Serum cholesterol levels within the high normal range are associated with better cognitive performance among Chinese elderly

© 2016, Serdi and Springer-Verlag France.Objectives: The association between cognitive function and cholesterol levels is poorly understood and inconsistent results exist among the elderly. The purpose of this study is to investigate the association of cholesterol level with cognitive performance among Chinese elderly. Design: A cross-sectional study was implemented in 2012 and data were analyzed using generalized additive models, linear regression models and logistic regression models. Setting: Community-based setting in eight longevity areas in China. Subjects: A total of 2000 elderly aged 65 years and over (mean 85.8±12.0 years) participated in this study. Measurements: Total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C) concentration were determined and cognitive impairment was defined as Mini-Mental State Examination (MMSE) score≤23. Results: There was a significant positive linear association between TC, TG, LDL-C, HDL-C and MMSE score in linear regression models. Each 1 mmol/L increase in TC, TG, LDL-C and HDL-C corresponded to a decreased risk of cognitive impairment in logistic regression models. Compared with the lowest tertile, the highest tertile of TC, LDL-C and HDL-C had a lower risk of cognitive impairment. The adjusted odds ratios and 95% CI were 0.73(0.62–0.84) for TC, 0.81(0.70–0.94) for LDL-C and 0.81(0.70–0.94) for HDL-C. There was no gender difference in the protective effects of high TC and LDL-C levels on cognitive impairment. However, for high HDL-C levels the effect was only observed in women. High TC, LDL-C and HDL-C levels were associated with lower risk of cognitive impairment in the oldest old (aged 80 and older), but not in the younger elderly (aged 65 to 79 years). Conclusions: These findings suggest that cholesterol levels within the high normal range are associated with better cognitive performance in Chinese elderly, specifically in the oldest old. With further validation, low cholesterol may serve a clinical indicator of risk for cognitive impairment in the elderly.

Improving Indwelling Glucose Sensor Performance: Porous, Dexamethasone-Releasing Coatings that Modulate the Foreign Body Response

Inflammation and the formation of an avascular fibrous capsule have been identified as the key factors controlling the wound healing associated failure of implantable glucose sensors. Our aim is to guide advantageous tissue remodeling around implanted sensor leads by the temporal release of dexamethasone (Dex), a potent anti-inflammatory agent, in combination with the presentation of a stable textured surface.

First, Dex-releasing polyurethane porous coatings of controlled pore size and thickness were fabricated using salt-leaching/gas-foaming technique. Porosity, pore size, thickness, drug release kinetics, drug loading amount, and drug bioactivity were evaluated. In vitro sensor functionality test were performed to determine if Dex-releasing porous coatings interfered with sensor performance (increased signal attenuation and/or response times) compared to bare sensors. Drug release from coatings monitored over two weeks presented an initial fast release followed by a slower release. Total release from coatings was highly dependent on initial drug loading amount. Functional in vitro testing of glucose sensors deployed with porous coatings against glucose standards demonstrated that highly porous coatings minimally affected signal strength and response rate. Bioactivity of the released drug was determined by monitoring Dex-mediated, dose-dependent apoptosis of human peripheral blood derived monocytes in culture.

The tissue modifying effects of Dex-releasing porous coatings were accessed by fully implanting Tygon® tubing in the subcutaneous space of healthy and diabetic rats. Based on encouraging results from these studies, we deployed Dex-releasing porous coatings from the tips of functional sensors in both diabetic and healthy rats. We evaluated if the tissue modifying effects translated into accurate, maintainable and reliable sensor signals in the long-term. Sensor functionality was accessed by continuously monitoring glucose levels and performing acute glucose challenges at specified time points.

Sensors treated with porous Dex-releasing coatings showed diminished inflammation and enhanced vascularization of the tissue surrounding the implants in healthy rats. Functional sensors with Dex-releasing porous coatings showed enhanced sensor sensitivity over a 21-day period when compared to controls. Enhanced sensor sensitivity was accompanied with an increase in sensor signal lag and MARD score. These results indicated that Dex-loaded porous coatings were able to elicit a favorable tissue response, and that such tissue microenvironment could be conducive towards extending the performance window of glucose sensors in vivo.

The diabetic pilot animal study showed differences in wound healing patters between healthy and diabetic subjects. Diabetic rats showed lower levels of inflammation and vascularization of the tissue surrounding implants when compared to their healthy counterparts. Also, functional sensors treated with Dex-releasing porous coatings did not show enhanced sensor sensitivity over a 21-day period. Moreover, increased in sensor signal lag and MARD scores were present in porous coated sensors regardless of Dex-loading when compared to bare implants. These results suggest that the altered wound healing patterns presented in diabetic tissues may lead to premature sensor failure when compared to sensors implanted in healthy rats.

