PLoS ONE最新文献

Background: Osteoporosis is a skeletal disorder characterized by reduced bone mass and increased fracture risk. With the aging global population, its prevalence is rising, posing a significant public health challenge. Physical activity is considered an effective intervention to reduce osteoporosis risk, but the role of grip strength as a mediator remains underexplored.

Methods: Data from the English Longitudinal Study of Ageing (ELSA) and the Health and Retirement Study (HRS) were analyzed using generalized linear mixed models (GLMM) and mediation analysis to explore the impact of physical activity on osteoporosis and the role of grip strength. Subgroup analyses accounted for age, gender, and confounding factors.

Results: The prevalence of osteoporosis was 6.3% in ELSA and 14.1% in HRS. A significant negative correlation was found between physical activity and osteoporosis in both groups (ELSA: OR = 0.234, P < 0.001; HRS: OR = 0.638, P = 0.028). In those aged ≥65, physical activity had a more pronounced effect (OR = 0.478, P < 0.001). Women showed greater benefit. Mediation analysis in the ELSA group revealed that grip strength mediated 28.3% of the effect of physical activity on osteoporosis (ACME = -0.007, P < 0.001).

Conclusion: Physical activity, especially resistance training, reduces osteoporosis incidence by enhancing muscle strength, with grip strength playing a mediating role. These findings highlight the importance of physical activity, particularly in older women, for osteoporosis prevention.

Background: Coronary heart disease is the leading cause of global mortality, imposing significant health and economic burdens. Cardiac rehabilitation, including physical activity, can reduce coronary heart disease-related morbidity and mortality. We tested whether the addition of a behaviour change intervention to cardiac rehabilitation could promote and maintain physical activity achieved during cardiac rehabilitation, beyond standard care timeframes.

Methods: A cluster randomised controlled trial was conducted across six community-based maintenance stage cardiac rehabilitation classes. A total of 96 participants (mean age 65.04 ± 8.38 years; 75% male) received either standard care or a behaviour change intervention, with physical activity, measured with an ActiGraph GT3X+ accelerometer as the primary outcome.

Results: No significant differences in daily minutes of moderate-to-vigorous physical activity and steps per day, or any secondary outcomes, including self-rated health, quality of life, and mental wellbeing, were observed between the intervention and control groups at six months follow-up. These findings suggests that the behaviour change intervention did not significantly impact physical activity or health outcomes during maintenance cardiac rehabilitation. This may be attributed to high baseline physical activity levels among participants, and the extended cardiac rehabilitation support provided to both groups, potentially masking any intervention effects.

Conclusion: A behaviour change intervention added to standard maintenance stage cardiac rehabilitation did not improve physical activity or health outcomes. However, continued access to cardiac rehabilitation sustained high physical activity levels. Future research should disentangle the independent effects of behaviour interventions and ongoing cardiac rehabilitation support.

Trial registration: ClinicalTrials.gov NCT05705310.

Background: Sepsis represents a significant global health challenge, contributing to considerable morbidity, mortality, and healthcare costs. Integrating Chinese botanical drug injections (CBDIs) with Western Medical Treatments (WMT) has been increasingly recognized for its enhanced therapeutic effects in sepsis management. This Bayesian network meta-analysis aims to identify the optimal combination regimen of CBDIs and WMT for sepsis therapy.

Methods: A comprehensive literature search was conducted across eight electronic databases to identify Randomized Controlled Trials (RCTs) relevant to our study criteria, spanning from their inception until January 1, 2024. The quality of included studies was rigorously assessed using the Cochrane Collaboration's Risk of Bias 2 (ROB 2) tool. Data synthesis and analysis were performed utilizing R 4.1.2 and Stata 17.0 software. Additionally, publication bias was assessed through the construction of funnel plots.

Results: This network meta-analysis assessed 72 RCTs involving 6,351 participants to evaluate the effectiveness of seven CBDIs in conjunction with WMT. It found Huangqi injection to be the most effective in improving APACHE II scores. Tanreqing injections significantly reduced procalcitonin (PCT) levels, with particularly superior. Shenmai injection was most effective in decreasing C-reactive protein (CRP) levels. In terms of reducing tumor necrosis factor-alpha (TNF-α), Shenmai injection with WMT showed the best results. Xuebijing injection stood out in lowering white blood cell counts (WBC). Huangqi injection was noted for its best effectiveness in the 28-day mortality rates.

