Objective: To establish a fast and sensitive method for the detection of 8-hydroxy-2'-deoxyguanosine (8-OHdG) in precision-cut rat liver slices by HPLC-MS/MS and to investigate isoniazid (INH) -induced oxidative DNA damage.
Methods: Precision-cut liver slices (300 microm) were prepared from male rats, and incubated with INH (0.018 mol/L) for 2 h after 1 h preincubation. DNA in the slices was extracted and digested into free nucleosides at 37 degrees C. The samples were injected into HPLC-MS/MS after the proteins were removed. The level of oxidative DNA damage was estimated using the ratio of 8-OHdG to deoxyguanosine (dG).
Results: The limit of detection of 8-OHdG was 1 ng/mL (S/N=3) and the intra-assay relative standard variation was 3.38% when one transition 284.3/168.4 was used as a quantifier and another two transitions 284.3/140.2, 306.1/190.2 as qualifiers. 8-OHdG and dG were well separated, as indicated by elution at 10.02 and 7.37 min, respectively. INH significantly increased the ratio of 8-OHdG to dG in rat liver slices (P<0.05).
Conclusion: 8-OHdG in precision-cut liver slices could be sensitively determined by HPLC-MS/MS. HPLC-MS/MS coupled with precision-cut tissue slices is a fast and reliable analytical technique to evaluate oxidative DNA damage of target tissues caused by procarcinogens and cytotoxins.
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EJNMMI Phys
January 2025
Department of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel.
Background: A recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated.
Method: The digital-BGO PET/CT with AI-based auto-positioning was compared (χ, Mann-Whitney tests) to a solid-state lutetium-yttrium oxyorthosilicate (digital-LYSO) PET/CT with manual patient positioning (n = 432 and 343 studies each, respectively), with results split into groups before and after the date of a recalibration of the digital-BGO auto-positioning camera.
Sci Adv
January 2025
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Small extracellular vesicles (sEVs) are nanosized vesicles. Death receptor 5 (DR5) mediates extrinsic apoptosis. We engineer DR5 agonistic single-chain variable fragment (scFv) expression on the surface of sEVs derived from natural killer cells.
View Article and Find Full Text PDFAging Dis
January 2025
Institute of Nutrition and Food Technology (INTA), Universidad de Chile, Santiago, Chile.
The gut-brain axis is a bidirectional communication pathway that modulates cognitive function. A dysfunctional gut-brain axis has been associated with cognitive impairments during aging. Therefore, we propose evaluating whether modulation of the gut microbiota through fecal microbiota transplantation (FMT) from young-trained donors (YT) to middle-aged or aged mice could enhance brain function and cognition in old age.
View Article and Find Full Text PDFLiver Int
February 2025
Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Background & Aims: Body composition is an objective assessment reflecting nutritional status and is highly gender different. Surgical resection, the standard treatment for early-stage hepatocellular carcinoma (HCC), is an energy-consuming major operation that would affect body composition. However, the impacts of body composition on the post-operative prognosis of HCC are still uncertain.
View Article and Find Full Text PDFJ Magn Reson Imaging
February 2025
BioMedical Engineering and Imaging Institute, Icahn School of Medicine Mount Sinai, New York, New York, USA.
Background: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.
Purpose: To report initial results on automatic classification of liver MRE image quality using various deep learning (DL) architectures.
Study Type: Retrospective, single center, IRB-approved human study.
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