Prospective comparison of 68Ga-FAPI-04 and 18F-FDG PET/CT for tumor staging in nasopharyngeal carcinoma

PurposeTo explore the difference in the effectiveness of gallium-68 fibroblast activation protein inhibitor (68Ga-FAPI-04) PET/CT and fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT for the initial staging of patients with nasopharyngeal carcinoma (NPC).MethodsThe Affiliated Hospital of Southwest Medical University hosted this single-center prospective investigation (Clinical Trials registration No.ChiCTR2100044131) between March 2020 and September 2021. Within a week, all subjects underwent MR scans, 68Ga-FAPI-04 PET/CT, and 18F-FDG PET/CT in order. The effectiveness of medical staging employing 68Ga-FAPI-04 and 18F-FDG PET/CT was compared.ResultsTwenty-eight patients with primary NPC were evaluated (mean age53 ± 11 years). 68Ga-FAPI-04 PET/CT indicated an elevated recognition rate for diagnosing primary tumors (28/28 [100%] vs. 27/28 [96%]) and lymph node metastases (263/285 [92%] vs. 228/285 [80%]), but a lower detection rate for distant metastases (5/7 [71%] vs. 7/7 [100%]) compared with 18F-FDG PET/CT. A significant association between the maximum standard uptake value (SUVmax) of 18F-FDG PET and 68Ga-FAPI-04 PET was found in the primary cancers (r = 0.691, p < 0.001). In comparison to 18F-FDG PET/CT, 68Ga-FAPI-04 PET/CT upstaged the T stage in five patients while downstaging the N stage in seven patients. 68Ga-FAPI-04 PET/CT corrected the overall staging of five patients on18F-FDG PET/CT.Conclusion68Ga-FAPI-04 PET/CT is preferable to 18F-FDG PET/CT for NPC staging in terms of the detection efficiency for primary tumors and lymph node metastasis. This is especially true when evaluating the primary cancer and any spread to contiguous tissues. It is possible to improve the staging assessment of NPC by using 68Ga-FAPI-04 PET/CT in conjunction with 18F-FDG PET/CT

Oxysophocarpine Retards the Growth and Metastasis of Oral Squamous Cell Carcinoma by Targeting the Nrf2/HO-1 Axis

Background/Aims: Nuclear factor erythroid 2-related factor 2 (Nrf2) is an oncogene in various types of cancers, including oral squamous cell carcinoma (OSCC). Oxysophocarpine (OSC) is a natural alkaloid that has multiple pharmacological activities. However, the biological functions and molecular mechanism underlying the effects of OSC on the growth and metastasis of OSCC are unclear. Methods: Nrf2 levels were determined in OSCC tissues and non-cancerous specimens by quantitative real-time PCR, western blotting, and immunohistochemistry (IHC) assays. The effects of OSC on OSCC cell growth and metastasis were explored (1) using 5-ethynyl-20-deoxyuridine staining and Cell Counting Kit-8, colony formation, flow cytometry, wound-healing, Transwell, and tube formation assays in vitro; and (2) by establishing a xenograft nude mouse model in vivo. The molecular mechanisms underlying the effects of OSC on the growth and metastasis of OSCC were investigated in vitro by western blotting, caspase-3 activity, and enzyme-linked immunosorbent assays, and in vivo by western blotting and IHC assays. Results: The expression levels of Nrf2 in OSCC tissues and in cell lines were much higher than in non-cancerous tissues and normal oral keratinocytes. The upregulation of Nrf2 was positively correlated with a high incidence of lymph node metastasis and advanced histological grade and TNM stage, but inversely associated with differentiation and survival of OSCC patients. OSC reduced the expression of Nrf2 and heme oxygenase 1 (HO-1) in OSCC cells. OSC also inhibited proliferation, migration, invasion, and pro-angiogenesis of OSCC cells. Moreover, OSC induced cell cycle arrest, enhanced apoptosis of OSCC cells in vitro, and decreased OSCC tumor growth in vivo. Mechanically, OSC reduced the aggressive behavior of OSCC cells by inactivation of the Nrf2/HO-1 signaling pathway. Conclusion: Our findings provide evidence that OSC inhibits the growth and metastasis of OSCC by targeting the Nrf2/ HO-1 axis, suggesting that OSC may be a potential therapeutic agent for OSCC

GMDNet: A Graph-Based Mixture Density Network for Estimating Packages’ Multimodal Travel Time Distribution

In the logistics network, accurately estimating packages' Travel Time Distribution (TTD) given the routes greatly benefits both consumers and platforms. Although recent works perform well in predicting an expected time or a time distribution in a road network, they could not be well applied to estimate TTD in logistics networks. Because TTD prediction in the logistics network requires modeling packages' multimodal TTD (MTTD, i.e., there can be more than one likely output with a given input) while leveraging the complex correlations in the logistics network. To this end, this work opens appealing research opportunities in studying MTTD learning conditioned on graph-structure data by investigating packages' travel time distribution in the logistics network. We propose a Graph-based Mixture Density Network, named GMDNet, which takes the benefits of both graph neural network and mixture density network for estimating MTTD conditioned on graph-structure data (i.e., the logistics network). Furthermore, we adopt the Expectation-Maximization (EM) framework in the training process to guarantee local convergence and thus obtain more stable results than gradient descent. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed model