Cigarette smoke has been documented to be related to the development of cancer. However, the exact mechanism for the carcinogenic action of cigarette smoke is still unknown. Nicotine is recognized to be the major compound in cigarette smoke and has been suggested to play a role in oral cancer via a cyclooxygenase (COX)/ prostaglandin-dependent pathway. This study was designed to evaluate the action of nicotine in the oral cancer cell and to further examine whether COX-2 is responsible for expression of tumor-associated angiogenic vascular endothelial growth factor (VEGF) in vitro. Viability of human oral squamous cancer cells (BHY) was measured using MTT assay. Protein expression was determined by Western blot and immunoassay kits. We found that exposure of BHY cells to nicotine (200 µg/mL for 6 hours) resulted in 2.9-fold induction of COX-2 expression as well as a 4-fold increase in VEGF levels compared with a control group. Pretreatment with celecoxib inhibited nicotine-induced change in the expression of VEGF and COX-2. The results suggest that stimulation of COX-2 and VEGF expression can contribute as important factors in the tumorigenic action of nicotine in oral cancer progression. This effect can be blocked by celecoxib, suggesting an interaction of nicotine and COX-2 pathways.

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Introduction Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer’s disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD. Method Community-dwelling older adults (N = 95; 62–90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD. Results In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value. Discussion AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

INTRODUCTION: Sleep disorder is often the first symptom of age-related cognitive decline in Mild Cognitive Impairment (MCI), or early Alzheimer’s disease. Patient or family sleep complaints in primary care do not reliably lead to screening for sleep or cognitive loss. In this study, poor sleep and arousability were examined using movement arousals, a novel biobehavioral marker of cumulative sleep loss, identified by periodic (circa 4 min) sleep movements (SM). We report that SM events trigger respiratory upregulation (RR) in healthy, but not in MCI-related, sleep. Time latency (TL) between SM-RR events is proposed as a marker of sleep loss and potentially of neurodegeneration associated with cognitive loss in MCI.METHOD. Community-dwelling older adults (N=95; 62-90 years) were tested in the home bed for two days on an “under the sheets” mattress overlay with high sensitivity for respiration and all movement, including micro-movements of SM. Wrist actigraphy (7 days) and standard sleep self-reports were collected as well. A suite of neurocognitive testing identified three groups: Normal Cognition (NC; n=45); clinic diagnosed MCI (MCI-DX; n= 17); and MCI-Consensus determined by an expert panel (MCI-CON; n=33 consensus or pre-clinical MCI) groups.RESULTS: In adjusted cohort analyses, sleep fragmentation (SF) and WASO predicted poorer memory performance selectively. Actigraphy revealed greater sleep latency (SL; p<.008), sleep duration (p<.01) and SF (p=.008) in MCI-DX compared to NC groups. Longer SL and poor sleep were associated with depression which was more prevalent in both MCI groups than NC. Neural Networks AI methods were applied to discriminate MCI and NC cases using the TL metric between SM-RR coupling events. ROC diagnostics were applied which identified MCI vs. NC cases with 87% sensitivity; 89% specificity; and 88% positive predictive value. DISCUSSION: MCI cognitive phenotype was detected with a novel sleep biomarker TL, associated with the tight gap between SM-RR coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Movement arousals are less effective in initiating respiratory upregulation in MCI which suggests a potential mechanism for neurodegenerative changes and cognitive loss in early MCI.

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