Publications by authors named "G N Tseng"

Introduction: Alterations in multiple subregions of the human prefrontal cortex (PFC) have been heavily implicated in psychiatric diseases. Moreover, emerging evidence suggests that circadian rhythms in gene expression are present across the brain, including in the PFC, and that these rhythms are altered in disease. However, investigation into the potential circadian mechanisms underlying these diseases in animal models must contend with the fact that the human PFC is highly evolved and specialized relative to that of rodents.

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Aging women experience a significant decline of ovarian hormones, particularly estrogen, following menopause, and become susceptible to cognitive and psychomotor deficits. Although the effects of estrogen depletion had been documented in the prefrontal and somatosensory cortices, its impact on somatomotor cortex, a region crucial for motor and cognitive functions, remains unclear. To explore this, we ovariectomized young adult female rats and fed subsequently with phytoestrogen-free diet and studied the effects of estrogen depletion on the somato-sensory and motor cortices.

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Unintentional weight loss in older populations is linked to greater mortality and morbidity risks. This study aims to understand the metabolic mechanisms of unintentional weight loss and their relationship with body composition changes in older adults. We investigated plasma metabolite associations with weight and body composition changes over 5 years in 1335 participants (mean age 73.

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Introduction: Circadian rhythm disturbances have long been associated with the development of psychiatric disorders, including mood and substance use disorders. Adolescence is a particularly vulnerable time for the onset of psychiatric disorders and for circadian rhythm and sleep disruptions. Preclinical studies have found that circadian rhythm disruption (CRD) impacts the brain and behavior, but this research is largely focused on adult disruptions.

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Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating.

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