👤 M Rama

Chylomicronemia is characterized by extreme hypertriglyceridemia (triglyceride values >10 mmol/L). It may be caused by a biallelic combination of a pathogenic variant [familial chylomicronemia syndrome (FCS)] or by genetic susceptibility combined with comorbidities and environmental factors [multifactorial chylomicronemia syndrome (MCS)]. Acute pancreatitis (AP) is the most serious complication of chylomicronemia. In the general population, the prevalence of AP during pregnancy is estimated to be <0.35%. As triglyceride levels significantly increase during pregnancy, it may affect the course of pregnancy and further increase the risk of AP in women with chylomicronemia. One hundred sixteen pregnancies involving 49 European and North American women with a history of chylomicronemia (20 FCS, 29 MCS) were retrospectively reviewed. The occurrence of AP, the course of pregnancy, fetal development, and delivery were evaluated. Forty-two percent of FCS and 10% of MCS women experienced at least 1 AP episode during pregnancy (P = .01). Compared to MCS, women with FCS presented a higher percentage of pregnancies with AP (17% vs 5%, P = .02). Among all reviewed pregnancy-related AP, 56% occurred in primigravida FCS women compared to 0% in MCS. Premature deliveries were elevated in both groups, although they were more frequent in FCS (56%) vs MCS (19%) (P = .01). The percentages of miscarriages (11.8% vs 10.7%) and fetal failure to thrive (5.9% vs 9.2%) were not significantly different between the 2 cohorts. In this study, pregnant women with chylomicronemia had a 30-fold (MCS) to 120-fold (FCS) higher occurrence of AP compared to the general population. Chylomicronemia per se does not seem to influence fetal development. Show less

Obesity is a multifactorial disease with a strong genetic component. It is imperative to enhance the identification of genetic variations in their early and severe manifestations in order to facilitate the development of personalized therapeutic strategies, informed clinical care, and the facilitation of genetic counseling. The objective of the study was to provide a comprehensive description of rare variations identified by next generation sequencing of a panel of genes. From 2018 to 2023, a panel of 22 genes was genotyped in 1066 probands (499 children and 567 adults) with severe early-onset obesity. The genetic study led to a molecular diagnosis in 34 probands (3.2%) and revealed variants of unknown significance (VUS) in 10.3% of cases. In the pediatric cohort, a genetic diagnosis was established in 19 probands (3.8%) and a VUS was identified in 67 (13.4%). A total of 152 rare single-nucleotides variants (SNVs) were identified, of which 34 were classified as pathogenic, predominantly within the Early genetic screening in severe obesity provides valuable diagnostic insights and identifies candidates for personalized treatment. The integration of genetics into clinical practice is imperative for the enhancement of care pathways and the facilitation of targeted therapeutic strategies. Show less

A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ζ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Show less