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Ferroptosis is a form of iron-dependent programmed cell death, which is distinct from apoptosis, necrosis, and autophagy. Mitochondria play a critical role in initiating and amplifying ferroptosis in cancer cells. Voltage-Dependent Anion Channel 1 (VDAC1) embedded in the mitochondrial outer membrane, exerts roles in regulation of ferroptosis.

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We aimed to explore the role of ikarugamycin (IKA) in breast cancer, its connection with hexokinase-2 (HK-2) repression, and tissue factor (TF). This study sought to extend the role of HK-2 as a TF activator in a comprehensive analysis of these interactions from the enzyme, gene, and protein levels. The investigation was performed with MDA-MB-231 and MCF-7 breast cancer lines.

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Glioblastoma(GBM) is a highly malignant primary central nervous system tumor that poses a significant threat to patient survival due to its treatment resistance and rapid recurrence.Current treatment options, including maximal safe surgical resection, radiotherapy, and temozolomide (TMZ) chemotherapy, have limited efficacy.In recent years, the role of glycolytic metabolic reprogramming in GBM has garnered increasing attention.

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Ocular inoculation of toad venom: toxic cataract and proteomic profiling.

Front Med (Lausanne)

January 2025

Eye Center of the 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

Purpose: To report a singular case of cataract caused by toad venom inoculation and to scrutinize the pathological mechanisms through proteomic sequencing of the lens specimen.

Methods: A young Chinese male presented with progressively deteriorating vision in his right eye subsequent to a history of toad venom inoculation. He was diagnosed with a toxic cataract, and underwent phacoemulsification cataract surgery.

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Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism.

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