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Residual SYNTAX score and outcomes after TAVR+PCI versus SAVR+CABG
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Aortic stenosis (AS) and coronary artery disease (CAD) frequently coexist, requiring careful revascularisation strategy consideration. While surgical aortic valve replacement (SAVR) plus coronary artery bypass grafting (CABG) is traditional, transcatheter aortic valve replacement (TAVR) plus percutaneous coronary intervention (PCI) is increasingly used. The optimal strategy, particularly regarding residual CAD burden, remains unclear.
This study investigated the impact of residual SYNTAX (Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery) score (rSS) on outcomes in men and women with AS and CAD undergoing TAVR+PCI versus SAVR+CABG.
In this retrospective study, propensity score-matched cohorts of men and women undergoing either procedure were analysed. Matching variables included age, left ventricular ejection fraction, EuroSCORE II (European System for Cardiac Operative Risk Evaluation II) and CAD severity.
398 patients (114 women and 284 men) were included. The rSS was predictive of the primary composite endpoint in the TAVR+PCI group (p=0.006 women and p<0.001 men) but not in the SAVR+CABG group. In patients achieving an rSS<8, TAVR+PCI was associated with a lower combined endpoint rate compared with SAVR+CABG, consistent across genders (p=0.02). Furthermore, TAVR+PCI demonstrated significant safety benefits, including lower rates of major bleeding in men (2.1% vs 10.6%) and stroke in women (1.8% vs 12.3%).
The prognostic importance of the rSS is strategy-dependent. For patients undergoing TAVR+PCI, achieving extensive revascularisation (rSS <8) is a critical procedural goal associated with improved outcomes. For patients undergoing SAVR+CABG, prognosis appears driven more by baseline clinical risk
Replacement of a single residue in an antibody abolishes cognate antigen binding, as predicted by theoretical methods
No full textStructural insights into the interaction between antibodies and antigens at the atomic level are pivotal for understanding the molecular mechanisms of antigen binding. Despite the availability of structural models generated by recent artificial intelligence advancements, computational predictions require experimental validation to confirm their accuracy. Here, we demonstrate an approach that combines computational protein modeling with spectroscopic experiments to validate antibody-antigen interactions. As a case example we use solanezumab, a monoclonal antibody that targets amyloid-beta (A), whose misfolding is the main factor responsible for Alzheimer's disease. For this antibody, we predicted a single mutation, , within the paratope of the heavy chain to disrupt antigen binding. This mutation, referred to as a "dead mutant", was experimentally validated using an immuno-infrared biosensor (iRS). Our results confirmed that the mutation abolished antigen binding without affecting the native structure of the antibody. The use of dead mutants enables precise differentiation between specific and nonspecific binding, which is particularly important in medical diagnostics. We applied this approach to analyze the binding of solanezumab to synthetically produced A variants and A catched by the iRS functionalized surface from cerebrospinal fluid, showcasing its utility in Alzheimer’s disease diagnostics. These findings highlight the value of computational modeling and experimental validation in understanding antigen-antibody interactions, with significant implications for diagnostic and therapeutic applications
High-peak-power 2.1 m femtosecond holmium amplifier at 100 kHz
No full textHigh-power ultrafast laser sources in the short-wave infrared region are of great interest for numerous applications, including secondary sources of radiation and processing of materials commonly opaque in the near-infrared region. In this wavelength region, direct laser amplification around 2.1 m wavelength within the atmospheric transparency window is particularly attractive for realizing compact and efficient high-power lasers. However, this wavelength region was widely underrepresented in femtosecond laser technology so far. Here, we report on a 2.1 m laser system delivering 97 fs pulses with an unprecedented combination of high peak power of 525 MW and high repetition rate of 100 kHz. The amplifier system consists of a mode-locked oscillator seeding a regenerative amplifier (RA) using the broadband material holmium (Ho)-doped (CALGO), operating in the chirped pulse amplification (CPA) scheme and a nonlinear compression stage based on a Herriott-type multi-pass cell (MPC) with bulk material. We demonstrate the potential of this laser system by generating a microplasma in ambient air, demonstrating its high intensity for future plasma-driven secondary sources. This system bridges the gap between conventional 1-to-10 kHz amplifiers and high-power MHz laser oscillators in this attractive wavelength range
The complexity of problem-solving human social systems