Prospective Estimation of Radiation Dose and Image Quality for Optimized CT Performance

X-ray computed tomography (CT) is a non-invasive medical imaging technique that generates cross-sectional images by acquiring attenuation-based projection measurements at multiple angles. Since its first introduction in the 1970s, substantial technical improvements have led to the expanding use of CT in clinical examinations. CT has become an indispensable imaging modality for the diagnosis of a wide array of diseases in both pediatric and adult populations [1, 2]. Currently, approximately 272 million CT examinations are performed annually worldwide, with nearly 85 million of these in the United States alone [3]. Although this trend has decelerated in recent years, CT usage is still expected to increase mainly due to advanced technologies such as multi-energy [4], photon counting [5], and cone-beam CT [6].

Despite the significant clinical benefits, concerns have been raised regarding the population-based radiation dose associated with CT examinations [7]. From 1980 to 2006, the effective dose from medical diagnostic procedures rose six-fold, with CT contributing to almost half of the total dose from medical exposure [8]. For each patient, the risk associated with a single CT examination is likely to be minimal. However, the relatively large population-based radiation level has led to enormous efforts among the community to manage and optimize the CT dose.

As promoted by the international campaigns Image Gently and Image Wisely, exposure to CT radiation should be appropriate and safe [9, 10]. It is thus a responsibility to optimize the amount of radiation dose for CT examinations. The key for dose optimization is to determine the minimum amount of radiation dose that achieves the targeted image quality [11]. Based on such principle, dose optimization would significantly benefit from effective metrics to characterize radiation dose and image quality for a CT exam. Moreover, if accurate predictions of the radiation dose and image quality were possible before the initiation of the exam, it would be feasible to personalize it by adjusting the scanning parameters to achieve a desired level of image quality. The purpose of this thesis is to design and validate models to quantify patient-specific radiation dose prospectively and task-based image quality. The dual aim of the study is to implement the theoretical models into clinical practice by developing an organ-based dose monitoring system and an image-based noise addition software for protocol optimization.

More specifically, Chapter 3 aims to develop an organ dose-prediction method for CT examinations of the body under constant tube current condition. The study effectively modeled the anatomical diversity and complexity using a large number of patient models with representative age, size, and gender distribution. The dependence of organ dose coefficients on patient size and scanner models was further evaluated. Distinct from prior work, these studies use the largest number of patient models to date with representative age, weight percentile, and body mass index (BMI) range.

With effective quantification of organ dose under constant tube current condition, Chapter 4 aims to extend the organ dose prediction system to tube current modulated (TCM) CT examinations. The prediction, applied to chest and abdominopelvic exams, was achieved by combining a convolution-based estimation technique that quantifies the radiation field, a TCM scheme that emulates modulation profiles from major CT vendors, and a library of computational phantoms with representative sizes, ages, and genders. The prospective quantification model is validated by comparing the predicted organ dose with the dose estimated based on Monte Carlo simulations with TCM function explicitly modeled.

Chapter 5 aims to implement the organ dose-estimation framework in clinical practice to develop an organ dose-monitoring program based on a commercial software (Dose Watch, GE Healthcare, Waukesha, WI). In the first phase of the study we focused on body CT examinations, and so the patient’s major body landmark information was extracted from the patient scout image in order to match clinical patients against a computational phantom in the library. The organ dose coefficients were estimated based on CT protocol and patient size as reported in Chapter 3. The exam CTDIvol, DLP, and TCM profiles were extracted and used to quantify the radiation field using the convolution technique proposed in Chapter 4.

With effective methods to predict and monitor organ dose, Chapters 6 aims to develop and validate improved measurement techniques for image quality assessment. Chapter 6 outlines the method that was developed to assess and predict quantum noise in clinical body CT images. Compared with previous phantom-based studies, this study accurately assessed the quantum noise in clinical images and further validated the correspondence between phantom-based measurements and the expected clinical image quality as a function of patient size and scanner attributes.

Chapter 7 aims to develop a practical strategy to generate hybrid CT images and assess the impact of dose reduction on diagnostic confidence for the diagnosis of acute pancreatitis. The general strategy is (1) to simulate synthetic CT images at multiple reduced-dose levels from clinical datasets using an image-based noise addition technique; (2) to develop quantitative and observer-based methods to validate the realism of simulated low-dose images; (3) to perform multi-reader observer studies on the low-dose image series to assess the impact of dose reduction on the diagnostic confidence for multiple diagnostic tasks; and (4) to determine the dose operating point for clinical CT examinations based on the minimum diagnostic performance to achieve protocol optimization.

Chapter 8 concludes the thesis with a summary of accomplished work and a discussion about future research.