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Microstructural differentiation of cerebral metastases, glioblastoma, meningioma, and primary CNS lymphoma using advanced diffusion imaging techniques
No full textBackgroundMicrostructural tumor characteristics discriminate metastases, glioblastoma, meningioma, and primary CNS lymphoma. We aimed to assess these intracranial neoplasms utilizing multiparametric diffusion imaging as a translational measure of morphology.MethodsWe investigated 101 newly diagnosed intracranial tumors (35 metastases, 34 glioblastomas [GB], 21 meningiomas, 11 primary CNS lymphomas [PCNSL]) with advanced diffusion MRI including Diffusion Tensor Imaging (DTI), Neurite Orientation and Dispersion Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI). Beyond DTI-derived metrics (aD, fractional anisotropy [FA], mD, rD), we extracted the NODDI and DMI intra-axonal (NODDI intra-cellular volume fraction, DMI V-intra), extra-axonal cellular (DMI V-extra), and free water (NODDI ISO-VF, DMI V-CSF) fractions using a multi-compartment model. These metrics were read from contrast-enhancing tumor portions and compared across the entities.ResultsVarious microstructural parameters served as effective discriminators in pairwise comparisons: ISO-VF demonstrated high accuracy in distinguishing metastases from PCNSL (accuracy 90.13%) and meningiomas (accuracy 80.69%). aD was most accurate in discriminating GB from PCNSL (accuracy 89.57%) and meningioma from PCNSL (accuracy 74.03%), similar to MD which distinguished GB from meningiomas (accuracy 77.73%). FA performed best in discriminating GB from metastases (accuracy 83.11%). Discrimination on two axes of directionality and compartmentalization illustrate the comprehensive approach to tumor assessment.Conclusion Advanced microstructural imaging facilitates discrimination of four common intracranial neoplasms. Features such as cell density, extent of free water, and directional cellular elements are reflected in the diffusion metrics to varying degrees. As part of a first non-invasive assessment, they may direct early diagnostic and therapeutic procedures
Neuroimaging in advanced Parkinson’s disease: insights into pathophysiology, biomarkers, and personalized therapies
No full textAdvanced Parkinson’s disease (APD) represents a late stage of Parkinson’s disease and is characterized by complex motor and non-motor symptoms that are less responsive to oral dopaminergic therapies. While APD has a relevant impact on patients’ quality of life and requires intensified treatment, consistent diagnostic criteria have only recently been proposed. The precise pathophysiology underlying the symptoms of APD remains poorly understood, making early prognostication and intervention difficult. Neuroimaging has emerged as a promising tool for elucidating the mechanisms driving APD, identifying biomarkers for disease staging, and predicting therapeutic response. Techniques such as molecular imaging and magnetic resonance imaging provide insight into molecular and structural changes associated with the progression of PD, including protein aggregation, neuroinflammation, and regional neurodegeneration. While positron emission tomography imaging of alpha-synuclein and other pathologies offers avenues for staging and differential diagnosis, advanced magnetic resonance imaging approaches have the potential for capturing subtle microstructural changes i.e. through neuromelanin-sensitive or diffusion-weighted imaging. However, the majority of imaging studies has focused on early Parkinson’s disease, leaving their applicability to APD uncertain. Future research should prioritize the validation of neuroimaging findings in well-defined APD cohorts and extend their use to predict clinical milestones such as motor fluctuations, dyskinesia, and cognitive decline. These efforts are essential to advance personalized therapeutic strategies and bridge the gap between research and clinical management of APD
On structural and practical identifiability: current status and update of results
No full textIdentifiability of parameters in dynamical systems is a fundamental concept of mathematical modelling in systems biology and systems medicine. Both the structurally inherent identifiability of parameters in models and the practical identifiability of parameters, which arises from insufficient available data, play crucial roles in the development of useful models.Here, we provide an overview of recent developments in the field of structural identifiability analysis of models based on ordinary differential equations, emphasising its importance for accurate parameter estimation. We extend an existing benchmark study by comparing the methods for structural identifiability analysis with the recently developed StrucID, showing it to be a fast, efficient and intuitive algorithm. Furthermore, this review highlights the challenges in practical identifiability analysis and the need for benchmarking with real-world models using experimental data. The potential benefits of standardising documentation for benchmarking models with experimental data and practical non-identifiabilities are stressed
