Citations

Abstract The prevalence of steatotic liver disease (SLD) is rising globally, presenting a significant public health challenge. While non-contrast computed tomography (CT) scans hold promise for the opportunistic detection of SLD, their full potential has not been systematically explored. Clinical indicators, such as serum data, are crucial for monitoring the progression of SLD. We introduce a novel Multimodal AI for Opportunistic Steatotic Liver Disease Screening (MAOSS), which integrates demographic data, serum tests, conventional CT biomarkers, and deep learning-derived CT image features to enhance model performance and explainability. MAOSS employs an innovative learning strategy, trained on a large-scale dataset (n = 1,783) encompassing both histopathologically-confirmed SLD and radiologically-reported steatosis cases. The model underwent extensive retrospective validation using steatosis grades derived from both histology and MRI-PDFF across internal and external datasets (n = 913), as well as real-world patient data (n = 18,504). It accurately detected mild to severe steatosis (≥ S1) with AUCs ranging from 0.903 to 0.929. Notably, MAOSS significantly improved the overall AUC for radiologists (n = 11) from 0.709 to 0.798 (p < 0.001). Furthermore, MAOSS was applied to optimize the standard clinical pathway for a simulated primary risk screening of SLD patients (n = 1,192), resulting in the identification of 31% more patients at risk of steatohepatitis and liver fibrosis progression, highlighting its potential to enhance SLD detection and management.

Endothelial cell (EC) injury is a crucial contributor to the progression of diabetic kidney disease (DKD), but the specific EC populations and mechanisms involved remain elusive. Kidney ECs (n = 5464) were collected at three timepoints from diabetic BTBRob/ob mice and non-diabetic littermates. Their heterogeneity, transcriptional changes, and alternative splicing during DKD progression were mapped using SmartSeq2 single-cell RNA sequencing (scRNAseq) and elucidated through pathway, network, and gene ontology enrichment analyses. We identified 13 distinct transcriptional EC phenotypes corresponding to different kidney vessel subtypes, confirmed through in situ hybridization and immunofluorescence. EC subtypes along nephrons displayed extensive zonation related to their functions. Differential gene expression analyses in peritubular and glomerular ECs in DKD underlined the regulation of DKD-relevant pathways including EIF2 signaling, oxidative phosphorylation, and IGF1 signaling. Importantly, this revealed the differential alteration of these pathways between the two EC subtypes and changes during disease progression. Furthermore, glomerular and peritubular ECs also displayed aberrant and dynamic alterations in alternative splicing (AS), which is strongly associated with DNA repair. Strikingly, genes displaying differential transcription or alternative splicing participate in divergent biological processes. Our study reveals the spatiotemporal regulation of gene transcription and AS linked to DKD progression, providing insight into pathomechanisms and clues to novel therapeutic targets for DKD treatment.

AbstractAlthough it is well established that human cytochrome P450 1 family enzymes are induced by cigarette smoking through activation of the Ah receptor, it is not known whether this leads to increased metabolic activation or detoxification of carcinogenic polycyclic aromatic hydrocarbons (PAH), which are present in cigarette smoke and the general environment. We gave oral doses of deuterated phenanthrene ([D10]Phe), a non-carcinogenic surrogate of carcinogenic PAH such as benzo[a]pyrene, to smokers (N = 170, 1 or 10 μg doses) and non-smokers (N = 57, 1 μg dose). Bioactivation products (dihydrodiol and tetraol) and detoxification products (phenols) of [D10]Phe were determined in 6-h urine to obtain a comprehensive metabolic profile. Cigarette smoking increased the bioactivation of [D10]Phe and decreased its detoxification resulting in significantly different metabolic patterns between smokers and non-smokers (P &lt; 0.01), consistent with increased cancer risk in smokers. The Phe bioactivation ratios ([D10]PheT/total [D9]OHPhe) were significantly higher (2.3 (P &lt; 0.01) to 4.8 (P &lt; 0.001) fold) in smokers than non-smokers. With solid human in vivo evidence, our results for the first time demonstrate that cigarette smoking enhances the metabolic activation of Phe, structurally representative of carcinogenic PAH, in humans, strongly supporting their causal role in cancers caused by smoking. The results suggest potential new methods for identifying smokers who could be at particularly high risk for cancer.

Abstract Aim A previous study showed significantly greater reductions in number of cigarettes smoked and biomarkers of toxicant and carcinogen exposure in smokers assigned to immediate reduction of nicotine in cigarettes to very low levels versus gradually over time or continued smoking of normal nicotine content cigarettes. This study examines the effects of these approaches on selected biomarkers associated with harmful biological effects. Design Three‐arm, randomized controlled trial. Setting Ten United States academic institutional sites. Participants Daily smokers uninterested in quitting smoking with a mean age of 45.1 [standard deviation (SD) = 13.4)] years and smoking 17.1 (SD = 8.5) cigarettes/day; 43.9% (549 of 1250) female; 60.6% (758 of 1250) white ethnicity. Interventions (1) Smoking cigarettes where nicotine content was immediately reduced to very low levels ( n = 503); (2) smoking cigarettes where nicotine content was gradually reduced, with dose changes occurring monthly ( n = 498); and (3) continued smoking with normal nicotine content cigarettes ( n = 249). Measurements Smokers were assessed at baseline while smoking their usual brand cigarettes, and again at 4, 8, 12, 16 and 20 weeks. Outcomes were areas under the concentration time curve (AUC) for the period of study of biomarkers of inflammation, oxidative stress and hematological parameters. Findings No consistent significant differences were observed across groups (Bayes factors showing data to be insensitive), with the only exception being red blood cell size variability, which was observed to be lower in the immediate versus gradual nicotine reduction [mean difference = −0.11; 95% confidence interval (CI) = –0.18, −0.04, P = 0.004] and normal nicotine control groups (mean difference = − 0.15, 95% CI = –0.23, −0.06, P = 0.001). Conclusion It remains unclear whether switching to very low nicotine cigarettes leads to a short‐term reduction in biomarkers of tobacco‐related harm.