Relationship between brain—derived neurotrophic factor and cognitive function of obstructive sleep apnea/hypopnea syndrome patients

Objective:To determine the relationship between the blood serum brain-derived neurotrophic factor（BDNF） level and cognitive function deterioration in patients with obstructive sleep apnea/ hypopnea syndrome（OSAHS）,and to explore the possible mechanism of cognitive impairment. Methods:Twenty-eight male OSAHS patients and 14 normal males（as controls） were enrolled in the study.Polysomnography and the Montreal cognitive assessment（MoCA） were conducted. The blood serum BDNF levels were measured using ELISA.Results:The OSAHS group had significantly decreased blood serum BDNF levels compared with the control group（t=-10.912, P=0.000）.The blood serum BDNF level of the subjects was significantly positively associated with the MoCA score（r=0.544,P=0.000）,significantly negatively associated with the apneahypopnea index（AHI） and shallow sleep（S1+S2）（AHI:r=-0.607,P=0.000;S1+S2:r =-0.768,P= 0.000）,and significantly positively associated with the lowest SaO2（LSO）,slow wave sleep（S3+S4）, and rapid eye movement sleep（REM）（LSO:r=0.566,P = 0.000;S3+S4:r=0.778,P=0.000;REM: r=0.575,P=0.000）.Conclusions:OSAHS patients have significantly decreased blood serum BDNF levels compared with the control.Nocturnal hypoxia as well as the deprivation of slow wave sleep and REM may lead to the decreased serum BDNF level of OSAHS patients.This decreased blood serum BDNF level may contribute to the cognitive impairment in OSAHS.

Association between knowledge and risk for cardiovascular disease among older adults: A cross-sectional study in China

Objectives:This study aimed to describe cardiovascular risk and cardiovascular disease(CVD)knowledge among older adults,and further explore the association between knowledge and risk.Methods:In this cross-sectional study,we enrolled 1120 older adults who received physical examination in health centers.The participants were interviewed to obtain their behavioral risk factors related to CVD and clinical characteristics.A risk prediction chart was used to predict participants'cardiovascular risk based on clinical characteristics and behavioral risk factors.Participants'CVD knowledge was collected with a pretested knowledge questionnaire.Results:Among the 1120 participants,240(21.4%)had low cardiovascular risk,353(31.5%)had moderate cardiovascular risk,527(47%)had high and very high cardiovascular risk.The knowledge level about CVD among 0.8%of the 1120 participants was good while that of 56.9%was poor.Lower CVD knowledge level,older age,lower income,and lower educational level were the independent factors of higher cardiovascular risk level.Conclusions:This study highlights the need to reduce the cardiovascular risk among older adults.CVD knowledge should be considered when developing health interventions.

A NEW CONSTRAINTS IDENTIFICATION TECHNIQUE-BASED QP-FREE ALGORITHM FOR THE SOLUTION OF INEQUALITY CONSTRAINED MINIMIZATION PROBLEMS

In this paper, we propose a feasible QP-free method for solving nonlinear inequality constrained optimization problems. A new working set is proposed to estimate the active set. Specially, to determine the working set, the new method makes use of the multiplier information from the previous iteration, eliminating the need to compute a multiplier function. At each iteration, two or three reduced symmetric systems of linear equations with a common coefficient matrix involving only constraints in the working set are solved, and when the iterate is sufficiently close to a KKT point, only two of them are involved. Moreover, the new algorithm is proved to be globally convergent to a KKT point under mild conditions. Without assuming the strict complementarity, the convergence rate is superlinear under a condition weaker than the strong second-order sufficiency condition. Numerical experiments illustrate the efficiency of the algorithm.

Parallel Algorithms for Large-scale Linearly Constrained Minimization Problem

In this paper, two PVD-type algorithms are proposed for solving inseparable linear constraint optimization. Instead of computing the residual gradient function, the new algorithm uses the reduced gradients to construct the PVD directions in parallel computation, which can greatly reduce the computation amount each iteration and is closer to practical applications for solve large-scale nonlinear programming. Moreover, based on an active set computed by the coordinate rotation at each iteration, a feasible descent direction can be easily obtained by the extended reduced gradient method. The direction is then used as the PVD direction and a new PVD algorithm is proposed for the general linearly constrained optimization. And the global convergence is also proved.

SEQUENTIAL SYSTEMS OF LINEAR EQUATIONS ALGORITHM FOR NONLINEAR OPTIMIZATION PROBLEMS-INEQUALITY CONSTRAINED PROBLEMS

Presents information on a study which proposed a superlinearly convergent algorithm of sequential systems of linear equations or nonlinear optimization problems with inequality constraints. Assumptions; Discussion on lemmas about several matrices related to the common coefficient matrix F; Strengthening of the regularity assumptions on the functions involved; Numerical experiments.