Differences compel further investigation.

The Association between Pre-season Running Loads and Injury during the Subsequent Season in Elite Gaelic Football.

The aim of this study was to determine if the quantity of running load performed in pre-season affects the incidence of injury in elite Gaelic footballers. It was hypothesized that a greater quantity of running loads completed will reduce the incidence rate of injury. A cohort of elite male Gaelic football players (n = 25) was prospectively monitored throughout one full season. This longitudinal study involved the collection of GPS data and injury data. Participants were retrospectively divided into two groups and assigned to a high (HTL, completed >50% of pre-season sessions, n = 13) or low (LTL, completed <50% of pre-season sessions, n = 12) training load group based on the percentage of pre-season sessions completed. Data for total distance, PlayerLoad™, meters covered at running speeds (4.0−4.84 m/s), meters covered at high running speeds (4.85−6.39 m/s), meters covered at sprint speeds (>6.4 m/s) and high-intensity running meters (high-speed running meters and sprint meters combined) were collected during all sessions. A one-way analysis of variance (ANOVA) was completed to understand the variation of external training load data across the different phases of the season. A series of repeated measures of ANOVA were subsequently completed to understand the variation of external training load data across seasonal phases within the training groups. Although the LTL group had a higher incidence rate of non-contact injuries (large effect size) per 1000 h of exposure in each phase of the season, statistical analysis revealed that there was no significant difference (F = 4.32, p = 0.173, partial η2 = 0.684, large) between the HTL (14.9 ± 4.17/1000 h) and the LTL (24.5 ± 7.36/1000 h) groups. Additionally, the HTL group was able to sustain greater running loads in the competitive phases of the season compared to the LTL group, total distance (F = 8.10, p < 0.001, partial η2 = 0.299, large), high-speed running distance (F = 8.74, p < 0.001, partial η2 = 0.304, large) and high-intensity running distance (F = 7.63, p < 0.001, partial η2 = 0.276, large). Furthermore, players who complete a greater proportion of running loads in pre-season may alter their body composition levels to more optimal levels, which in turn may reduce the risk of injury while also helping increase or maintain performance-related fitness markers such as aerobic fitness.

Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study.

BACKGROUND: The COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic in the long term, it is pivotal to understand what factors drive prevalence rates and to predict the future trajectory of the virus. OBJECTIVE: This study had 2 objectives. First, it tested the statistical relationship between socioeconomic status and COVID-19 prevalence. Second, it used machine learning techniques to predict cumulative COVID-19 cases in a multicountry sample of 182 countries. Taken together, these objectives will shed light on socioeconomic status as a global risk factor of the COVID-19 pandemic. METHODS: This research used exploratory data analysis and supervised machine learning methods. Exploratory analysis included variable distribution, variable correlations, and outlier detection. Following this, the following 3 supervised regression techniques were applied: linear regression, random forest, and adaptive boosting (AdaBoost). Results were evaluated using k-fold cross-validation and subsequently compared to analyze algorithmic suitability. The analysis involved 2 models. First, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 reported case data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index (HDI) metrics of life expectancy, mean years of schooling, expected years of schooling, and gross national income were used to approximate socioeconomic status. RESULTS: All variables correlated positively with the 2021 COVID-19 prevalence, with R2 values ranging from 0.55 to 0.85. Using socioeconomic indicators, COVID-19 prevalence was predicted with a reasonable degree of accuracy. Using 2020 reported case rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R2=0.543). When socioeconomic indicators were added alongside 2020 prevalence rates as features, the average predictive performance improved considerably (R2=0.721) and all error statistics decreased. Thus, adding socioeconomic indicators alongside 2020 reported case data optimized the prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R2=0.693 on the first model and R2=0.763 on the second model, followed by random forest (0.481 and 0.722) and AdaBoost (0.454 and 0.679). Following this, the second model was retrained using a selection of additional COVID-19 risk factors (population density, median age, and vaccination uptake) instead of the HDI metrics. However, average accuracy dropped to 0.649, which highlights the value of socioeconomic status as a predictor of COVID-19 cases in the chosen sample. CONCLUSIONS: The results show that socioeconomic status is an important variable to consider in future epidemiological modeling, and highlights the reality of the COVID-19 pandemic as a social phenomenon and a health care phenomenon. This paper also puts forward new considerations about the application of statistical and machine learning techniques to understand and combat the COVID-19 pandemic.

An assessment of current concussion identification and diagnosis methods in sports settings: a systematic review.

