Publications by authors named "Kenneth Zhu"

The purpose of this work was to evaluate the performance of CoVarScan, a multiplex fragment analysis approach, in identifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of the Omicron lineage rapidly and accurately. The ability to identify variants with high fidelity and low turnaround time is important both epidemiologically and clinically for pandemic monitoring and therapeutic monoclonal antibody (mAb) selection. Currently, the gold-standard test for this task is whole-genome sequencing (WGS), which is prohibitively expensive and/or inaccessible due to equipment requirements for many laboratories.

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Gliomas make up ~80% of malignant brain tumors in adults and are responsible for the majority of deaths from primary brain tumors. The glioma tumor microenvironment (TME) is a dynamic, heterogeneous mixture of extracellular matrix and malignant and non-malignant cells. Several ongoing clinical trials are evaluating the efficacy of therapies that target non-malignant cells, particularly immune cells.

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Objectives: Beta-3 adrenergic receptors (β3-AR) stimulate lipolysis and thermogenesis in white and brown adipose tissue (WAT and BAT). Obesity increases oxidative stress and inflammation that attenuate AT β3-AR signaling. The objective of this study was to test the hypothesis that the combination of the β3-AR agonist CL-316,243 (CL) and the antioxidant alpha-lipoic acid (ALA) would lower inflammation in diet-induced obesity (DIO) and improve β3-AR function.

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β3-Adrenergic receptors (β3-ARs) are the predominant regulators of rodent brown adipose tissue (BAT) thermogenesis. However, in humans, the physiological relevance of BAT and β3-AR remains controversial. Herein, using primary human adipocytes from supraclavicular neck fat and immortalized brown/beige adipocytes from deep neck fat from 2 subjects, we demonstrate that the β3-AR plays a critical role in regulating lipolysis, glycolysis, and thermogenesis.

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Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures.

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The current obesity pandemic results from a physiological imbalance in which energy intake chronically exceeds energy expenditure (EE), and prevention and treatment strategies remain generally ineffective. Approaches designed to increase EE have been informed by decades of experiments in rodent models designed to stimulate adaptive thermogenesis, a long-term increase in metabolism, primarily induced by chronic cold exposure. At the cellular level, thermogenesis is achieved through increased rates of futile cycling, which are observed in several systems, most notably the regulated uncoupling of oxidative phosphorylation from ATP generation by uncoupling protein 1, a tissue-specific protein present in mitochondria of brown adipose tissue (BAT).

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Background: Neuroblastoma (NB) is the most common extracranial solid tumor found in children. The frequent gain/loss of many chromosome bands in tumor cells and absence of mutations found at diagnosis suggests that NB is a copy number-driven cancer. Despite the previous work, a systematic analysis that investigates the relationship between such frequent gain/loss of chromosome bands and patient prognosis has yet to be implemented.

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