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We aim to facilitate phantom-based (ground truth) evaluation of dynamic, quantitative myocardial perfusion imaging (MPI) applications. Current MPI phantoms are static representations or lack clinical hard- and software evaluation capabilities. This proof-of-concept study demonstrates the design, realisation and testing of a dedicated cardiac flow phantom. The 3D printed phantom mimics flow through a left ventricular cavity (LVC) and three myocardial segments. In the accompanying fluid circuit, tap water is pumped through the LVC and thereafter partially directed to the segments using adjustable resistances. Regulation hereof mimics perfusion deficit, whereby flow sensors serve as reference standard. Seven phantom measurements were performed while varying injected activity of 99m Tc-tetrofosmin (330–550 MBq), cardiac output (1.5–3.0 L/min) and myocardial segmental flows (50–150 mL/min). Image data from dynamic single photon emission computed tomography was analysed with clinical software. Derived time activity curves were reproducible, showing logical trends regarding selected input variables. A promising correlation was found between software computed myocardial flows and its reference ( ρ = − 0.98; p  = 0.003). This proof-of-concept paper demonstrates we have successfully measured first-pass LV flow and myocardial perfusion in SPECT-MPI using a novel, dedicated, myocardial perfusion phantom. Graphical abstract This proof-of-concept study focuses on the development of a novel, dedicated myocardial perfusion phantom, ultimately aiming to contribute to the evaluation of quantitative myocardial perfusion imaging applications.

Macrophages represent a major immune cell population in atherosclerotic plaques and play central role in the progression of this lipid-driven chronic inflammatory disease. Targeting immunometabolism is proposed as a strategy to revert aberrant macrophage activation to improve disease outcome. Here, we show ATP citrate lyase (Acly) to be activated in inflammatory macrophages and human atherosclerotic plaques. We demonstrate that myeloid Acly deficiency induces a stable plaque phenotype characterized by increased collagen deposition and fibrous cap thickness, along with a smaller necrotic core. In-depth functional, lipidomic, and transcriptional characterization indicate deregulated fatty acid and cholesterol biosynthesis and reduced liver X receptor activation within the macrophages in vitro. This results in macrophages that are more prone to undergo apoptosis, whilst maintaining their capacity to phagocytose apoptotic cells. Together, our results indicate that targeting macrophage metabolism improves atherosclerosis outcome and we reveal Acly as a promising therapeutic target to stabilize atherosclerotic plaques. Inhibition of the metabolic enzyme ATP-citrate lyase can attenuate atherosclerosis by preventing dyslipidemia and potentially also by reducing macrophage-mediated inflammation. Here, the authors show that specific targeting of ACLY in macrophages results in more stable atherosclerotic plaques.

The prevalence of comorbidities in individuals with neurodevelopmental disorders (NDDs) is not well understood, yet these are important for accurate diagnosis and prognosis in routine care and for characterizing the clinical spectrum of NDD syndromes. We thus developed PhenomAD-NDD, an aggregated database containing the comorbid phenotypic data of 51,227 individuals with NDD, all harmonized into Human Phenotype Ontology (HPO), with in total 3,054 unique HPO terms. We demonstrate that almost all congenital anomalies are more prevalent in the NDD population than in the general population, and the NDD baseline prevalence allows for an approximation of the enrichment of symptoms. For example, such analyses of 33 genetic NDDs show that 32% of enriched phenotypes are currently not reported in the clinical synopsis in the Online Mendelian Inheritance in Man (OMIM). PhenomAD-NDD is open to all via a visualization online tool and allows us to determine the enrichment of symptoms in NDD. Data from pediatric populations with neurodevelopmental disorders, obtained through a combinatorial strategy of literature review scoping and in-patient appointments, were used to construct a Phenomics Aggregation Database (PhenomAD-NDD) that can aid clinical diagnosis of comorbidities.

Several molecular and phenotypic algorithms exist that establish genotype–phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype–phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1 , SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP -related phenotypes, already established functionally. PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.

DNA mismatch repair-deficient (MMR-d) cancers present an abundance of neoantigens that is thought to explain their exceptional responsiveness to immune checkpoint blockade (ICB) 1 , 2 . Here, in contrast to other cancer types 3 – 5 , we observed that 20 out of 21 (95%) MMR-d cancers with genomic inactivation of β2-microglobulin (encoded by B2M ) retained responsiveness to ICB, suggesting the involvement of immune effector cells other than CD8 + T cells in this context. We next identified a strong association between B2M inactivation and increased infiltration by γδ T cells in MMR-d cancers. These γδ T cells mainly comprised the Vδ1 and Vδ3 subsets, and expressed high levels of PD-1, other activation markers, including cytotoxic molecules, and a broad repertoire of killer-cell immunoglobulin-like receptors. In vitro, PD-1 + γδ T cells that were isolated from MMR-d colon cancers exhibited enhanced reactivity to human leukocyte antigen (HLA)-class-I-negative MMR-d colon cancer cell lines and B2M -knockout patient-derived tumour organoids compared with antigen-presentation-proficient cells. By comparing paired tumour samples from patients with MMR-d colon cancer that were obtained before and after dual PD-1 and CTLA-4 blockade, we found that immune checkpoint blockade substantially increased the frequency of γδ T cells in B2M-deficient cancers. Taken together, these data indicate that γδ T cells contribute to the response to immune checkpoint blockade in patients with HLA-class-I-negative MMR-d colon cancers, and underline the potential of γδ T cells in cancer immunotherapy. γδ T cells contribute to the response to immune checkpoint blockade treatment in patients with HLA-class-I-negative DNA mismatch repair-deficient colon cancers. .

Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [18F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [18F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes.

This report describes a man in his late 50s who underwent donation-after-circulatory-death kidney transplantation in 2012, due to end-stage kidney disease of unknown origin. More than a decade post-transplant, he presented with a progressive decline in graft function after maintenance immunosuppression had been reduced due to multiple skin carcinomas and the prolonged time since transplantation. Kidney biopsy revealed chronic-active tubulointerstitial nephritis with positive immunohistochemical staining for SV40, initially raising suspicion for BK polyomavirus-associated nephropathy. However, quantitative PCR (qPCR) analysis for BKPyV in both plasma and tissue was negative. In contrast, qPCR for JC polyomavirus (JCPyV) was positive in both plasma and biopsy tissue, leading to the diagnosis of JC polyomavirus nephropathy. Despite the reduction of immunosuppressive therapy, the patient experienced ongoing deterioration of graft function. Our report adds to the limited but growing body of literature on JCPyV and emphasises the need for increased clinical awareness and further research into its prevalence, pathogenesis and optimal management in kidney transplant recipients.

Frontotemporal dementia (FTD) is the second most prevalent form of early-onset dementia, affecting predominantly frontal and temporal cerebral lobes. Heterozygous mutations in the progranulin gene (GRN) cause autosomal-dominant FTD (FTD-GRN), associated with TDP-43 inclusions, neuronal loss, axonal degeneration and gliosis, but FTD-GRN pathogenesis is largely unresolved. Here we report single-nucleus RNA sequencing of microglia, astrocytes and the neurovasculature from frontal, temporal and occipital cortical tissue from control and FTD-GRN brains. We show that fibroblast and mesenchymal cell numbers were enriched in FTD-GRN, and we identified disease-associated subtypes of astrocytes and endothelial cells. Expression of gene modules associated with blood–brain barrier (BBB) dysfunction was significantly enriched in FTD-GRN endothelial cells. The vasculature supportive function and capillary coverage by pericytes was reduced in FTD-GRN tissue, with increased and hypertrophic vascularization and an enrichment of perivascular T cells. Our results indicate a perturbed BBB and suggest that the neurovascular unit is severely affected in FTD-GRN.Frontotemporal dementia (FTD) is a common early-onset dementia caused by heterozygous mutations in the progranulin gene (GRN). Gerrits et al. demonstrate blood–brain barrier dysfunction and a severely affected neurovasculature in FTD-GRN.

Ketoacidosis is a potentially lethal condition caused by the imbalance between hepatic production and extrahepatic utilization of ketone bodies. We performed exome sequencing in a patient with recurrent, severe ketoacidosis and identified a homozygous frameshift mutation in the gene encoding monocarboxylate transporter 1 ( SLC16A1 , also called MCT1 ). Genetic analysis in 96 patients suspected of having ketolytic defects yielded seven additional inactivating mutations in MCT1 , both homozygous and heterozygous. Mutational status was found to be correlated with ketoacidosis severity, MCT1 protein levels, and transport capacity. Thus, MCT1 deficiency is a novel cause of profound ketoacidosis; the present work suggests that MCT1-mediated ketone-body transport is needed to maintain acid–base balance. Ketoacidosis is potentially lethal. The authors of this study found a homozygous frameshift mutation in the gene encoding monocarboxylate transporter 1 ( MCT1 ) in a patient with recurrent, severe ketoacidosis and additional inactivating mutations in MCT1 in 96 other patients. Acetoacetate and 3-hydroxybutyrate are slightly acidic biomolecules that, together with acetone, are called ketone bodies and serve as the major circulating energy source during fasting. Ketone bodies are formed in the liver from the ultimate breakdown product of fatty acids — acetyl coenzyme A (CoA) — by coupling of two acetyl units in a three-step enzymatic process called ketogenesis. Ketone bodies are believed to undergo passive distribution to metabolically active tissues, where they are used as an energy source. 1 Ketoacidosis, a pathologic state, occurs when ketone formation exceeds ketone utilization. The clinical consequences of ketoacidosis are exemplified by diabetic ketoacidosis, . . .