BACKGROUND: Concussion in sport is an ongoing global concern. The head injury assessment (HIA) by the field of play is acknowledged as the first step in recognising and identifying concussion. While previous systematic literature reviews have evaluated the sensitivity of side-line screening tools and assessment protocols, no systematic review has evaluated the research designs and assessments used in a field setting. This systematic review investigated existing screening and diagnostic tools used in research as part of the HIA protocol to identify concussion that are currently used in professional, semi-professional and amateur (club) sports settings. METHODS: A systematic searching of relevant databases was undertaken for peer-reviewed literature between 2015 and 2020. RESULTS: Twenty-six studies met the inclusion criteria. Studies were of moderate to good quality, reporting a variety of designs. The majority of studies were undertaken in professional/elite environments with medical doctors and allied health practitioners (e.g., physical therapists) involved in 88% of concussion assessments. While gender was reported in 24 of the 26 studies, the majority of participants were male (77%). There was also a variety of concussion assessments (n = 20) with the sports concussion assessment tool (SCAT) used in less than half of the included studies. CONCLUSION: The majority of studies investigating concussion HIAs are focused on professional/elite sport. With concussion an issue at all levels of sport, future research should be directed at non-elite sport. Further, for research purposes, the SCAT assessment should also be used more widely to allow for consistency across studies.

Sodium-glucose cotransport-2 inhibitor induced ketoacidosis following coronary artery bypass surgery: implications for management.

Sodium-glucose co-transporter 2 inhibitors (SGLT2i) improve cardiovascular outcomes in patients with type 2 diabetes (T2D). Diabetic ketoacidosis (DKA) is an uncommon, but well recognised, life-threatening complication of SGLT2i. In a retrospective study of patients with T2D undergoing cardiac surgery at our institution, DKA occurred in 15.3% of patients taking SGLT2i at the time of surgery, compared with 0.47% of non-SGLT2i-treated patients. Intravenous insulin in the first 24 h after surgery was associated with a significantly lower risk of DKA in SGLT2i patients. Use of an insulin infusion should be considered in these patients, especially in those who are unable to cease their SGLT2i pre-operatively.

High-precision inversion of dynamic radiography using hydrodynamic features.

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.

Clinical and operational insights from data-driven care pathway mapping: a systematic review.

BACKGROUND: Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The "as is" or "de facto" care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained. METHODS: A systematic review was conducted of literature abstracting sequential patient care activities ("de facto" care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned. RESULTS: 254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes. DISCUSSION: Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues. CONCLUSION: This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Estimating and examining the costs of inpatient diabetes care in an Irish Public Hospital.

AIM: To estimate and examine hospitalisation costs of Type 1 and Type 2 diabetes in an Irish public hospital. METHODS: A retrospective audit of hospital inpatient admissions over a 5-year period was undertaken, and a wide range of admission-related data were collected for a sample of 7,548 admissions. Hospitalisations were costed using the diagnosis-related group methodology. A series of descriptive, univariate and multivariate regression analyses were undertaken. RESULTS: The mean hospitalisation cost for Type 1 diabetes was €4,027 and for Type 2 diabetes was €5,026 per admission. Sex, admission type and length of stay were significantly associated with hospitalisation costs for admissions with a primary diagnosis of Type 1 diabetes. Age, admission type, diagnosis status, complications status, discharge destination, length of stay and year were significantly associated with hospitalisation costs for admissions with a primary diagnosis of Type 2 diabetes. Length of stay was associated with higher mean costs, with each additional day increasing Type 1 diabetes costs by €260 (p = 0.001) and Type 2 diabetes by €216 (p < 0.001). Unscheduled admissions were associated with significantly lower costs than elective admissions; €1,578 (p = 0.035) lower for Type 1 diabetes and €2,108 (p < 0.001) lower for Type 2 diabetes. CONCLUSIONS: This study presents estimates of the costs of diabetes care in the Irish public hospital system and identifies the factors which influence costs for Type 1 and Type 2 diabetes. These findings may be of interest to patients, the public, researchers and those with influence over diabetes policy and practice in Ireland and internationally.

Can Clinical Outcomes Be Improved, and Inpatient Length of Stay Reduced for Adults With Diabetes? A Systematic Review.

