Publications by authors named "Yita Hsieh"

Mitochondrial transcriptional factor A (TFAM) acts as a key regulatory to control mitochondrial DNA (mtDNA); the impact of TFAM and mtDNA in modulating carcinogenesis is controversial. Current study aims to define TFAM mediated regulations in head and neck cancer (HNC). Multifaceted analyses in HNC cells genetically manipulated for TFAM were performed.

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This study surveyed visitor perceptions of the resources of the Guanwu National Forest Recreation Area (GNFRA) in Taiwan', their perceptions toward the ecosystem service functions of the GNFRA, their expectations and opinions on its management, and their recreational activities. Independent sample t-tests and one-way analysis of variance (ANOVA) were used to examine the influences of the backgrounds of respondents on their perceptions toward the ecosystem service functions of the GNFRA. The importance-performance analysis (IPA) method was used to explore evaluations by tourists on the management of the GNRFA.

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To support great demand of cell growth, cancer cells preferentially obtain energy and biomacromolecules by glycolysis over mitochondrial oxidative phosphorylation (OxPhos). Among all glycolytic enzymes, hexokinase (HK), a rate-limiting enzyme at the first step of glycolysis to catalyze cellular glucose into glucose-6-phosphate, is herein emphasized. Four HK isoforms, HK1-HK4, were discovered in nature.

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Kaposi's sarcoma-associated herpesvirus (KSHV) is the causative agent of Kaposi's sarcoma (KS), an AIDS-defining cancer with abnormal angiogenesis. The high incidence of KS in human immunodeficiency virus (HIV)-infected AIDS patients has been ascribed to an interaction between HIV type 1 (HIV-1) and KSHV, focusing on secretory proteins. The HIV-1 secreted protein HIV Tat has been found to synergize with KSHV lytic proteins to induce angiogenesis.

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Considering the great energy and biomass demand for cell survival, cancer cells exhibit unique metabolic signatures compared to normal cells. Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent neoplasms worldwide. Recent findings have shown that environmental challenges, as well as intrinsic metabolic manipulations, could modulate HNSCC experimentally and serve as clinic prognostic indicators, suggesting that a better understanding of dynamic metabolic changes during HNSCC development could be of great benefit for developing adjuvant anti-cancer schemes other than conventional therapies.

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Even with increasing evidence for roles of glycolytic enzymes in controlling cancerous characteristics, the best target of candidate metabolic enzymes for lessening malignancy remains under debate. Pyruvate is a main glycolytic metabolite that could be mainly converted into either lactate by Lactate Dehydrogenase A (LDHA) or acetyl-CoA by Pyruvate Dehydrogenase E1 component α subunit (PDHA1) catalytic complex. In tumor cells, accumulating lactate is produced whereas the conversion of pyruvate into mitochondrial acetyl-CoA is less active compared with their normal counterparts.

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Ductal epithelium is primarily detected in porcine neonatal pancreatic cell clusters (NPCCs) bearing grafts, suggesting that transplants might exhibit progenitor-like phenotypes. Here we found that soon after NPCC isolation, PDX1/insulin and SOX9 pancreatic progenitor-like cells dramatically increased while dual-hormonal progenitor-like cells were routinely observed in NPCC culture. After transplantation (Tx), insulin cells increased and PDX1 and SOX9 cells gradually decreased in both non-diabetic (NDM) and streptozotocin-induced diabetic (DM) grafts over 2 months.

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This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms.

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