Outbreak investigation including molecular characterization of community associated methicillin-resistant Staphylococcus aureus in a primary and secondary school in Eastern Switzerland

At our tertiary children's hospital, infections with newly detected methicillin-resistant Staphylococcus aureus (MRSA) among children attending primary (age 6-12 years) and secondary school (age 13-16 years) nearly doubled in 2018 compared to previous years. This observation initiated an epidemiological outbreak investigation including phenotypic (susceptibility testing) and genotypic (whole genome sequencing) characterization of the isolates. In addition, a cross-sectional study was conducted to determine source of the outbreak, colonization frequency and to identify risk factors for transmission using a questionnaire. As a result, 49 individuals were detected with 57 corresponding isolates. Based on the case definition combined with whole genome sequencing, a core cluster was identified that shared common genetic features and a similar antimicrobial susceptibility pattern (efflux-mediated macrolide resistance, tetracycline susceptibility along with presence of Panton-Valentine leukocidin). Epidemiologic evaluation identified a distinct school as a common risk factor. However, the source of the clustered infections within that school could not be further specified. No further cases could be detected after decolonization of infected and colonized children

Presence of centromeric but absence of telomeric group B KIR haplotypes in stem cell donors improve leukaemia control after HSCT for childhood ALL

Although allogeneic hematopoietic stem-cell transplantation (HSCT) provides high cure rates for children with high-risk acute lymphoblastic leukaemia (ALL), relapses remain the main cause of treatment failure. Whereas donor killer cell immunoglobulin-like receptor (KIR) genotype was shown to impact on relapse incidence in adult myeloid leukaemia similar studies in paediatric ALL are largely missing. Effect of donor KIR genotype on transplant outcome was evaluated in 317 children receiving a first myeloablative HSCT from an HLA-matched unrelated donor or sibling within the prospective ALL-SCT-BFM-2003 trial. Analysis of donor KIR gene polymorphism revealed that centromeric presence and telomeric absence of KIR B haplotypes was associated with reduced relapse risk. A centromeric/telomeric KIR score (ct-KIR score) integrating these observations correlated with relapse risk (hazard ratio (HR) 0.58; P = 0.002) while it had no impact on graft-versus-host disease or non-relapse mortality. In multivariable analyses ct-KIR score was associated with reduced relapse risk (HR 0.58; P = 0.003) and a trend towards improved event-free survival (HR 0.76; P = 0.059). This effect proved independent of MRD level prior to HSCT. Our data suggest that in children with ALL undergoing HSCT after myeloablative conditioning, donor selection based on KIR genotyping holds promise to improve clinical outcome by decreasing relapse risk and prolonged event-free survival

CD16xCD33 Bispecific Killer Cell Engager (BiKE) as potential immunotherapeutic in pediatric patients with AML and biphenotypic ALL

Similar to pediatric acute myeloid leukemia (AML) the subgroup of biphenotypic acute lymphoblastic leukemia (ALL) is a rare complex entity with adverse outcome, characterized by the surface expression of CD33. Despite novel and promising anti-CD19 targeted immunotherapies such as chimeric antigen receptor T cells and bispecific anti-CD19/CD3 antibodies, relapse and resistance remain a major challenge in about 30% to 60% of patients. To investigate the potential role of the fully humanized bispecific antibody CD16 × CD33 (BiKE) in children with CD3

HLA-E expression constitutes a novel determinant for ALL disease monitoring following hematopoietic stem cell transplantation

Design!#!Prospective diagnostic study.!##!Objectives!#!Primary imaging-based diagnosis of spinal cord tumor-suspected lesions is often challenging. The identification of the definite entity is crucial for dedicated treatment and therefore reduction of morbidity. The aim of this trial was to investigate specific quantitative signal patterns to differentiate unclear intramedullary tumor-suspected lesions based on diffusion tensor imaging (DTI).!##!Setting!#!Medical Center - University of Freiburg, Germany.!##!Methods!#!Forty patients with an unclear tumor-suspected lesion of the spinal cord prospectively underwent DTI. Primary diagnosis was determined by histological or clinical work-up or remained indeterminate with follow-up. DTI metrics (FA/ADC) were evaluated at the central lesion area, lesion margin, edema, and normal spinal cord and compared between different diagnostic groups (ependymomas, other spinal cord tumors, inflammations).!##!Results!#!Mean DTI metrics for all spinal cord tumors (n = 18) showed significantly reduced FA and increased ADC values compared to inflammatory lesions (n = 8) at the lesion margin (p < 0.001, p = 0.001) and reduced FA at the central lesion area (p < 0.001). There were no significant differences comparing the neoplastic subgroups of ependymomas (n = 10) and other spinal cord tumors (n = 8), but remaining differences for both compared to the inflammation subgroup. We found significant higher ADC (p = 0.040) and a trend to decreased FA (p = 0.081) for ependymomas compared to inflammations at the edema.!##!Conclusion!#!Even if distinct differentiation of ependymomas from other spinal cord neoplasms was not possible based on quantitative DTI metrics, FA and ADC were feasible to separate inflammatory lesions. This may avoid unnecessary surgery in patients with unclear intramedullary tumor-suspected lesions

HLA-Bw4 80(T) and multiple HLA-Bw4 copies combined with KIR3DL1 associate with spontaneous clearance of HCV infection in people who inject drugs

BACKGROUND AND AIMS: NK cell function is regulated by inhibitory and activating receptors including killer-cell immunoglobulin-like receptors (KIRs). Here, we analyzed the impact of different KIR/KIR-ligand genotypes on the outcome of HCV infection in people who inject drugs (PWID). KIR/KIR-ligand genotypes associated with spontaneous clearance of HCV infection were identified in a cohort of PWID from Germany (n=266) and further validated in a second anti-HCV positive cohort of PWID recruited in North America (n=342). Moreover, NK cells of PWID and healthy donors were functionally characterized according to their KIR/KIR-ligand genotype by flow cytometry.RESULTS: Multivariate logistic regression analysis revealed that KIR3DL1/HLA-Bw4 80(T) was associated with spontaneous clearance of HCV infection in PWID, which was confirmed in the PWID cohort from North America. Moreover, compared with PWID with detectable HCV-RNA the frequency of individuals with multiple HLA-Bw4 alleles was significantly higher in anti-HCV positive PWID with resolved HCV infection (29.7% vs. 15.2%; p=0.0229) and in anti-HCV seronegative PWID (39.2%; p=0.0006). KIR3DL1(+) NK cells from HLA-Bw4 80(T)-positive PWID showed superior functionality compared to HLA-Bw4 80(I)-positive PWID. This differential impact was not observed in healthy donors; however the HLA-Bw4 copy number strongly correlated with the functionality of KIR3DL1(+) NK cells.CONCLUSIONS: HLA-Bw4-80(T) and multiple HLA-Bw4 copies in combination with KIR3DL1 are associated with protection against chronic hepatitis C in PWID by distinct mechanisms. Better licensing of KIR3DL1(+) NK cells in the presence of multiple HLA-Bw4 copies is beneficial prior to seroconversion whereas HLA-Bw4 80(T) may be beneficial during acute hepatitis C. Lay summary NK cells are part of the innate immune system and are regulated by a complex network of activating and inhibiting receptors. The regulating receptor-ligand pairs of an individual are genetically determined. Here, we identified a particular set of ligand and receptor genes that associated with better functionality of NK cells and better outcome upon exposure to HCV in a high risk group.</p

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.</jats:p