Special issue on brain-computer interfaces: highlighting research from the 10th International Brain-Computer Interface Meeting
No full textPhase behavior and pathway-selective oligomerization driven by amino acid side-chain recognition
No full textWithin living systems, DNA-encoded information is translated into proteins through a precise process involving amino acid activation, recognition, and biocatalytic acyl transfer reactions. This process raises a fundamental question: what essential ingredients are required for amino acid side-chain recognition and as- sembly in the absence of enzymatic machinery? In this study, we demonstrate abiotic acyl transfer reactions from aminoacyl phosphate esters, synthetic analogs of biological aminoacyl adenylates, to amino esters, which serve as mimics of tRNA esters. The coupling of amino acid oligomerization to acyl transfer reaction cycles drives selective assembly of amino acid derivatives, recognizing aromatic side chains. Liquid-liquid phase separation creates selective microenvironments that enhance specificity and direct oligomerization. Notably, droplets formed by aromatic amino acids maintain specificity even in the presence of competing species. These findings suggest that amino acid-based compartments sustain chemical processes without biological templates, offering a minimal model of compartmentalization from simple activated monomers
Queere Geschichte (n) : Erinnerungen und Visionen im Anschluss an Leslie Feinbergs »Stone Butch Blues«
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Climate-related and other uncertainties in IFRS financial reporting: an empirical analysis of ED/2024/6
No full textEntwicklung eines Risikostratifizierungsmodells zur Sterblichkeit nach kardiochirurgischen Eingriffen bei Kindern mit kongenitalen Herzfehlern
No full textKongenitale Herzfehler stellen die häufigste angeborene Organfehlbildung beim Neugeborenen dar. Kinderkardiochirurgische Eingriffe bei angeborenen Herzfehlern sind trotz großer Fortschritte vor allem bei Säuglingen noch immer mit einer relevanten Letalität assoziiert. Die Etablierung von Risikostratifizierungsmodellen trägt zu einer Verbesserung der Versorgungsqualität und einer Optimierung des klinischen Managements bei. In der jüngeren Vergangenheit wurden mit der Einführung von Klassifikationen wie dem RACHS 1-, ABC- und STAT-Kategorie erste Meilensteine hinsichtlich einer besseren Risiko stratifizierung gelegt. Ziel dieser Arbeit war die Entwicklung eines durch die Hinzunahme von Patientenfaktoren verbesserten Risikostratifizierungsmodells zum frühen Abschätzen der 30-Tagesletalität nach KHC-Eingriffen. Hierbei sollten die eingehenden Parameter in der klinischen Routine leicht und schnell verfügbar sein. Ausgehend von den vier Variablen Alter, STAT-Kategorie, Aortenabklemmzeit und Laktatkonzentration wurde mittels maschinellen Lernens und interner Kreuzvalidierung ein Modell zur Einschätzung der Überlebenswahrscheinlichkeit 24 Stunden postoperativ entwickelt. Die untersuchte Kohorte umfasste 585 Eingriffe, die zwischen 01/2014 und 12/2019 am Universitätsklinikum Freiburg – Universitäts Herzzentrum durchgeführt wurden. Das entstandene Freiburger Risikostratifizierungsmodell zeigte eine sehr gute Diskrimination (AUC 0,9) und eine Verbesserung der Vorhersagekraft im Vergleich zur reinen STAT Kategorie (AUC 0,77). Bei einem Schwellenwert von 0,4 ergab sich eine Sensitivität von 90,6 % und eine Spezifität von 76,5 %, was zu 3 falsch-negativen und 130 falsch-positiven Ergebnissen führte. Das im Rahmen der Promotionsarbeit entwickelte Risikostratifizierungsmodell wurde bereits in einer zweiten Kohorte extern validiert. Auch in diesem Zusammenhang zeigte sich eine sehr gute Performanz des Modells, sodass dieses aktuell in die klinische Routine implementiert wird
Simulated infrared and raman spectra of phosphorus allotropes
No full textElemental phosphorus is both an intriguing and challenging case in solid state chemistry. The variety of existing allotropes, with different chemistry and properties, makes this long-time studied element still of high intertest today. In this work, we have systematically investigated the vibrational properties of , white , fibrous red, violet, and black phosphorus allotropes by simulating their infrared (IR) and Raman spectra at the density functional theory (DFT) level. The latter are provided as powder and directionally resolved (oriented single crystal) spectra. As all results are obtained with the same method and computational setup, our work provides a data set that is consistent across the different polimorphs, hence minimizing discrepancies arising from methodological differences. Our results are in general good agreement with available experimental and theoretical spectra from literature in both the Raman and IR cases. At the same time, some of our spectra are meant to fill gaps in the existing literature