Prospective characterization of incident hepatic steatosis in pediatric and adolescent patients after total pancreatectomy with islet autotransplantation

BackgroundHepatic steatosis has been described as a common finding in adults following total pancreatectomy with islet autotransplantation (TPIAT) but it is unknown if this occurs in children and adolescents.ObjectivesTo define the frequency of post-TPIAT hepatic steatosis in a sample of children and adolescents and to identify clinical predictors of incident steatosis post-TPIAT.MethodsIn this prospective study, consecutive participants at least 1-month post-TPIAT underwent a liver MRI with proton density fat fraction (PDFF) and blood draw at our pediatric academic medical center between April 2021 and January 2022. Comparison clinical pre-TPIAT liver MRI or ultrasound and insulin use and graft function data were extracted from the medical record. T-tests were used for the comparison of means across continuous variables between participants with and without post-TPIAT steatosis.ResultsA total of 20 participants (mean: 13 ± 4 years; 12 female) were evaluated. Mean liver PDFF at research MRI was 7.4 ± 6.2% (range: 2–25%). Seven participants (35%) had categorical hepatic steatosis (PDFF>5%) post-TPIAT, five of whom had pre-TPIAT steatosis, reflecting a 13% (2/15; 95% CI: 2–40%) incidence of post-TPIAT steatosis. Participant characteristics were not significantly different between subgroups with and without post-TPIAT steatosis. Mean PDFF at research MRI was not different between graft function subgroups (7.5% optimal/good vs. 7.3% marginal/failure; p = .96).ConclusionOur study shows a moderate prevalence but low incidence of hepatic steatosis in a small sample of children and adolescents post-TPIAT. This study raises questions about a causal relationship between TPIAT and hepatic steatosis.     

脂质纳米粒-mRNA递送系统及其在嵌合抗原受体T细胞治疗中的应用

尽管嵌合抗原受体（CAR）T细胞治疗在血液系统恶性肿瘤患者中取得了显著的临床疗效，但需要进一步优化。脂质纳米粒（LNP）-信使核糖核酸（mRNA）递送系统作为一种非病毒性基因载体运用于CAR-T细胞治疗研究中，一方面通过LNP将密封蛋白-6 mRNA靶向递送至抗原提呈细胞，从而实现抗原提呈细胞辅助性增强密封蛋白-6靶向的CAR-T细胞的功能，以进一步诱导对实体瘤的清除；另一方面，通过LNP将成纤维细胞激活蛋白（FAP）CARmRNA靶向递送至T细胞，实现体内FAP靶向的CAR-T细胞的制备，以通过阻断心脏纤维化过程达到治疗急性心肌损伤的目的。在CAR-T细胞研究和治疗中，LNP-mRNA递送系统具有不与细胞基因组整合、价格便宜、毒副作用小及可修饰等优点，亦存在蛋白瞬时表达导致调控细胞功能的持久性不足及制备等方面的技术局限性。本文综述了LNP-mRNA递送系统及其在CAR-T细胞治疗中的应用研究。     

Recent advances in lipid nanoparticles for delivery of nucleic acid,mRNA, and gene editing-based therapeutics

Lipid nanoparticles (LNPs) are becoming popular as a means of delivering therapeutics, including those based on nucleic acids and mRNA. The mRNA-based coronavirus disease 2019 vaccines are perfect examples to highlight the role played by drug delivery systems in advancing human health. The fundamentals of LNPs for the delivery of nucleic acid- and mRNA-based therapeutics, are well established. Thus, future research on LNPs will focus on addressing the following: expanding the scope of drug delivery to different constituents of the human body, expanding the number of diseases that can be targeted, and studying the change in the pharmacokinetics of LNPs under physiological and pathological conditions. This review article provides an overview of recent advances aimed at expanding the application of LNPs, focusing on the pharmacokinetics and advantages of LNPs. In addition, analytical techniques, library construction and screening, rational design, active targeting, and applicability to gene editing therapy have also been discussed.     

A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis

Evaluation of the ossification of the medial clavicular epiphysis plays a key role in forensic age estimation. The purpose of the present study was to assess a new numerical cut-off at the age of 18 years, taking into consideration Magnetic Resonance (MR) images of the medial clavicular epiphysis. We analyzed 163 MR scans of Italian subjects aged between 14 and 25 years. Using the data obtained we calculated two ratios: REM-1 (ratio between the length of the whole epiphysis and the length of the metaphysis) and REM-2 (ratio between the length of epiphyseal-metaphyseal fusion and the length of the metaphysis). In 68 out of 163 cases it was not possible to measure REM-2. The reproducibility was demonstrated using the Intraclass Correlation Coefficient (ICC) (Cronbach’s alpha > 0.80). REM-1 and REM-2 were compared in each category of age (adult and minor) by the Wilcoxon signed-rank test. The cut-off points for measurements of REM-1 and REM-2 were determined by logistic regression. For REM-1, the cut-off scores were 0.83 for all individuals (accuracy = 94.77%) and males (accuracy = 96.05%), and 0.86 for females (accuracy = 92.30%). For REM-2, the cut-off values were 0.40 for all individuals and males (accuracy = 100.00%), and 0.41 for females (accuracy = 100.00%). Finally, receiver operating characteristic (ROC) curves for age classification based on REM-1 and REM-2 were constructed, showing that REM-2 had the highest discriminative power. Thus, a new cut-off model for predicting the age of majority has been introduced, conducting a quantitative analysis thanks to the use of a high-resolution imaging tool.     

MR-safety in clinical practice at 7T: Evaluation of a multistep screening process in 1819 subjects

IntroductionMR facilities must implement and maintain adequate screening and safety procedures to ensure safety during MR examinations. The aim of this study was to evaluate a multi-step MR safety screening process used at a 7T facility regarding incidence of different types of safety risks detected during the safety procedure.MethodsSubjects scheduled for an MR examination and having entered the 7T facility during 2016–2019 underwent a pre-defined multi-step MR safety screening process. Screening documentation of 1819 included subjects was reviewed, and risks identified during the different screening steps were compiled. These data were also related to documented decisions made by a 7T MR safety committee and reported MR safety incidents.ResultsPassive or active implants (n = 315) were identified in a screening form and/or an additional documented interview in 305 subjects. Additional information not previously self-reported by the subject, regarding implants necessitating safety decisions performed by the staff was revealed in the documented interview in 102 subjects (106 items). In total, the 7T MR safety committee documented a decision in 36 (2%) of the included subjects. All of these subjects were finally cleared for scanning.ConclusionA multi-step screening process allows a thorough MR screening of subjects, avoiding safety incidents. Different steps in the process allow awareness to rise and items to be detected that were missed in earlier steps.Implications for practiceSafety questions posed at a single timepoint during an MR screening process might not reveal all safety risks. Repetition and rephrasing of screening questions leads to increased detection of safety risks. This could be effectively mitigated by a multi-step screening process. A multi-disciplinary safety committee is efficient at short notice responding to unexpected safety issues.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.