Classification of Rest and Active Periods in Actigraphy Data using PCA

In this paper we highlight a clustering algorithm for the purpose of identifying sleep and wake periods directly from actigraphy signals. The paper makes use of statistical Principal Component Analysis to identify periods of rest and activity. The aim of the proposed methodology is to develop a quick and efficient method to determine the sleep duration of an individual. In addition, a robust method that can identify sleep periods in the accelerometer data when duration, time of day varies by individual. A selected group of 10 individual\u27s sensor data consisting of actigraphy from an accelerometer (3-axis), near body temperature, and lux sensors from a single GENEActiv watch worn on the non-dominant hand. The actigraphy of each individual was collected 24 hours a day for a period spanning 80 days. We highlight that a simple data preprocessing stage followed with a 2 phase clustering method provides results that align with previously validated methodologies

Xeroderma Pigmentosum Group C Deficiency Alters Cigarette Smoke DNA Damage Cell Fate and Accelerates Emphysema Development

Cigarette smoke (CS) exposure is a major risk factor for the development of emphysema, a common disease characterized by loss of cells comprising the lung parenchyma. The mechanisms of cell injury leading to emphysema are not completely understood but are thought to involve persistent cytotoxic or mutagenic DNA damage induced by CS. Using complementary cell culture and mouse models of CS exposure, we investigated the role of the DNA repair protein, xeroderma pigmentosum group C (XPC), on CS-induced DNA damage repair and emphysema. Expression of XPC was decreased in mouse lungs after chronic CS exposure and XPC knockdown in cultured human lung epithelial cells decreased their survival after CS exposure due to activation of the intrinsic apoptosis pathway. Similarly, cell autophagy and apoptosis were increased in XPC-deficient mouse lungs and were further increased by CS exposure. XPC deficiency was associated with structural and functional changes characteristic of emphysema, which were worsened by age, similar to levels observed with chronic CS exposure. Taken together, these findings suggest that repair of DNA damage by XPC plays an important and previously unrecognized role in the maintenance of alveolar structures. These findings support that loss of XPC, possibly due to chronic CS exposure, promotes emphysema development and further supports a link between DNA damage, impaired DNA repair, and development of emphysema

The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population

BACKGROUND: As the use of bundled care payment models has become widespread in neurosurgery, there is a distinct need for improved preoperative predictive tools to identify patients who will not benefit from prolonged hospitalization, thus facilitating earlier discharge to rehabilitation or nursing facilities.OBJECTIVE: To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition.METHODS: Patients undergoing elective posterior lumbar fusion from June 2016 to February 2017 were prospectively enrolled. RAPT scores and discharge outcomes were recorded for patients aged 50 yr or more (n = 432). Logistic regression analysis was used to assess the ability of RAPT score to predict discharge disposition. Multivariate regression was performed in a backwards stepwise logistic fashion to create a binomial model.RESULTS: Escalating RAPT score predicts disposition to home (P \u3c .0001). Every unit increase in RAPT score increases the chance of home disposition by 55.8% and 38.6% than rehab and skilled nursing facility, respectively. Further, RAPT score was significant in predicting length of stay (P = .0239), total surgical cost (P = .0007), and 30-d readmission (P \u3c .0001). Amongst RAPT score subcomponents, walk, gait, and postoperative care availability were all predictive of disposition location (P \u3c .0001) for both models. In a generalized multiple logistic regression model, the 3 top predictive factors for disposition were the RAPT score, length of stay, and age (P \u3c .0001, P \u3c .0001 and P = .0001, respectively).CONCLUSION: Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition