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Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Background: Does the dogma of nephron sparing surgery (NSS) still stand for large renal masses? Available studies dealing with that issue are considerably biased often mixing imperative with elective indications for NSS and also including less malignant variants or even benign renal tumors. Here, we analyzed the oncological long-term outcomes of patients undergoing elective NSS or radical tumor nephrectomy (RN) for non-endophytic, large (≥7cm) clear cell renal carcinoma (ccRCC).
Methods: Prospectively acquired, clinical databases from two academic high-volume centers were screened for patients from 1980 to 2010. The query was strictly limited to patients with elective indications. Surgical complications were retrospectively assessed and classified using the Clavien-Dindo-classification system (CDS). Overall survival (OS) and cancer specific survival (CSS) were analyzed using the Kaplan-Meier-method and the log-rank test.
Results: Out of in total 8664 patients in the databases, 123 patients were identified (elective NSS (n = 18) or elective RN (n = 105)) for ≥7cm ccRCC. The median follow-up over all was 102 months (range 3–367 months). Compared to the RN group, the NSS group had a significantly longer median OS (p = 0.014) and median CSS (p = 0.04).
Conclusions: In large renal masses, NSS can be performed safely with acceptable complication rates. In terms of long-term OS and CSS, NSS was at least not inferior to RN. Our findings suggest that NSS should also be performed in patients presenting with renal tumors ≥7cm whenever technically feasible. Limitations include its retrospective nature and the limited availability of data concerning long-term development of renal function in the two groups.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
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.