No full textResearch in the social sciences often describes social complexity through a combination of structure, organization, and behavior within human social systems. In this paper, I argue that these aspects, while important, are conceptually distinct. Specifically, I distinguish between structural complexity — the organizational properties of a system — and dynamic complexity — the patterns of behavior and interaction within the system. To illustrate this distinction, I present three agent-based models of collective problem-solving: a hierarchical model, a random network model, and a hybrid of the two. These models are used to demonstrate how different forms of complexity can be measured and how they affect system performance. Several metrics are proposed to quantify structural and dynamic complexity, and model simulations show that the structurally complex hierarchical model is more efficient at solving problems than the dynamically complex network model. The simulations confirm the widespread intuition that systems with high structural complexity are effective for solving known problems, while systems with high dynamic complexity are more flexible. However, I also show that the hierarchical model is less robust against error than the network model. Finally, the proposed metrics provide a foundation for rigorous empirical research on the complexities of human social systems
Task-relevant representational spaces in human memory traces
No full textDuring encoding, stimuli are embedded into memory traces that allow for their later retrieval. However, we cannot remember every aspect of our experiences. Here, we show that memory traces consist of multidimensional representational spaces whose formats are flexibly strengthened or weakened during encoding and consolidation. In a series of behavioral experiments, participants compared pairs of natural images on either two conceptual or two perceptual dimensions, leading them to incorporate the images into representational ‘spaces’. We found that representations from deep neural networks relate to both behavioral similarity and memory confidence judgements. Furthermore, we found that distances in task-relevant but not irrelevant spaces affected memory strengths. Interestingly, conceptual encoding did not impair subsequent rejection of similar lures, suggesting that task-irrelevant perceptual information remained in the memory trace. However, targeted memory reactivation following conceptual encoding deteriorated perceptual discrimination, indicating that it weakened the accessibility of perceptual formats. Our results demonstrate that representational formats are flexibly incorporated into memory, and more generally show how the organization of information in cognitive representational spaces shapes behavior
Unveiling surface species formed on Ni-Fe spinel oxides during the oxygen evolution reaction at the atomic scale
No full textOptimizing electrocatalyst performance requires an atomic-scale understanding of surface state changes and how those changes affect activity and stability during the reaction. This is particularly important for the oxygen evolution reaction (OER) since the electrocatalytically active surfaces undergo substantial reconstruction and transformation. Herein, a multimodal method is employed that combines X-ray photoemission spectroscopy, transmission electron microscopy, atom probe tomography, operando surface-enhanced Raman spectroscopy with electrochemical measurements to examine the surface species formed on NiFeO, P-doped NiFeO and NiFeO upon OER cycling. The activated NiFeO and P-doped NiFeO exhibit a significantly lower Tafel slope (40 mV dec) than NiFeO (90 mV dec), although oxyhydroxides are grown on all three Ni-Fe spinels during OER. This is likely attributed to the formation of a 1 nm highly defective layer with a higher oxygen concentration on the activated NiFeO and P-doped NiFeO nanoparticle surfaces (than that in bulk), which improves the charge transfer kinetics toward OER. Such surface species are not formed on NiFeO. Overall, this study provides a mechanistic understanding of the role of Fe, P, and Ni in forming active oxygen species in the Ni-based spinels toward OER
A composite score of serum cytokines enables early identification of patients at high risk for irAEs under immune checkpoint inhibition
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Immune checkpoint inhibitors (ICI) have revolutionized the treatment of advanced skin cancers. However, potentially severe and irreversible immune-related adverse events (irAEs) represent a major clinical challenge. Identifying reliable predictive biomarkers for irAEs remains critical to balancing efficacy and toxicity.
In this prospective cohort of 131 skin cancer patients receiving ICI we analyzed the expression of 57 cytokines in baseline and longitudinal serum samples and tested their predictive value regarding the occurrence of irAEs.
We observed distinct cytokine expression profiles in patients who developed irAEs compared to those who did not, with variations reflecting the affected organ systems, particularly liver and thyroid toxicity. Elevated levels of IL-1RA and CXCL-13 and downregulated levels of IL-7 after the first ICI application significantly correlated with irAEs occurrence. To improve predictive accuracy, we developed a composite cytokine risk score integrating these cytokines, which independently predicted irAE in both univariable and multivariable models. ROC analysis of the cytokine risk score yielded an AUC of 0.710. Time-dependent Cox regression confirmed the cytokine risk score as an independent predictor of irAEs (univariable HR = 1.801 [95%CI=1.424–2.277; p<0.001]; multivariable HR = 1.407 [95%CI=1.072–1.846; p=0.014]). After stratifying patients into low- and high-risk groups, the high-risk patients had a significantly increased hazard of experiencing irAEs.