Aim: To examine the efficacy of clinical practice strategies in improving clinical outcomes and reducing length of hospital stay for inpatients with Type 1 and Type 2 diabetes. Background: People living with diabetes are at increased risk of being admitted to hospital and to stay in hospital longer than those who do not have the condition. Diabetes and its complications cause substantial economic loss to those living with the condition, their families, to health systems and national economies through direct medical costs and loss of work and wages. Length of stay is a major factor driving up hospitalisation costs relating to those with Type 1 and Type 2 diabetes with suboptimal blood glucose management, hypoglycaemia, hyperglycaemia, and co-morbidities shown to considerably impact upon length of stay. The identification of attainable evidence-based clinical practice strategies is necessary to inform the knowledge base and identify service improvement opportunities that could lead to improved clinical outcomes for these patients. Study Design: A systematic review and narrative synthesis. Methods: A systematic search of CINAHL, Medline Ovid, and Web of Science databases was carried out to identify research papers reporting on interventions that have reduced length of hospital stay for inpatients living with diabetes for the period 2010-2021. Selected papers were reviewed, and relevant data extracted by three authors. Eighteen empirical studies were included. Results: Eighteen studies spanned the themes of clinical management innovations, clinical education programmes, multidisciplinary collaborative care and technology facilitated monitoring. The studies demonstrated improvements in healthcare outcomes such as glycaemic control, greater confidence with insulin administration and reduced occurrences of hypoglycaemia and hyperglycaemia and decreased length of hospital stay and healthcare costs. Conclusions: The clinical practice strategies identified in this review contribute to the evidence base for inpatient care and treatment outcomes. The implementation of evidence-based research can improve clinical practice and show that appropriate management can enhance clinical outcomes for the inpatient with diabetes, potentially leading to reductions in length of stay. Investment in and commissioning of practices that have the potential to afford clinical benefits and reduce length of hospital stay could influence the future of diabetes care. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=204825, identifier 204825.

Local models for scatter estimation and descattering in polyenergetic X-ray tomography.

We propose a new modeling approach for scatter estimation and descattering in polyenergetic X-ray computed tomography (CT) based on fitting models to local neighborhoods of a training set. X-ray CT is widely used in medical and industrial applications. X-ray scatter, if not accounted for during reconstruction, creates a loss of contrast in CT reconstructions and introduces severe artifacts including cupping, shading, and streaks. Even when these qualitative artifacts are not apparent, scatter can pose a major obstacle in obtaining quantitatively accurate reconstructions. Our approach to estimating scatter is, first, to generate a training set of 2D radiographs with and without scatter using particle transport simulation software. To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it. We compared local and global (fit on full data sets) versions of several X-ray scatter models, including two from the recent literature, as well as a recent deep learning-based scatter model, in the context of descattering and quantitative density reconstruction of simulated, spherically symmetrical, single-material objects comprising shells of various densities. Our results show that, when applied locally, even simple models provide state-of-the-art descattering, reducing the error in density reconstruction due to scatter by more than half.

Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction.

Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typically involve supervised learning or unsupervised learning, both of which have their advantages and disadvantages. In this work, we propose a unified supervised-unsupervised (SUPER) learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis. The proposed training algorithm is also an approximate scheme for a bilevel supervised training optimization problem, wherein the network-based regularizer in the lower-level MBIR problem is optimized using an upper-level reconstruction loss. The training problem is optimized by alternating between updating the network weights and iteratively updating the reconstructions based on those weights. We demonstrate the learned SUPER models' efficacy for low-dose CT image reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand Challenge dataset for training and testing. In our experiments, we studied different combinations of supervised deep network priors and unsupervised learning-based or analytical priors. Both numerical and visual results show the superiority of the proposed unified SUPER methods over standalone supervised learning-based methods, iterative MBIR methods, and variations of SUPER obtained via ablation studies. We also show that the proposed algorithm converges rapidly in practice.

Hypoxemia without Respiratory Distress: Hereditary Hemorrhagic Telangiectasia in a Child.

Hereditary hemorrhagic telangiectasia (HHT) is an underrecognized genetic disorder of vascular development in pediatric patients. Its presentation can range from mild cutaneous findings to life-threatening hemorrhage from arteriovenous malformations. Clinical diagnosis can be challenging in the pediatric population as disease manifestations evolve over time and may be difficult to identify in younger patients. This case highlights how nonspecific symptoms and signs in the preanesthesia period can be misleading, potentially placing a patient with unrecognized HHT at risk for significant morbidity and mortality.

DPM Foot and Ankle Fellowship Selection Criteria: Survey of Fellowship Directors.

