Association of the combination of obstructive sleep apnea risk and sleep duration with ideal cardiovascular health metrics in patients undergoing hemodialysis.

BACKGROUND: The purpose of this study was to explore the separate and combined associations of obstructive sleep apnea (OSA) risk and sleep duration with ideal cardiovascular health metrics in hemodialysis (HD) patients. METHODS: 470 HD participants (average: 59.48 ± 12.89 y, 281 men) were included in this study. Sleep duration was measured as self-reported average sleep time during the previous month. The OSA risk was assessed using the STOP-BANG questionnaire. Participants were divided into three groups based on the number of ideal cardiovascular health (CVH) metrics: 0-2,3-4, and 5-7. Ordinal logistic regression was conducted to model the associations of CVH metrics with sleep duration, OSA risk, and their combined effects by adjusting for specific covariates. RESULTS: After adjusting for covariates, short sleep duration (< 7 h) (OR = 0.53; 95% CI [ 0.30, 0.92]) and OSA risk (OR = 0.58; 95% CI [0.32, 0.83]) were negatively associated with better CVH (ideal vs. intermediate; intermediate vs. poor), respectively. For HD patients with both short sleep duration and OSA risk, the odds of ideal CVH metrics were reduced by 72% (odds ratio 0.28 [95% CI 0.13, 0.60]). CONCLUSIONS: Short sleep duration and OSA risk are separately and jointly associated with poor CVH in hemodialysis patients. Suitable interventions for sleep may minimize the risk of developing cardiovascular disease.

Relationships between muscle strength, lung function, and cognitive function in Chinese middle-aged and older adults: A study based on the China health and retirement longitudinal study (CHARLS).

OBJECTIVE: As the population ages, concerns about cognitive decline have become increasingly relevant in medical consultations. This study aims to analyze the interaction between muscle strength, lung function, and cognitive function in Chinese middle-aged and older adults, providing a theoretical basis for better prevention of cognitive decline. METHODS: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) wave 3, including 13 716 participants aged 45 years or older. Cognitive function was assessed through two dimensions, resulting in a total score ranging from 0 to 31 points, with higher scores indicating better cognitive function. Muscle strength was measured using normalized grip strength and chair-standing time, while lung function was evaluated using peak expiratory flow (PEF). RESULTS: Total cognitive function scores exhibited significant correlations with grip strength, chair-standing time, and PEF. Muscle strength and lung function demonstrated significant associations with cognitive function, with lung function emerging as a notable mediating factor. This relationship persisted even after adjusting for potential confounding variables. Specifically, PEF played a substantial mediating role in linking grip strength to cognitive function scores (estimated indirect effect = 0.0132, boot-strapped standard error = 0.0015, boot-strapped standard 95% confidence interval = 0.0104, 0.0162). Additionally, PEF served as a significant mediator in the association between chair-standing time and cognitive function scores (estimated indirect effect = -0.0204, boot-strapped standard error = 0.0023, boot-strapped standard 95% confidence interval = -0.0251, -0.0159). CONCLUSION: The study highlights the importance of addressing declines in muscle strength and lung function to identify risk factors associated with cognitive function. Understanding these relationships can provide insights into potential pathways linking these variables and may aid in better prevention of cognitive decline. Further long-term longitudinal cohort studies are needed to explore the causality between these factors.

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics.

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.