Hip Fracture Incidence in Relation to Age, Menopausal Status, and Age at Menopause: Prospective Analysis

Using data from the UK Million Women Study, Emily Banks and colleagues investigate the relationships between the incidence of hip fracture and a woman's age, menopausal status, and age at menopause

Low urine pH and acid excretion do not predict bone fractures or the loss of bone mineral density: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>The acid-ash hypothesis, the alkaline diet, and related products are marketed to the general public. Websites, lay literature, and direct mail marketing encourage people to measure their urine pH to assess their health status and their risk of osteoporosis.</p> <p>The objectives of this study were to determine whether 1) low urine pH, or 2) acid excretion in urine [sulfate + chloride + 1.8x phosphate + organic acids] minus [sodium + potassium + 2x calcium + 2x magnesium mEq] in fasting morning urine predict: a) fragility fractures; and b) five-year change of bone mineral density (BMD) in adults.</p> <p>Methods</p> <p>Design: Cohort study: the prospective population-based Canadian Multicentre Osteoporosis Study. Multiple logistic regression was used to examine associations between acid excretion (urine pH and urine acid excretion) in fasting morning with the incidence of fractures (6804 person years). Multiple linear regression was used to examine associations between acid excretion with changes in BMD over 5-years at three sites: lumbar spine, femoral neck, and total hip (n = 651). Potential confounders controlled included: age, gender, family history of osteoporosis, physical activity, smoking, calcium intake, vitamin D status, estrogen status, medications, renal function, urine creatinine, body mass index, and change of body mass index.</p> <p>Results</p> <p>There were no associations between either urine pH or acid excretion and either the incidence of fractures or change of BMD after adjustment for confounders.</p> <p>Conclusion</p> <p>Urine pH and urine acid excretion do not predict osteoporosis risk.</p

Safety of aromatase inhibitors in the adjuvant setting

The third-generation aromatase inhibitors (AIs) letrozole, anastrozole, and exemestane are replacing tamoxifen as adjuvant therapy in most postmenopausal women with early breast cancer. Although AIs have demonstrated superior efficacy and better overall safety compared with tamoxifen in randomized controlled trials, they may not provide the cardioprotective effects of tamoxifen, and bone loss may be a concern with their long-term adjuvant use. Patients require regular bone mineral density monitoring, and prophylactic bisphosphonates are being evaluated to determine whether they may protect long-term bone health. AIs decrease the risks of thromboembolic and cerebrovascular events compared with tamoxifen, and the overall rate of cardiovascular events in patients treated with AIs is within the range seen in age-matched, non-breast-cancer populations. AIs are also associated with a lower incidence of endometrial cancer and fewer vaginal bleeding/discharge events than tamoxifen. Compared with tamoxifen, the incidence of hot flashes is lower with anastrozole and letrozole but may be higher with exemestane. Generally, adverse events with AIs are predictable and manageable, whereas tamoxifen may be associated with life-threatening events in a minority of patients. Overall, the benefits of AIs over tamoxifen are achieved without compromising overall quality of life

Mathematical methods for personal positioning and navigation

Computing the position of a personal mobile device based on a mix of various types of measurements requires a wide array of mathematical concepts ranging from optimisation to robust estimation and nonlinear filtering theory. Algorithms for positioning and navigation have surfaced concurrently with the development of new measurement equipment and navigation infrastructure. However, most solutions and algorithms pertain only to certain equipment, involving just a single or few measurement sources. This work synthesises existing techniques into a general framework covering static positioning, filtering, batch positioning and dead reckoning. Measurements are not restricted to any specific technology, equation form or distribution assumption. The static positioning problem, deducing position from a set of simultaneous measurements, is considered first. Parallels between geometric, least squares and statistical approaches are given. The more complex problem of time series estimation can be solved by navigation filters that also make use of all past measurements and information about the system dynamics. Different filter implementations can be derived from the ideal Bayesian filter by choosing different probability density function (pdf) approximation schemes. The standard methods are briefly introduced in this context along with a novel generalisation of a piecewise defined grid filter. Finally, given the wide variety of existing and potential filter implementations, fair and expressive methods for comparing the quality and performance of nonlinear filters are discussed

Mathematical methods for personal positioning and navigation

Computing the position of a personal mobile device based on a mix of various types of measurements requires a wide array of mathematical concepts ranging from optimisation to robust estimation and nonlinear filtering theory. Algorithms for positioning and navigation have surfaced concurrently with the development of new measurement equipment and navigation infrastructure. However, most solutions and algorithms pertain only to certain equipment, involving just a single or few measurement sources. This work synthesises existing techniques into a general framework covering static positioning, filtering, batch positioning and dead reckoning. Measurements are not restricted to any specific technology, equation form or distribution assumption. The static positioning problem, deducing position from a set of simultaneous measurements, is considered first. Parallels between geometric, least squares and statistical approaches are given. The more complex problem of time series estimation can be solved by navigation filters that also make use of all past measurements and information about the system dynamics. Different filter implementations can be derived from the ideal Bayesian filter by choosing different probability density function (pdf) approximation schemes. The standard methods are briefly introduced in this context along with a novel generalisation of a piecewise defined grid filter. Finally, given the wide variety of existing and potential filter implementations, fair and expressive methods for comparing the quality and performance of nonlinear filters are discussed

Consistency of three Kalman filter extensions in hybrid navigation

A filter is consistent if predicted errors are at least as large as actual errors. In this paper, we evaluate the consistency of three filters and illustrate what could happen if filters are inconsistent. Our application is hybrid positioning which is based on signals from satellites and from mobile phone network base stations. Examples show that the consistency of a filter is very important. We evaluate three filters: EKF, EKF2 and PKF. Extended Kalman Filter (EKF) solves the filtering problem by linearizing functions. EKF is very commonly used in satellite-based positioning and it has also been applied in hybrid positioning. We show that nonlinearities are insignificant in satellite measurements but often significant in base station measurements. Because of this, we also apply Second Order Extended Kalman Filter (EKF2) in hybrid positioning. EKF2 is an elaboration of EKF that takes into consideration the nonlinearity of the measurement models. The third filter is called Position Kalman Filter (PKF), which filters a sequence of static positions and velocities. We also check what kind of measurement combinations satisfy CGALIES and FCC requirements for location.Peer reviewe