Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

• MeSH
• deep learning * MeSH
• duktální karcinom pankreatu * diagnostické zobrazování   patologie   MeSH
• lidé MeSH
• nádory slinivky břišní * diagnostické zobrazování   patologie   MeSH
• pankreas diagnostické zobrazování   patologie   MeSH
• počítačová rentgenová tomografie MeSH
• retrospektivní studie MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH

Subcellular membrane systems are highly enriched in dolichol, whose role in organelle homeostasis and endosomal-lysosomal pathway remains largely unclear besides being involved in protein glycosylation. DHDDS encodes for the catalytic subunit (DHDDS) of the enzyme cis-prenyltransferase (cis-PTase), involved in dolichol biosynthesis and dolichol-dependent protein glycosylation in the endoplasmic reticulum. An autosomal recessive form of retinitis pigmentosa (retinitis pigmentosa 59) has been associated with a recurrent DHDDS variant. Moreover, two recurring de novo substitutions were detected in a few cases presenting with neurodevelopmental disorder, epilepsy and movement disorder. We evaluated a large cohort of patients (n = 25) with de novo pathogenic variants in DHDDS and provided the first systematic description of the clinical features and long-term outcome of this new neurodevelopmental and neurodegenerative disorder. The functional impact of the identified variants was explored by yeast complementation system and enzymatic assay. Patients presented during infancy or childhood with a variable association of neurodevelopmental disorder, generalized epilepsy, action myoclonus/cortical tremor and ataxia. Later in the disease course, they experienced a slow neurological decline with the emergence of hyperkinetic and/or hypokinetic movement disorder, cognitive deterioration and psychiatric disturbances. Storage of lipidic material and altered lysosomes were detected in myelinated fibres and fibroblasts, suggesting a dysfunction of the lysosomal enzymatic scavenger machinery. Serum glycoprotein hypoglycosylation was not detected and, in contrast to retinitis pigmentosa and other congenital disorders of glycosylation involving dolichol metabolism, the urinary dolichol D18/D19 ratio was normal. Mapping the disease-causing variants into the protein structure revealed that most of them clustered around the active site of the DHDDS subunit. Functional studies using yeast complementation assay and in vitro activity measurements confirmed that these changes affected the catalytic activity of the cis-PTase and showed growth defect in yeast complementation system as compared with the wild-type enzyme and retinitis pigmentosa-associated protein. In conclusion, we characterized a distinctive neurodegenerative disorder due to de novo DHDDS variants, which clinically belongs to the spectrum of genetic progressive encephalopathies with myoclonus. Clinical and biochemical data from this cohort depicted a condition at the intersection of congenital disorders of glycosylation and inherited storage diseases with several features akin to of progressive myoclonus epilepsy such as neuronal ceroid lipofuscinosis and other lysosomal disorders.

• MeSH
• alkyltransferasy a aryltransferasy * MeSH
• dítě MeSH
• dolichol metabolismus   MeSH
• lidé MeSH
• myoklonus * MeSH
• neurodegenerativní nemoci * genetika   MeSH
• retinopathia pigmentosa * genetika   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) is the most common congenital malformation of the upper digestive tract. This study represents the first genome-wide association study (GWAS) to identify risk loci for EA/TEF. We used a European case-control sample comprising 764 EA/TEF patients and 5,778 controls and observed genome-wide significant associations at three loci. On chromosome 10q21 within the gene CTNNA3 (p = 2.11 × 10-8; odds ratio [OR] = 3.94; 95% confidence interval [CI], 3.10-5.00), on chromosome 16q24 next to the FOX gene cluster (p = 2.25 × 10-10; OR = 1.47; 95% CI, 1.38-1.55) and on chromosome 17q12 next to the gene HNF1B (p = 3.35 × 10-16; OR = 1.75; 95% CI, 1.64-1.87). We next carried out an esophageal/tracheal transcriptome profiling in rat embryos at four selected embryonic time points. Based on these data and on already published data, the implicated genes at all three GWAS loci are promising candidates for EA/TEF development. We also analyzed the genetic EA/TEF architecture beyond the single marker level, which revealed an estimated single-nucleotide polymorphism (SNP)-based heritability of around 37% ± 14% standard deviation. In addition, we examined the polygenicity of EA/TEF and found that EA/TEF is less polygenic than other complex genetic diseases. In conclusion, the results of our study contribute to a better understanding on the underlying genetic architecture of ET/TEF with the identification of three risk loci and candidate genes.

• Publikační typ
• časopisecké články MeSH