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Molecular surveillance of carbapenem-resistant gram-negative bacteria in liver transplant candidates
(2021)
Background: Carbapenem-resistant Gram-negative bacteria (CRGN) cause life-threatening infections due to limited antimicrobial treatment options. The occurrence of CRGN is often linked to hospitalization and antimicrobial treatment but remains incompletely understood. CRGN are common in patients with severe illness (e.g., liver transplantation patients). Using whole-genome sequencing (WGS), we aimed to elucidate the evolution of CRGN in this vulnerable cohort and to reconstruct potential transmission routes.
Methods: From 351 patients evaluated for liver transplantation, 18 CRGN isolates (from 17 patients) were analyzed. Using WGS and bioinformatic analysis, genotypes and phylogenetic relationships were explored. Potential epidemiological links were assessed by analysis of patient charts.
Results: Carbapenem-resistant (CR) Klebsiella pneumoniae (n=9) and CR Pseudomonas aeruginosa (n=7) were the predominating pathogens. In silico analysis revealed that 14/18 CRGN did not harbor carbapenemase-coding genes, whereas in 4/18 CRGN, carbapenemases (VIM-1, VIM-2, OXA-232, and OXA-72) were detected. Among all isolates, there was no evidence of plasmid transfer-mediated carbapenem resistance. A close phylogenetic relatedness was found for three K. pneumoniae isolates. Although no epidemiological context was comprehensible for the CRGN isolates, evidence was found that the isolates resulted of a transmission of a carbapenem-susceptible ancestor before individual radiation into CRGN.
Conclusion: The integrative epidemiological study reveals a high diversity of CRGN in liver cirrhosis patients. Mutation of carbapenem-susceptible ancestors appears to be the dominant way of CR acquisition rather than in-hospital transmission of CRGN or carbapenemase-encoding genetic elements. This study underlines the need to avoid transmission of carbapenem-susceptible ancestors in vulnerable patient cohorts.
Objectives: Rising prevalence of multidrug-resistant organisms (MDRO) is a major health problem in patients with liver cirrhosis. The impact of MDRO colonization in liver transplantation (LT) candidates and recipients on mortality has not been determined in detail.
Methods: Patients consecutively evaluated and listed for LT in a tertiary German liver transplant center from 2008 to 2018 underwent screening for MDRO colonization including methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant gram-negative bacteria (MDRGN), and vancomycin-resistant enterococci (VRE). MDRO colonization and infection status were obtained at LT evaluation, planned and unplanned hospitalization, three months upon graft allocation, or at last follow-up on the waiting list.
Results: In total, 351 patients were listed for LT, of whom 164 (47%) underwent LT after a median of 249 (range 0–1662) days. Incidence of MDRO colonization increased during waiting time for LT, and MRDO colonization was associated with increased mortality on the waiting list (HR = 2.57, p<0.0001. One patients was colonized with a carbapenem-resistant strain at listing, 9 patients acquired carbapenem-resistant gram-negative bacteria (CRGN) on the waiting list, and 4 more after LT. In total, 10 of these 14 patients died.
Conclusions: Colonization with MDRO is associated with increased mortality on the waiting list, but not in short-term follow-up after LT. Moreover, colonization with CRGN seems associated with high mortality in liver transplant candidates and recipients.
Survival following relapse in children with Acute Myeloid leukemia: a report from AML-BFM and COG
(2021)
Simple Summary: Acute myeloid leukemia in children remains a difficult disease to cure despite intensive therapies that push the limits of tolerability. Though the intent of initial therapy should be the prevention of relapse, about 30% of all patients experience a relapse. Hence, relapse therapy remains critically important for survival. This retrospective analysis of two large international study groups (COG and BFM) was undertaken to describe the current survival, response rates and clinical features that predict outcomes. We demonstrate that children with relapsed AML may be cured with cytotoxic therapy followed by HSCT. High-risk features at initial diagnosis and early relapse remain prognostic for post-relapse survival. Current response criteria are not aligned with the standards of care for children, nor are the count recovery thresholds meaningful for prognosis in children with relapsed AML. Our data provide a new baseline for future treatment planning and will allow an updated stratification in upcoming studies.
Abstract: Post-relapse therapy remains critical for survival in children with acute myeloid leukemia (AML). We evaluated survival, response and prognostic variables following relapse in independent cooperative group studies conducted by COG and the population-based AML-BFM study group. BFM included 197 patients who relapsed after closure of the last I-BFM relapse trial until 2017, while COG included 852 patients who relapsed on the last Phase 3 trials (AAML0531, AAML1031). Overall survival at 5 years (OS) was 42 ± 4% (BFM) and 35 ± 2% (COG). Initial high-risk features (BFM 32 ± 6%, COG 26 ± 4%) and short time to relapse (BFM 29 ± 4%, COG 25 ± 2%) predicted diminished survival. In the BFM dataset, there was no difference in OS for patients who had a complete remission with full hematopoietic recovery (CR) following post-relapse re-induction compared to those with partial neutrophil and platelet recovery (CRp and CRi) only (52 ± 7% vs. 63 ± 10%, p = 0.39). Among 90 patients alive at last follow-up, 87 had received a post-relapse hematopoietic stem cell transplant (HSCT). OS for patients with post-relapse HSCT was 54 ± 4%. In conclusion, initial high-risk features and early relapse remain prognostic. Response assessment with full hematopoietic recovery following initial relapse therapy does not predict survival. These data indicate the need for post-relapse risk stratification in future studies of relapse therapies.
Simple Summary: Children with acute myeloid leukemia (AML) experience high relapse rates of about 30%; still, survival rates following the first relapse are encouraging. Hence, it is critically important to examine the consequences of a second relapse; however, little is known about this subgroup of patients. This retrospective population-based analysis intends to describe response, survival and prognostic factors relevant for the survival of children with second relapse of AML. Treatment approaches include many different therapeutic regimens, including palliation and intensive treatment with curative intent (63% of the patients). Survival is poor; however, patients who respond to reinduction attempts can be rescued with subsequent hematopoietic stem cell transplantation. We deciphered risk factors, such as short time interval from first to second relapse below one year as being associated with a poor outcome. This analysis will help to improve future international treatment planning and patient care of children with advanced AML.
Abstract: Successful management of relapse is critical to improve outcomes of children with acute myeloid leukemia (AML). We evaluated response, survival and prognostic factors after a second relapse of AML. Among 1222 pediatric patients of the population-based AML-Berlin–Frankfurt–Munster (BFM) study group (2004 until 2017), 73 patients met the quality parameters for inclusion in this study. Central review of source documentation warranted the accuracy of reported data. Treatment approaches included palliation in 17 patients (23%), intensive therapy with curative intent (n = 46, 63%) and other regimens (n = 10). Twenty-five patients (35%) received hematopoietic stem cell transplantation (HSCT), 21 of whom (88%) had a prior HSCT. Survival was poor, with a five-year probability of overall survival (pOS) of 15 ± 4% and 31 ± 9% following HSCT (n = 25). Early second relapse (within one year after first relapse) was associated with dismal outcome (pOS 2 ± 2%, n = 44 vs. 33 ± 9%, n = 29; p < 0.0001). A third complete remission (CR) is required for survival: 31% (n = 14) of patients with intensive treatment achieved a third CR with a pOS of 36 ± 13%, while 28 patients (62%) were non-responders (pOS 7 ± 5%). In conclusion, survival is poor but possible, particularly after a late second relapse and an intensive chemotherapy followed by HSCT. This analysis provides a baseline for future treatment planning.
By analyzing 6.32 fb − 1 of e+ e− annihilation data collected at the center-of-mass energies between 4.178 and 4.226 GeV with the BESIII detector, we determine the branching fraction of the leptonic decay D + s → τ + ντ, with τ+ → π + π0¯ντ, to be B D + s → τ + ν τ = (5.29 ± 0.25 stat ± 0.20 syst) %. We estimate the product of the Cabibbo-Kobayashi-Maskawa matrix element |Vcs|and the D + s decay constant f D + s to be f D + s|Vcs| = (244.8 ± 5.8 stat ± 4.8syst) MeV, using the known values of the τ + and D + s masses as well as the D + s lifetime, together with our branching fraction measurement. Combining the value of |Vcs| obtained from a global fit in the standard model and f D + s from lattice quantum chromodynamics, we obtain f D + s = (251.6 ± 5.9 stat ± 4.9syst) MeV and |Vcs| = 0.980 ± 0.023 stat ± 0.019 syst. Using the branching fraction of B D + s → μ + νμ = (5.35±0.21)×10−3, we obtain the ratio of the branching fractions B D + s → τ + ντ/B D +s → μ+νμ = 9.89±0.71, which is consistent with the standard model prediction of lepton flavor universality.
DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
(2021)
Background: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results: We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions: These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E–P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure (“stressor reactivity,” SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.
We report a measurement of the observed cross sections of e+ e− → J/ψX based on 3.21 fb − 1 of data accumulated at energies from 3.645 to 3.891 GeV with the BESIII detector operated at the BEPCII collider. In analysis of the cross sections, we measured the decay branching fractions of B(ψ(3686) → J/ψX) = (64.4 ± 0.6 ± 1.6)% and B(ψ(3770) → J/ψX) = (0.5 ± 0.2 ± 0.1)% for the first time. The energy-dependent line shape of these cross sections cannot be well described by two Breit-Wigner (BW) amplitudes of the expected decays ψ (3686) → J/ψX and ψ(3770) → J/ψX. Instead, it can be better described with one more BW amplitude of the decay R(3760)→ J/ψX. Under this assumption, we extracted the R (3760) mass M R (3760 ) = 3766.2 ± 3.8 ± 0.4 MeV/c2, total width Γ tot R ( 3760 ) = 22.2 ± 5.9 ± 1.4 MeV, and product of leptonic width and decay branching fraction
ΓeeR(3760) B[R(3760) → J/ψX] = (79.4 ± 85.5 ± 11.7) eV. The significance of the R(3760) is 5.3σ. The first uncertainties of these measured quantities are from fits to the cross sections and second systematic.