BACKGROUND: A recent increase in podiatric medicine fellowships has occurred as the field continues to progress. Research regarding selection criteria from a fellowship director's perspective for potential fellows is lacking. This study aimed to examine objective and subjective selection criteria that directors consider when selecting applicants for the interview and when ranking prospective fellows after the interview. METHODS: We electronically surveyed American College of Foot and Ankle Surgeons fellowship directors with preselected criteria for granting applicants an interview and for compiling their ranking list after the interview. A Likert scale from 1 (most important) to 5 (least important) was used to prioritize each criterion, an average rating was calculated, and the results were placed in order of importance. RESULTS: The most important selection criteria for granting an interview were quality of residency program (1.985), a written personal statement of reasons for attending that fellowship (2.063), and publications/presentations produced as a resident (2.267). The most important criteria in completing the ranking order after the interview were assessment of applicant's personality (1.111), interview performance (1.173), and expressed interest in program (1.563). CONCLUSIONS: Knowledge of the selection criteria that fellowship directors seek in applicants can assist those who desire to further their training. The selection criteria that program directors seek differed between being selected for the interview, which combined both objective and subjective criteria, and when compiling their rankings after the interview, which included only subjective criteria. Results show more emphasis on subjective selection criteria when directors select applicants for an interview and when ranking applicants after the interview.

Acquired digital fibrokeratoma: a rare case study.

Acquired digital fibrokeratomas are a benign soft tissue tumor with typical appearance and anatomical locations. This lesion generally occurs in middle aged males with common sites of occurrence in the digits of upper or lower extremities. Previous case studies have reported incidences of this lesion appearing on heels of middle-aged males but are generally described as giant digital fibrokeratoma based on the lesion's diameter. This case report describes an acquired digital fibrokeratoma in a pediatric female patient in an infrequent location.

Impact of adherence to surgical and non-surgical components of infective endocarditis guidelines and recommendations.

BACKGROUND: Infective endocarditis (IE) is associated with significant morbidity and mortality. Non-adherence to IE guidelines and recommendations is frequent, and may adversely impact patient outcomes. AIM: To assess the impact of non-adherence to components of existing IE guidelines and recommendations on a composite outcome consisting of any of the following: mortality, unplanned cardiac surgery, embolic event or relapse of positive blood culture within six months of diagnosis. METHODS: A single centre, retrospective cohort study. RESULTS: Amongst 157 patients, there was inconsistent adherence to: initial diagnosis of an infective condition (87%), timely administration of antimicrobial therapy (82%), appropriateness of predominant antimicrobial regime (94%), appropriate management of the portal of entry (86%), multidisciplinary input (75%), end of antimicrobial therapy repeat echocardiography (60%) and adherence to indications for surgery (76%). Inpatient mortality was 12.1% (n = 19) and the composite adverse outcome occurred in 36 (22.9%) patients. In multivariate logistic regression analysis, infection of prosthetic device (adjusted odds ratio [95% confidence interval]; 2.43 [1.07-5.50]) and non-adherence to surgical guidelines (aOR 3.67 [1.60-8.47]) were significantly associated with an adverse outcome. CONCLUSIONS: Our data suggests that adherence to differing components of IE management guidelines and recommendations varies and that non-adherence to surgical aspects of guidelines has the biggest impact in determining outcomes.

A value proposition for natriuretic peptide measurement in the assessment of patients with suspected acute heart failure.

There is robust clinical trial evidence supporting the role of natriuretic peptides [NPs] in the assessment of patients presenting with suspected acute heart failure [AHF]. Despite the fact that clinical guidelines have for some time advocated NP measurement, the availability and uptake of NP testing in acute care services remains patchy and incomplete. The reasons for this are multifactorial but are underpinned by compartmentalised management and budget structures within complex healthcare delivery organisations. This paper outlines a value proposition for NP testing in the acute care setting which crosses the continuum of services and budgets. It articulates the expected benefits to each stakeholder in terms of efficiency of processes, clinical outcomes and cost effectiveness. It describes a pathway to implementation and suggests metrics that may be used to measure the effectiveness of introduction of NP testing. It is hoped that the value proposition will facilitate the uptake of NT testing fostering collaboration between laboratory, clinical, management and finance teams and by informing the development of business cases.

Calcaneal Osteomyelitis: Current Treatment Concepts.

Calcaneal osteomyelitis can be a debilitating disease if proper treatment protocols are not initiated. This literature review details the epidemiology, clinical, diagnostic, and prognostic factors, and medical management in those who developed this disease.