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Although plasma complement factor B (CFB, NX_P00751), both alone and in combination with CA19-9 (i.e., the ComB-CAN), previously exhibited a reliable diagnostic ability for pancreatic cancer (PC), its detectability of the early stages and the cancer detection mechanism remained elusive. We first evaluated the diagnostic accuracy of ComB-CAN using plasma samples from healthy donors (HDs), patients with chronic pancreatitis (CP), and patients with different PC stages (I/II vs III/IV). An analysis of the area under the curve (AUC) by PanelComposer using logistic regression revealed that ComB-CAN has a superior diagnostic ability for early-stage PC (97.1.% [95% confidence interval (CI): (97.1-97.2)]) compared with CFB (94.3% [95% CI: 94.2-94.4]) or CA19-9 alone (34.3% [95% CI: 34.1-34.4]). In the comparisons of all stages of patients with PC vs CP and HDs, the AUC values of ComB-CAN, CFB, and CA19-9 were 0.983 (95% CI: 0.983-0.983), 0.950 (95% CI: 0.950-0.951), and 0.873 (95% CI: 0.873-0.874), respectively. We then investigated the molecular mechanism underlying the detection of early-stage PC by using stable cell lines of knockdown and overexpression. A global transcriptomic analysis coupled to cell invasion assays of both -modulated cell lines suggested that plays a tumor-promoting role in PC, which likely initiates the PI3K-AKT cancer signaling pathway. Thus our study establishes ComB-CAN as a reliable early diagnostic marker for PC that can be clinically applied for early PC screening in the general public.
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http://dx.doi.org/10.1021/acs.jproteome.1c00805 | DOI Listing |
Arch Orthop Trauma Surg
September 2025
Division of Orthopaedics and Traumatology, Cantonal Hospital Winterthur, Winterthur, Switzerland.
Background: Accurate acetabular cup orientation in total hip arthroplasty (THA) is crucial for successful outcomes. Intraoperative fluoroscopy may be used to evaluate acetabular cup placement. This study aimed to evaluate the accuracy of purely visual estimation of cup inclination and anteversion using intraoperative fluoroscopy, considering different surgeon experience levels and cup designs.
View Article and Find Full Text PDFClin Res Cardiol
September 2025
Department of (Interventional) Cardiology, Thoraxcenter, Erasmus University Medical Center, Room Rg-628, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands.
Background: Fractional flow reserve (FFR) for non-culprit lesions (NCLs) in patients with ST-elevation myocardial infarction (STEMI) can be influenced by temporary changes in microvascular resistance. Angiography-derived vessel fractional flow reserve (vFFR) has been tested as a less-invasive alternative.
Aims: The FAST STEMI II study aimed to assess the diagnostic performance of acute-setting vFFR vs.
Acta Parasitol
September 2025
Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaborative Innovation Center for Eco-Environment, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Science, Hebei Normal University, Shijiazhuang, 050024, China.
Purpose: This study aimed to identify and analyze the role of Ferric reductase inBlastocystis sp. subtype 2 (ST2) and explore the relationship between the parasite and iron metabolism.
Methods: The location of Ferric reductase in Blastocystis sp.
Mol Pharm
September 2025
Center for Orthopedic Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China.
Myocardial fibrosis, a key pathological feature of hypertensive heart disease (HHD), remains diagnostically challenging due to limited clinical tools. In this study, a FAPI-targeted uptake mechanism previously reported by our group, originally developed for tumor imaging, is extended to the detection of myocardial fibrosis in HHD using [F]F-NOTA-FAPI-MB. The diagnostic performance of this tracer is compared with those of [F]F-FDG, [F]F-FAPI-42, and [F]F-NOTA-FAP2286, and its potential for fluorescence imaging is also evaluated.
View Article and Find Full Text PDFRadiology
September 2025
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115.
Despite the rapid growth of Food and Drug Administration-cleared artificial intelligence (AI)- and machine learning-enabled medical devices for use in radiology, current tools remain limited in scope, often focusing on narrow tasks and lacking the ability to comprehensively assist radiologists. These narrow AI solutions face limitations in financial sustainability, operational efficiency, and clinical utility, hindering widespread adoption and constraining their long-term value in radiology practice. Recent advances in generative and multimodal AI have expanded the scope of image interpretation, prompting discussions on the development of generalist medical AI.
View Article and Find Full Text PDF