Fast conditional density estimation for quantitative structure-activity relationships

Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional methods for conditional density estimation, they are based on generic machine learning schemes, more specifically, class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training. Therefore, generic machine-learning based methods for conditional density estimation may be a good and fast option for quantifying uncertainty in QSAR modeling.http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/181

Popliteal Cysts in Paediatric Patients: Clinical Characteristics and Imaging Features on Ultrasound and MRI

Popliteal cysts, or Baker cysts, are considered rare in children and may exhibit particular features, as compared with adults. We studied data from 80 paediatric patients with 55 Baker cysts, examined over a period of 7 years, and correlated clinical presentation with findings on ultrasonography and MRI. Prevalence of popliteal cysts was 57% in arthritic knees, 58% with hypermobility syndrome, and 28% without risk factors. Only one patient had a trauma history and showed an ipsilateral cyst. Mean cyst volume was 3.4 mL; cysts were larger in boys. Patients with arthritis had echogenic cysts in 53%. Cyst communication with the joint space was seen in 64% on ultrasonography and 86% on MRI. In conclusion, Baker cysts are a common finding in a clinically preselected paediatric population. Children with Baker cysts should be assessed for underlying arthritis and inherited joint hypermobility, while sporadic Baker cysts appear to be common, as well

Post-Pandemic Seroprevalence of Pandemic Influenza A (H1N1) 2009 Infection (Swine Flu) among Children <18 Years in Germany

We determined antibodies to the pandemic influenza A (H1N1) 2009 virus in children to assess: the incidence of (H1N1) 2009 infections in the 2009/2010 season in Germany, the proportion of subclinical infections and to compare titers in vaccinated and infected children. Eight pediatric hospitals distributed over Germany prospectively provided sera from in- or outpatients aged 1 to 17 years from April 1(st) to July 31(st) 2010. Vaccination history, recall of infections and sociodemographic factors were ascertained. Antibody titers were measured with a sensitive and specific in-house hemagglutination inhibition test (HIT) and compared to age-matched sera collected during 6 months before the onset of the pandemic in Germany. We analyzed 1420 post-pandemic and 300 pre-pandemic sera. Among unvaccinated children aged 1-4 and 5-17 years the prevalence of HI titers (≥1∶10) was 27.1% (95% CI: 23.5-31.3) and 53.5% (95% CI: 50.9-56.2) compared to 1.7% and 5.5%, respectively, for pre-pandemic sera, accounting for a serologically determined incidence of influenza A (H1N1) 2009 during the season 2009/2010 of 25,4% (95% CI : 19.3-30.5) in children aged 1-4 years and 48.0% (95% CI: 42.6-52.0) in 5-17 year old children. Of children with HI titers ≥1∶10, 25.5% (95% CI: 22.5-28.8) reported no history of any infectious disease since June 2009. Among vaccinated children, 92% (95%-CI: 87.0-96.6) of the 5-17 year old but only 47.8% (95%-CI: 33.5-66.5) of the 1-4 year old children exhibited HI titers against influenza A virus (H1N1) 2009. Serologically determined incidence of influenza A (H1N1) 2009 infections in children indicates high infection rates with older children (5-17 years) infected twice as often as younger children. In about a quarter of the children with HI titers after the season 2009/2010 subclinical infections must be assumed. Low HI titers in young children after vaccination with the AS03(B)-adjuvanted split virion vaccine need further scrutiny

The multifaceted presentation of chronic recurrent multifocal osteomyelitis: a series of 486 cases from the Eurofever international registry

Objectives: Chronic non-bacterial osteomyelitis (CNO) or chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory disorder characterized by sterile bone osteolytic lesions. The aim of this study was to evaluate the demographic data and clinical, instrumental and therapeutic features at baseline in a large series of CNO/CRMO patients enrolled in the Eurofever registry. Methods: A web-based registry collected retrospective data on patients affected by CRMO/CNO. Both paediatric and adult centres were involved. Results: Complete baseline information on 486 patients was available (176 male, 310 female). The mean age of onset was 9.9 years. Adult onset (>18 years of age) was observed in 31 (6.3%) patients. The mean time from disease onset to final diagnosis was 1 year (range 0-15). MRI was performed at baseline in 426 patients (88%), revealing a mean number of 4.1 lesions. More frequent manifestations not directly related to bone involvement were myalgia (12%), mucocutaneous manifestations (5% acne, 5% palmoplantar pustulosis, 4% psoriasis, 3% papulopustular lesions, 2% urticarial rash) and gastrointestinal symptoms (8%). A total of 361 patients have been treated with NSAIDs, 112 with glucocorticoids, 61 with bisphosphonates, 58 with MTX, 47 with SSZ, 26 with anti-TNF and 4 with anakinra, with a variable response. Conclusion: This is the largest reported case series of CNO patients, showing that the range of associated clinical manifestations is rather heterogeneous. The study confirms that the disease usually presents with an early teenage onset, but it may also occur in adults, even in the absence of mucocutaneous manifestations

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals

The German National Registry of Primary Immunodeficiencies (2012-2017)

Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs. Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel. Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1–25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0–88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE- syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%—subcutaneous; 29%—intravenous; 1%—unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy. Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment

Adapted transfer of distance measures for quantitative structure-activity relationships and data-driven selection of source datasets

Quantitative structure-activity relationships (QSARs) are regression models relating chemical structure to biological activity. Such models allow to make predictions for toxicologically relevant endpoints, which constitute the target outcomes of experiments. The task is often tackled by instance-based methods, which are all based on the notion of chemical (dis-)similarity. Our starting point is the observation by Raymond and Willett that the two families of chemical distance measures, fingerprint-based and maximum common subgraph based measures, provide orthogonal information about chemical similarity. The paper presents a novel method for finding suitable combinations of them, called adapted transfer, which adapts a distance measure learned on another, related dataset to a given dataset. Adapted transfer thus combines distance learning and transfer learning in a novel manner. In our experiments, we visualize the performance of the methods by learning curves and present a quantitative comparison for 10% and 100% of the maximum training set size to show that transfer exploiting source datasets is effective even with small training datasets. Additionally, we present an approach to select the source task in a data-driven manner. The relevant experiments include an example that shows that the selection of a meaningful source task is a critical factor for transfer learning

Adapted transfer of distance measures for quantitative structure-activity relationships

Quantitative structure-activity relationships (QSARs) are regression models relating chemical structure to biological activity. Such models allow to make predictions for toxicologically or pharmacologi- cally relevant endpoints, which constitute the target outcomes of trials or experiments. The task is often tackled by instance-based methods (like k-nearest neighbors), which are all based on the notion of chemical (dis- )similarity. Our starting point is the observation by Raymond and Willett that the two big families of chemical distance measures, fingerprint-based and maximum common subgaph based measures, provide orthogonal in- formation about chemical similarity. The paper presents a novel method for finding suitable combinations of them, called adapted transfer, which adapts a distance measure learned on another, related dataset to a given dataset. Adapted transfer thus combines distance learning and transfer learning in a novel manner. In a set of experiments, we compare adapted transfer with distance learning on the target dataset itself and inductive transfer without adaptations. In our experiments, we visualize the per- formance of the methods by learning curves (i.e., depending on training set size) and present a quantitative comparison for 10% and 100% of the maximum training set size