Publications by authors named "Fanrong Li"

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and poses a treatment challenge due to high recurrence risk. Consequently, there is an urgent need for novel and efficacious therapies targeting TNBC. In this context, our study delineates the identification and characterization of a long non-coding RNA (lncRNA)-derived micropeptide miPEP205.

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Objective: We aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.

Methods: Barcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models.

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Objective: To assess the independent and combined associations of tumor-related psychiatric symptoms (TRPS) with dynamic health-related quality of life (HRQL) in patients with hepatocellular carcinoma (HCC) after hepatectomy and to identify related patterns of health behaviors.

Methods: This prospective study included patients with HCC who underwent hepatectomy between September 2021 and May 2022. Independent and combined associations between TRPS and HRQL were identified by generalized linear model and weighted quantile sum model, respectively.

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Unlabelled: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of lethal kidney cancer. Reprogramming of fatty acid and glucose metabolism resulting in the accumulation of lipids and glycogen in the cytoplasm is a hallmark of ccRCC. Here, we identified a micropeptide ACLY-BP encoded by the GATA3-suppressed LINC00887, which regulated lipid metabolism and promoted cell proliferation and tumor growth in ccRCC.

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Network pruning and binarization have been demonstrated to be effective in neural network accelerator design for high speed and energy efficiency. However, most existing pruning approaches achieve a poor tradeoff between accuracy and efficiency, which on the other hand, has limited the progress of neural network accelerators. At the same time, binary networks are highly efficient, however, a large accuracy gap exists between binary networks and their full-precision counterparts.

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