IL-7, IL-1RA, and CXCL-13 represent potential biomarkers for irAEs risk stratification
Non-contrast photon counting computed tomography of the head
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Photon counting CT (PCCT) is a promising technique for neuroradiological CT examinations. In initial studies on non-contrast PCCT of the head (NCCT), however, artifacts close to the calvarium were noticed, which lead to an inhomogeneous representation of the brain tissue. In this study, a new software for image reconstruction to reduce artifacts is evaluated.
In the new CT software developed by the manufacturer, off-focal radiation was remodeled and is mathematically corrected in the NCCT in data processing during image formation. For the evaluation, 60 patients with an NCCT in the currently used software and 44 patients in the new software were included retrospectively. A detailed quantitative analysis using multiple regions of interest and a qualitative analysis with a reading by experienced radiologists was performed to evaluate image quality and tissue homogeneity below the calvarium.
The new software reduced the inhomogeneity of the cortical brain tissue near the calvarium. As a quantitative measure, there is a clear reduction of the signal difference of the gray and white matter at different distances from the calvarium ( < 0.001). In the qualitative analysis, the inhomogeneity of the brain tissue was reduced, and the gray-white differentiation improved ( < 0.001) in the clinically used virtual monoenergetic image at 65 keV.
Optimized modelling and mathematical correction of the off-focal radiation in the new software led to an effective reduction of the inhomogeneity of the cortical brain tissue and thus improved image quality
Children's eating attitudes test (ChEAT)
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The objective of this study is to assess the validity of the German version of the Children's Eating Attitudes Test (ChEAT), an internationally used tool for the detection of eating disorder (ED) symptoms, in a clinical sample.
The ChEAT self-report questionnaire, comprising 26 items, was employed to examine eating behaviors of a clinical sample of 342 adolescents (aged 12–18 years) undergoing inpatient treatment at a child and adolescent psychiatric clinic in Germany. The ChEAT was validated through an exploratory factor analysis (EFA), followed by a confirmatory factor analysis (CFA) and an examination of internal consistency. Subsequent analyses were conducted to identify differences associated with participant characteristics, including age, gender, body mass index (BMI), and diagnosis. Furthermore, additional eating behaviors, depression, and anxiety symptoms were documented via supplementary questionnaires and correlated to the ChEAT to evaluate the convergent and discriminant validity.
The factorial validity of the ChEAT was confirmed through EFA and CFA, resulting in a five-factor structure with the following dimensions: 'Body and Weight Concern,' 'Dieting,' 'Social Pressure,' 'Purging and Binge Eating,' and 'Food Preoccupation'. The 24-item model showed high internal consistency and demonstrated an acceptable fit to the data. Convergent and discriminant validity of the ChEAT was supported by significant correlations with other self-report questionnaires. Higher ChEAT average scores were observed in females and those with a history of eating or depressive disorders, whereas age or BMI showed no correlation.
The data demonstrate that the German version of the ChEAT appears to be a reliable and valid instrument for identifying ED symptoms in clinical samples. However, further studies are necessary to evaluate the factor structure and validity
Common-gain autoencoder network for binaural speech enhancement
No full textBinaural processing is becoming an important feature of high-end commercial headsets and hearing aids. Speech enhancement with binaural output requires adequate treatment of spatial cues in addition to desirable noise reduction and simultaneous speech preservation. Binaural speech enhancement was traditionally approached with model-based statistical signal processing, where the principle of common-gain filtering with identical treatment of left- and right-ear signals has been designed to achieve enhancement constrained by strict binaural cue preservation. However, model-based approaches may also be instructive for the design of modern deep learning architectures. In this article, the common-gain paradigm is therefore embedded into an artificial neural network approach. In order to maintain the desired common-gain property end-to-end, we derive the requirements for compressed feature formation and data normalization. Binaural experiments with moderate-sized artificial neural networks demonstrate the superiority of the proposed common-gain autoencoder network over model-based processing and related unconstrained network architectures for anechoic and reverberant noisy speech in terms of segmental SNR, binaural perception-based metrics MBSTOI, better-ear HASQI, and a listening experiment