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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.
The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.
Though immensely successful, the standard model of particle physics does not offer any explanation as to why our Universe contains so much more matter than antimatter. A key to a dynamically generated matter–antimatter asymmetry is the existence of processes that violate the combined charge conjugation and parity (CP) symmetry1. As such, precision tests of CP symmetry may be used to search for physics beyond the standard model. However, hadrons decay through an interplay of strong and weak processes, quantified in terms of relative phases between the amplitudes. Although previous experiments constructed CP observables that depend on both strong and weak phases, we present an approach where sequential two-body decays of entangled multi-strange baryon–antibaryon pairs provide a separation between these phases. Our method, exploiting spin entanglement between the double-strange Ξ− baryon and its antiparticle2 Ξ¯+
, has enabled a direct determination of the weak-phase difference, (ξP − ξS) = (1.2 ± 3.4 ± 0.8) × 10−2 rad. Furthermore, three independent CP observables can be constructed from our measured parameters. The precision in the estimated parameters for a given data sample size is several orders of magnitude greater than achieved with previous methods3. Finally, we provide an independent measurement of the recently debated Λ decay parameter αΛ (refs. 4,5). The ΛΛ¯
asymmetry is in agreement with and compatible in precision to the most precise previous measurement.
Using a sample of (10.09±0.04)×109 J/ψ events collected with the BESIII detector, a partial wave analysis of J/ψ→γη′η′ is performed.The masses and widths of the observed resonances and their branching fractions are reported. The main contribution is from J/ψ→γf0(2020) with f0(2020)→η′η′, which is found with a significance of greater than 25σ. The product branching fraction B(J/ψ → γf0(2020))⋅B(f0(2020) → η′η′ is measured to be (2.63±0.06(stat.) + 0.31−0.46(syst.))×10−4.
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.
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.
Using 10.1 × 109 J/ψ events produced by the Beijing Electron Positron Collider (BEPCII) at a center-of-mass energy √s = 3.097 GeV and collected with the BESIII detector, we present a search for the rare semi-leptonic decay J/ψ → D−e+νe + c.c. No excess of signal above background is observed, and an upper limit on the branching fraction ℬ(J/ψ → D−e+νe + c. c.) < 7.1 × 10−8 is obtained at 90% confidence level. This is an improvement of more than two orders of magnitude over the previous best limit.
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.
Cardiac rehabilitation (CR) is a multidisciplinary intervention including patient assessment and medical actions to promote stabilization, management of cardiovascular risk factors, vocational support, psychosocial management, physical activity counselling, and prescription of exercise training. Millions of people with cardiac implantable electronic devices live in Europe and their numbers are progressively increasing, therefore, large subsets of patients admitted in CR facilities have a cardiac implantable electronic device. Patients who are cardiac implantable electronic devices recipients are considered eligible for a CR programme. This is not only related to the underlying heart disease but also to specific issues, such as psychological adaptation to living with an implanted device and, in implantable cardioverter-defibrillator patients, the risk of arrhythmia, syncope, and sudden cardiac death. Therefore, these patients should receive special attention, as their needs may differ from other patients participating in CR. As evidence from studies of CR in patients with cardiac implantable electronic devices is sparse, detailed clinical practice guidelines are lacking. Here, we aim to provide practical recommendations for CR in cardiac implantable electronic devices recipients in order to increase CR implementation, efficacy, and safety in this subset of patients.