Conclusion: The therapeutic efficacy of CBDIs in treating sepsis is underscored by our research findings, wherein certain botanical drugs exhibit heightened efficacy and safety attributes. The incorporation of these alternative modalities into contemporary sepsis management paradigms is advocated by the outcomes of our investigation. Nonetheless, rigorous, large-scale trials are imperative to substantiate and enhance these preliminary discoveries.

[This corrects the article DOI: 10.1371/journal.pone.0333637.].

Background and aims: The macronutrient composition of donor human milk (DHM) can vary substantially due to several factors such as maternal age, diet, and lactation duration. However, consistent macronutrient levels in DHM facilitate the administration of the required amounts to preterm infants. The current pooling strategy at most human milk banks combines milk from different batches from a single donor. This study aims to stabilize the macronutrient quality of DHM by pooling milk from different donors by utilizing machine learning prediction and optimisation techniques.

Methods: The current pooling strategy is compared with a new theoretical approach that pools milk batches from up to 5 donors. To predict the crude protein and energy content, we used the following variables: body mass index, the donor's diet (vegetarian or non-vegetarian), maternal age, full-term or preterm delivery, lactation stage, and volume pumped. These predictions are then used within an optimisation model to create milk pools that minimize the deviations from the target macronutrient levels (1.0 g protein/100 mL and 70 kcal/100 mL).

Results: The prediction model is based on 2236 created single-donor pools from 480 donors. Random forest regression models provided the most accurate predictions of macronutrient content. The new pooling strategy using multiple donors shows reduced deviations from target values compared to the current single-donor approach (average total absolute deviation 0.402 versus 0.664).

Conclusion: This study proves the potential of data-driven methods to improve operational efficiency in human milk banks, and improving the consistency of donor human milk.

Glass nanostructure embossing is a critical manufacturing process for producing high-precision glass components used in optics and electronics. However, controlling the deformation and fracture mechanisms of glass during embossing remains a significant challenge due to its complex behavior, which can vary between solid and liquid-like states under different conditions. To investigate these mechanisms, this study employs large-scale molecular dynamics (MD) simulations that mirror experimental conditions. The simulations reveal how compressive forces near the mold interface lead to densification and lateral flow of the glass, while tensile stresses at the edges can promote crack formation. Additionally, the study examines the role of strain rate in crack propagation, showing that higher strain rates accelerate failure. These findings offer a deeper understanding of the atomic-level behavior of glass during embossing, highlighting key factors such as stress distribution, energy evolution, and material flow. By bridging the gap between molecular simulations and experimental observations, this work provides valuable insights into optimizing embossing conditions. The results can be applied to improve the quality of glass nanostructures, reducing defects and ensuring the mechanical robustness of glass-based devices.

Background: Mental health challenges, including insomnia, anxiety, and depression, are common among antenatal women and can affect both maternal and fetal outcomes. This study explores the determinants of these conditions in antenatal women in China, aiming to inform the design of mental health interventions and preventive strategies for this population.

Methods: A cross-sectional survey design was employed in this hospital-based study targeting antenatal women at a tertiary hospital in China, conducted from May 2024 to March 2025 during routine antenatal visits. Validated questionnaires assessed insomnia, anxiety, and depression. Multiple linear and logistic regression analyses identified factors associated with symptom severity and occurrence, while Structural Equation Modeling (SEM) was used to explore the relationships and mediating effects between biological and social factors, insomnia, anxiety, and depression.

Results: The participants had a mean age of 31.48 ± 6.94 years, with most being married (90.7%), living in urban areas (74.3%), and having undergraduate/college education (45.8%). Significant predictors of insomnia included geographical location, with those in central (OR = 1.818, 95% CI: 1.500-2.204) and southern areas (OR = 1.368, 95% CI: 1.143-1.637) showing higher odds compared to the northern region. Living in rural areas (OR = 0.796, 95% CI: 0.718-0.845) and higher education levels (OR = 1.544, 95% CI: 1.012-2.355) were associated with lower odds. Other significant factors included the number of live births and household composition. For anxiety, older age (OR = 0.955, 95% CI: 0.937-0.973) and rural living (OR = 0.675, 95% CI: 0.539-0.845) decreased odds, while living with others (OR = 3.726, 95% CI: 2.463-5.639) increased the risk. Significant predictors of depression included geographical location (central areas: OR = 1.508, 95% CI: 1.106-2.055), income level, and number of live births. The logistic regression Area Under the Curve (AUC) were 0.579 for insomnia, 0.603 for anxiety, and 0.567 for depression. SEM demonstrated an excellent model fit (CFI = 0.994, TLI = 0.999, RMSEA = 0.014). Insomnia was strongly predicted by geographic location, education, and number of live births. In turn, insomnia significantly predicted anxiety (β = 0.741) and depression (β = 0.138). The model explained 54.9% of the variance in anxiety and 70.6% of the variance in depression, indicating partial mediation.

