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1.
Angew Chem Int Ed Engl ; 61(42): e202209885, 2022 Oct 17.
Artículo en Inglés | MEDLINE | ID: mdl-35924716

RESUMEN

Deep cavitands, concave molecular containers, represent an important supramolecular host class that has been explored for a variety of applications ranging from sensing, switching, purification and adsorption to catalysis. A major limitation in the field has been the cavitand volume that is restricted by the size of the structural platform utilized (diameter approx. 7 Å). We here report the synthesis of a novel, unprecedentedly large structural platform, named acridane[4]arene (diameter approx. 14 Å), suitable for the construction of cavitands with volumes of up to 814 Å3 . These megalo-cavitands serve as size-selective hosts for fullerenes with mM to sub-µM binding affinity for C60 and C70 . Furthermore, the selective binding of fullerene C70 in the presence of C60 was demonstrated.


Asunto(s)
Fulerenos , Éteres Cíclicos , Fulerenos/química , Resorcinoles
2.
Neuroimage Clin ; 35: 103090, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35752061

RESUMEN

BACKGROUND: Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting. METHODS: This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation). RESULTS: FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%). CONCLUSIONS: The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.


Asunto(s)
Encéfalo , Trastornos de Conversión , Imagen por Resonancia Magnética , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Estudios de Casos y Controles , Trastornos de Conversión/diagnóstico por imagen , Trastornos de Conversión/fisiopatología , Humanos , Imagen por Resonancia Magnética/métodos , Reproducibilidad de los Resultados
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