Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 21% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 130 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (188%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (502%) of 11 476 included individuals were female and 5720 (498%) were male. Sex data were missing for 372 (31%) of 11 848 individuals. Median age at registry entry was 96 years (IQR 58-132). 10 099 (899%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (101%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (52%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [924%] of 10 202) than in children and adolescents from non-high-income countries (199 [480%] of 415). 3414 (316%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (724%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 500 mmol/L (IQR 405-608). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation.Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Shearlet-based regularization in sparse dynamic tomography

Classical tomographic imaging is soundly understood and widely employed in medicine, nondestructive testing and security applications. However, it still o↵ers many challenges when it comes to dynamic tomography. Indeed, in classical tomography, the target is usually assumed to be stationary during the data acquisition, but this is not a realistic model. Moreover, to ensure a lower X-ray radiation dose, only a sparse collection of measurements per time step is assumed to be available. With such a set up, we deal with a sparse data, dynamic tomography problem, which clearly calls for regularization, due to the loss of information in the data and the ongoing motion. In this paper, we propose a 3D variational formulation based on 3D shearlets, where the third dimension accounts for the motion in time, to reconstruct a moving 2D object. Results are presented for real measured data and compared against a 2D static model, in the case of fan-beam geometry. Results are preliminary but show that better reconstructions can be achieved when motion is taken into account

Learning the invisible: a hybrid deep learning-shearlet-based framework for limited angle tomography

The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same time, we witness an exceptional success of data-based methodologies such as deep learning. However, in the context of inverse problems, deep neural networks mostly act as black box routines, used for instance for a somewhat unspecified removal of artifacts in classical image reconstructions. In this paper, we will focus on the severely ill-posed inverse problem of limited angle computed tomography, in which entire boundary sections are not captured in the measurements. We will develop a hybrid reconstruction framework that fuses model-based sparse regularization with data-driven deep learning. Our method is reliable in the sense that we only learn the part that can provably not be handled by model-based methods, while applying the theoretically controllable sparse regularization technique to the remaining parts. Such a decomposition into visible and invisible segments is achieved by means of the shearlet transform that allows to resolve wavefront sets in the phase space. Furthermore, this split enables us to assign the clear task of inferring unknown shearlet coefficients to the neural network and thereby offering an interpretation of its performance in the context of limited angle computed tomography. Our numerical experiments show that our algorithm significantly surpasses both pure model- and more data-based reconstruction methods

Evidence for SERRS Enhancement in the Spectra of Ruthenium Dye–Metal Nanoparticle Conjugates

Metal–molecule interfaces have a high potential for applications in various fields of chemistry, as the features and functions of metal nanostructures can be modified and, to a certain degree, extended by surface-bound molecules. In this article, the functionalization of complex colloidal particles, namely, Au nanopeanuts and Au/Pt/Au nanoraspberries, with the commercially available Ru complexes <b>N719</b>, <b>N749</b>, and <b>Z907</b> is reported; these Ru complexes have already been applied as photosensitizers in dye-sensitized solar cells. A detailed investigation of the conjugates by means of Raman spectroscopy showed that the electronic structures of the ruthenium complexes are retained upon binding to the metal nanoparticles. Furthermore, microfluidics as an efficient tool for the systematic investigations of SER­(R)S signal-enhancement dispersion in colloidal solutions was applied. The enhancement profiles obtained differed from the extinction spectra of the nanoparticles, indicating that electronically resonant processes are involved in the Raman signal enhancement of the investigated nanoparticle conjugates, in addition to the Raman signal enhancement due to the SERS effect