Conclusion: This study identifies the multidimensional factors influencing antenatal women's insomnia, anxiety, and depression in China, particularly highlighting the roles of geographical location, current living situation, and household composition. These factors were consistently associated with all three outcomes. Targeted interventions targeting these specific risk factors are recommended to improve the mental health of antenatal women.

The 28-day compressive strength of cement is a key indicator for assessing cement quality. To overcome the time delays inherent in manual testing, this paper proposed a 28-day cement strength fusion prediction method based on a Transformer feature extractor and an XGBoost meta-learner. This method first encoded the physicochemical multi-source strength variables through the Transformer embedding layer, then calculated the attention scores using the multi-head attention mechanism to allocate weights dynamically. Next, XGBoost's gradient boosting tree structure and regularization techniques were employed to enhance the robustness of the cement strength prediction model in small-sample scenarios. Finally, the method was validated using real-world 28-day strength testing data from cement plants. The results indicated that, compared to the model without feature extraction, the regression model's R2 increased by 5.62%, and its RMSE decreased by 22.33% after applying Transformer feature extraction. Furthermore, when compared with other small-sample models, XGBoost achieved the highest average R2 of 0.93 in 5-fold cross-validation (CV). Its training efficiency, robustness to noise, and ability to handle feature missingness outperformed other meta-learners. Compared to other methods, TF-XGBoost achieved the highest average R2 of 0.94 in 25 Monte Carlo (MC) CVs, providing the best fit. The method proposed in this paper demonstrates higher accuracy, better generalization, and greater stability, offering a new approach for the prediction of cement 28-day strength with small sample sizes.

This study develops and empirically tests an integrated framework that explains how financial socialisation, technological factors, and financial capability jointly shape financial behaviour and in an emerging economy context. Using data from 306 Vietnamese adults, the study applies Partial Least Squares Structural Equation Modelling to assess direct, mediating, and moderating effects. The results show that both family financial socialization and artificial intelligence significantly enhance financial behaviour and financial well-being, with financial behaviour mediating these relationships. Artificial intelligence exerts a stronger influence on financial behaviour than family financial socialisation, while its impact on financial well-being operates primarily through behavioural pathways. Financial literacy and digital trust significantly strengthen the effect of artificial intelligence on financial behaviour, although the moderating effects are relatively modest. Financial well-being is positioned as the ultimate outcome of the model, and the findings confirm that improvements in well-being are largely driven by behavioural adjustments rather than direct technological exposure alone. The study offers theoretical contributions by integrating social, technological, and capability-based elements into a unified financial well-being framework and highlights the conditional roles of digital trust and financial literacy in shaping AI-driven financial behaviour. It also provides practical implications for financial education and responsible digital finance adoption to enhance financial resilience and long-term well-being.

The two main pathways for hormonal stress response are the hypothalamic-pituitary- adrenal (HPA) axis and the sympathoadrenal (SA) axis. The HPA axis produces and secretes cortisol, while the SA axis produces and secretes the fast-acting catecholamines, epinephrine and norepinephrine, which in turn stimulate cortisol. Since it is difficult to consistently measure or monitor their concentrations in plasma, mathematical modeling of the catecholamines and their connection to cortisol can provide more information about the acute stress response. Previous mathematical models have simulated the dynamics of the HPA axis, but a model of the SA axis has not been created nor one with the combined effects of the HPA and SA axes. We propose an extension of Bangsgaard and Ottesen's differential equation-based HPA axis model that includes the SA axis [1]. We performed sensitivity analysis using Morris screening and estimated model parameters using constrained optimization with respect to time series data of cortisol and catecholamine dynamics under acute physical stress. After subject-specific parameter estimation, the proposed model that includes both the HPA and SA axes shows qualitative agreement with the collected data.