National laboratory-based surveillance system for antimicrobial resistance: a successful tool to support the control of antimicrobial resistance in the Netherlands

An important cornerstone in the control of antimicrobial resistance (AMR) is a well-designed quantitative system for the surveillance of spread and temporal trends in AMR. Since 2008, the Dutch national AMR surveillance system, based on routine data from medical microbiological laboratories (MMLs), has developed into a successful tool to support the control of AMR in the Netherlands. It provides background information for policy making in public health and healthcare services, supports development of empirical antibiotic therapy guidelines and facilitates in-depth research. In addition, participation of the MMLs in the national AMR surveillance network has contributed to sharing of knowledge and quality improvement. A future improvement will be the implementation of a new semantic standard together with standardised data transfer, which will reduce errors in data handling and enable a more real-time surveillance. Furthermore, the

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

Didactiek van het techniekonderwijs tekenend techniek!: de opbrengsten van zes jaar lectoraatsonderzoek naar didactiek van het techniekonderwijs

Het lectoraat ‘Didactiek van het Techniekonderwijs’ bij de Pedagogisch Technische Hogeschool (PTH) in Eindhoven heeft diverse projecten en onderzoeken uitgevoerd. Leden van de kenniskring hebben onderzoek gedaan naar de onderwijspraktijk binnen en buiten de PTH. Daarnaast heeft het lectoraat frequent gewerkt met collega’s van andere opleidingen en docenten van het vmbo, mbo en hbo. Niet alleen leidde dit onderzoek tot inzicht op het terrein van het opleiden voor technisch beroepsonderwijs, het was tevens een vorm van professionalisering waardoor we als team stappen hebben gezet in de kwaliteit van onderzoek en vakdidactische ontwikkeling voor de technische lerarenopleiding

Molecular Discrimination of Atypical Bovine Spongiform Encephalopathy Strains from a Geographical Region Spanning a Wide Area in Europe† ▿

Transmissible spongiform encephalopathy strains can be differentiated by their behavior in bioassays and by molecular analyses of the disease-associated prion protein (PrP) in a posttranslationally transformed conformation (PrPSc). Until recently, isolates from cases of bovine spongiform encephalopathy (BSE) appeared to be very homogeneous. However, a limited number of atypical BSE isolates have recently been identified upon analyses of the disease-associated proteinase K (PK) resistance-associated moiety of PrPSc (PrPres), suggesting the existence of at least two additional BSE PrPres variants. These are defined here as the H type and the L type, according to the higher and lower positions of the nonglycosylated PrPres band in Western blots, respectively, compared to the position of the band in classical BSE (C-type) isolates. These molecular PrPres variants, which originated from six different European countries, were investigated together. In addition to the migration properties and glycosylation profiles (glycoprofiles), the H- and L-type isolates exhibited enhanced PK sensitivities at pH 8 compared to those of the C-type isolates. Moreover, H-type BSE isolates exhibited differences in the binding of antibodies specific for N- and more C-terminal PrP regions and principally contained two aglycosylated PrPres moieties which can both be glycosylated and which is thus indicative of the existence of two PrPres populations or intermediate cleavage sites. These properties appear to be consistent within each BSE type and independent of the geographical origin, suggesting the existence of different BSE strains in cattle. The choice of three antibodies and the application of two pHs during the digestion of brain homogenates provide practical and diverse tools for the discriminative detection of these three molecular BSE types and might assist with the recognition of other variants

National laboratory-based surveillance system for antimicrobial resistance:a successful tool to support the control of antimicrobial resistance in the Netherlands

An important cornerstone in the control of antimicrobial resistance (AMR) is a well-designed quantitative system for the surveillance of spread and temporal trends in AMR. Since 2008, the Dutch national AMR surveillance system, based on routine data from medical microbiological laboratories (MMLs), has developed into a successful tool to support the control of AMR in the Netherlands. It provides background information for policy making in public health and healthcare services, supports development of empirical antibiotic therapy guidelines and facilitates in-depth research. In addition, participation of the MMLs in the national AMR surveillance network has contributed to sharing of knowledge and quality improvement. A future improvement will be the implementation of a new semantic standard together with standardised data transfer, which will reduce errors in data handling and enable a more real-time surveillance. Furthermore, the scientific impact and the possibility of detecting outbreaks may be amplified by merging the AMR surveillance database with databases from selected pathogen-based surveillance programmes containing patient data and genotypic typing data.</p

NF135.C10: a new Plasmodium falciparum clone for controlled human malaria infections

Item does not contain fulltextWe established a new field clone of Plasmodium falciparum for use in controlled human malaria infections and vaccine studies to complement the current small portfolio of P. falciparum strains, primarily based on NF54. The Cambodian clone NF135.C10 consistently produced gametocytes and generated substantial numbers of sporozoites in Anopheles mosquitoes and diverged from NF54 parasites by genetic markers. In a controlled human malaria infection trial, 3 of 5 volunteers challenged by mosquitoes infected with NF135.C10 and 4 of 5 challenged with NF54 developed parasitemia as detected with microscopy. The 2 strains induced similar clinical signs and symptoms as well as cellular immunological responses. CLINICAL TRIALS REGISTRATION: NCT01002833

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd