Search Results (62)

Objective: This study investigated the association between paternal preconception paternal body mass index (BMI) categories and physical/neurodevelopmental outcomes in Chinese small-for-gestational-age (SGA) children. Methods: A prospective cohort study enrolled 412 singleton SGA infants born at Peking University People’s Hospital in 2020–2022. Fathers were stratified into underweight, normal-weight, overweight, and obese groups. Follow-up assessments at 24–36 months evaluated growth parameters weight, height, BMI Z-scores and neurodevelopment using the Ages and Stages Questionnaire-3 (ASQ-3) and ASQ: Social–Emotional (ASQ:SE). Multivariable regression was adjusted for paternal covariates. Results: In SGA offspring, paternal underweight correlated with lower birth weights vs. normal/obese paternal BMI and the highest severe SGA rates. Prospective monitoring identified elevated BMI Z-scores (ΔZ = +0.40) and 8.7-fold heightened obesity risk in the paternal obesity group versus normal-weight counterparts. Neurodevelopmental evaluations demonstrated gross motor impairments in both underweight (ΔZ = −0.22) and obese paternal subgroups (ΔZ = −0.25) compared with the normal-weight group, with the obesity cohort additionally exhibiting problem-solving deficiencies (ΔZ = −0.19). The paternal obesity group manifested three-fold greater likelihood of social–emotional delays than the normal-weight group. The underweight and obese paternal groups showed 3.46-fold and 2.73-fold higher probabilities of gross motor deficits, respectively, while obesity was linked to 3.27-fold elevated problem-solving impairment risk-all comparisons versus normal paternal BMI. Overweight status showed no significant links to growth or neurodevelopmental outcomes. Normal-weight fathers had lower risks of obesity and neurodevelopmental issues. Conclusions: This study revealed U-shaped paternal BMI–neurodevelopment links in SGA offspring. Paternal obesity raised offspring obesity/neurodevelopmental risks, while underweight linked to severe SGA and motor deficits, highlighting paternal weight optimization’s modifiable role. Full article

Background/Objectives: A large percentage of schoolchildren are children with overweight or obese, and weight gain over time increases the risk of poor health later in life. Motor coordination may be a protective factor, enabling young people to participate in healthy physical activities. However, it is unknown when the relationship between motor coordination and weight first emerges, nor whether it is evident across the body mass index (BMI) spectrum. The aim of this study was to explore what happens with coordination skills [i.e., locomotor coordination (LC), visuomotor coordination (VC), foot object control coordination (FOCC), hand object control coordination (HOCC), global motor coordination (GMC)] applying the 3JS battery, according to age (6 to 11 years) and BMI (normal weight, overweight or obesity) in a sample of 688 Primary Education participants (8.71 ± 1.66 years; 48.8% boys) from Galicia (Spain). Methods: To analyze the differences in the variables of the 3JS battery between age (6–11 years) and BMI (normal weight vs. overweight vs. obesity), they were evaluated using a multivariate analysis of covariance (MANCOVA), introducing the sex category (boy-girl) as a covariate to avoid possible confounding factors. Results: After the application of the 3JS Battery, statistically significant differences were observed depending on age [LC (p < 0.001); VC (p < 0.001); FOCC (p < 0.001); HOCC (p < 0.001); GMC (p < 0.001)] and BMI [LC (p < 0.001); VC (p = 0.008); HOCC (p < 0.001); GMC (p < 0.001)]. No statistically significant differences were found in the interactions between age and BMI (p > 0.05). Conclusions: Locomotor coordination increases progressively with age, both globally and in each of the manifestations, and this is greater the older the schoolchildren are. Schoolchildren with normal weight compared to schoolchildren with overweight or obesity have better global motor coordination, locomotor coordination, global control of objects, and control with the hand. Full article

A comprehensive theoretical investigation of shape coexistence and transition phenomena in the neutron-deficient nucleus ${}^{66}\mathrm{Se}$, using complementary microscopic and phenomenological approaches, is presented. The analysis employs the Covariant Density Functional Theory with the Density-Dependent Meson Exchange Model interaction to map the potential energy surface. This microscopic foundation is complemented by calculations using the Bohr–Mottelson Hamiltonian with a sextic oscillator potential, specifically adapted to explore shape coexistence between spherical and $\gamma$-unstable configurations. The latter model successfully reproduces the experimental energy spectrum, including the critical low-lying $0_2^{+}$ state at 1226 keV—a key signature of shape coexistence. An analysis of probability density distributions indicates a distinctive manifestation of shape coexistence wherein different shapes exist without significant mixing in the states. These findings provide crucial insights into the structural dynamics of ${}^{66}\mathrm{Se}$ and establish it as an important case study for understanding shape evolution in neutron-deficient nuclei beyond the $N=Z$ line. Full article

Introduction: Alzheimer’s disease (AD), and more specifically late-onset Alzheimer’s disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients’ quality of life. The current challenge is to accurately identify at-risk individuals before the manifestations of the first symptoms of AD. Methods and results: Here, we present an improved model for LOAD risk prediction, which applies the k-nearest neighbors (KNN) algorithm. We have achieved a sensitivity of 0.80 and an area under the curve (AUC) of 0.71, which represents a high performance especially when compared to an AUC of 0.66 reported previously in 2019 using a KNN model. Discussion: The application of a mathematical model that combines genetic and clinical covariates showed a good prediction of the AD/LOAD risk, with the higher weight being the polygenic genetic risk, APOE haplotype, and age. Compared to previous studies, our model integrates and correlates genetic prediction together with phenotypic information by fine-tuning the parameters of the model in order to achieve the best performance. This algorithm can be used in the general population and does not require the manifestation of any symptoms for its effective application. Thus, we present here an advanced model for risk prediction of LOAD. Full article

Background/Objectives: The recent COVID-19 pandemic posed a significant psychological challenge for healthcare workers. Resilience and the extent of psychological stress varied across professional groups and individual circumstances. This study aims to longitudinally capture the trajectories of psychological stress and resilience among medical personnel during the pandemic and identify various contributing factors. Methods: Over a period of three years, healthcare workers from five locations (Bonn, Cologne, Ulm, Erlangen, and Dresden) were surveyed regarding their psychological stress (PHQ-4) and other aspects of mental health. Data were collected at five different points during the pandemic. Using Growth Mixture Modeling (GMM), various stress trajectories during the crisis were modeled without initial adjustment for covariates to allow for an unbiased identification of latent classes. Differences in demographic and occupational factors (e.g., age, gender, profession) were analyzed across the identified trajectory groups in subsequent steps. Results: The application of GMM revealed three distinct profiles of psychological stress and resilience among the respondents, largely consistent with the literature. The largest group was the ‘resilience’ group (81%), followed by the ‘recovery’ (10%) and ‘delayed’ groups (9%). Group membership was consistent with self-reported trajectories over the course of the pandemic. It was not possible to predict individual trajectories based on the results of a short resilience questionnaire (RS-5). Conclusions: The COVID-19 pandemic had multiple psychological impacts on healthcare workers, manifesting in clearly differentiated group trajectories of distress over time. While a majority of respondents in this sample exhibited a stable trajectory with low distress, other groups showed varying stress responses over time. These findings highlight the necessity of longitudinal approaches to understand the complex interplay of stressors and coping mechanisms during prolonged crises. Full article

Evapotranspiration (ET) plays a pivotal role in linking the water and carbon cycles between the land and atmosphere, with latent heat flux (LE) representing the energy manifestation of ET. Due to adverse meteorological conditions, data quality filtering, and instrument malfunctions, LE measured by the eddy covariance (EC) is temporally discontinuous at the hourly and daily scales. Machine-learning (ML) models effectively capture the complex relationships between LE and its influencing factors, demonstrating superior performance in filling LE data gaps. However, the selection of features in ML models often relies on empirical knowledge, with identical features frequently used across stations, leading to reduced modeling accuracy. Therefore, this study proposes an LE gap-filling model (SHAP-AWF-BO-LightGBM) that combines the Shapley additive explanations adaptive weighted fusion method with the Bayesian optimization light gradient-boosting machine algorithm. This is tested using data from three stations in the Heihe River Basin, China, representing different plant functional types. For 30 min interval missing LE data, the RMSE ranges from 17.90 W/m² to 20.17 W/m², while the MAE ranges from 10.74 W/m² to 14.04 W/m². The SHAP-AWF method is used for feature selection. First, the importance of SHAP features from multiple ensemble-learning models is adaptively weighted as the basis for feature input into the BO-LightGBM algorithm, which enhances the interpretability and transparency of the model. Second, data redundancy and the cost of collecting other feature data during model training are reduced, improving model calculation efficiency (reducing the initial number of features of different stations from 42, 46, and 48 to 10, 15, and 8, respectively). Third, under the premise of ensuring accuracy as much as possible, the gap-filling ratio for missing LE data at different stations is improved, and the adaptability of using only automatic weather station observation is enhanced (the improvement range is between 7.46% and 11.67%). Simultaneously, the hyperparameters of the LightGBM algorithm are optimized using a Bayesian algorithm, further enhancing the accuracy of the model. This study provides a new approach and perspective to fill the missing LE in EC measurement. Full article

The covariant Hamilton–Jacobi formulation of electrodynamics is systematically derived from the first-order (Palatini-like) Lagrangian. This derivation utilizes the De Donder–Weyl covariant Hamiltonian formalism with constraints incroporating generalized Dirac brackets of forms and the associated polysymplectic reduction, which ensure manifest covariance and consistency with the field dynamics. It is also demonstrated that the canonical Hamilton–Jacobi equation in variational derivatives and the Gauss law constraint are derived from the covariant De Donder–Weyl Hamilton–Jacobi formulation after space + time decomposition. Full article

Background: The carriage of resistant bacteria and prior antimicrobial treatment are related, but in an individual, this diminishes over time. To better manage antimicrobial resistance risks, it is crucial that we better untangle any lasting impact of antibiotic use compared to other factors. This understanding is essential for informing antimicrobial stewardship programs and to better manage other important factors that likely contribute to persistently higher rates of antimicrobial resistance in different populations. The true association between antibiotic use and resistance is likely to be significantly overestimated due to the confounding influence of varying infection risk patterns within populations. Though missing explanatory covariates are a well-known cause of falsely interpreted statistical findings, how the problem manifests in this context has a particular and interpretable structure. This issue does not appear to have been previously addressed with clarity. To be more easily understood, a simple model is used to demonstrate this. Results: In our theoretical model case study, when we exclude an effect of past antibiotic usage, clinical history alone can predict future resistance patterns. Heterogeneity in infection risk and antibiotic resistance carriage rates, along with consequently observed antimicrobial treatment, often suffice to predict a pattern of resistance that mimics what is assumed to be caused by genuine biologically driven resistance by the associated use of antibiotics. The biological impact and/or lasting effects of antibiotics are not necessary for this prediction. Conclusions: Antimicrobial stewardship policies and future research must directly address how much of the apparent persistence of resistant bacteria results from biological consequences of antibiotic use compared to pure statistical confounding arising due to heterogeneous risks in community infection patterns. Full article

Background/Objectives: Huntington’s disease (HD) is a neurodegenerative movement disorder associated with significant disability and impairment of Activities of Daily Living (ADLs). The impact of upper limb disability on quality of life (QoL) and its influence on ADLs is not well known yet. The aim of this study was to describe the manipulative dexterity, strength, and manual eye coordination of patients with manifest and premanifest-HD compared to healthy individuals and to analyze its influence on ADLs and QoL. Methods: We performed an observational, cross-sectional study including 71 ambulatory participants (27 manifest-HD patients, 15 premanifest-HD, and 29 controls). We gathered sociodemographic data, as well as clinical data, including cognition (MMSE), HD motor severity (Unified HD rating scale, UHDRS-TMS), QoL (Neuro-QoL), and ADLs (HD-ADL). Hand dexterity and strength in the dominant and non-dominant hand were assessed with the Nine Hole Peg Test, Ten Neurotest, Nut and Bolt Test, dynamometry, and Late-Life FDI. Analysis of covariance (ANCOVA) models were performed to investigate differences in hand function between manifest-HD, premanifest-HD, and controls. Results: Manifest-HD patients had significantly worse performance in manual and finger dexterity, fine-motor coordination, and poorer handgrip strength than premanifest-HD and controls. Premanifest-HD required more time to complete the test than controls. Significant correlations were found between hand variables and Late-Life FDI, Neuro-QoL, HD-ADL, and UHDRS-TMS. Conclusions: HD affects manipulative dexterity and hand function in premanifest and manifest patients. Therefore, to prevent disability and decreased QoL, evaluating the progression of upper limb dysfunction in HD is important to offer the best possible therapeutic interventions. Full article

Our study presents an innovative variational Bayesian parameter estimation method for the Quantile Nonlinear Dynamic Latent Variable Model (QNDLVM), particularly when dealing with missing data and nonparametric priors. This method addresses the computational inefficiencies associated with the traditional Markov chain Monte Carlo (MCMC) approach, which struggles with large datasets and high-dimensional parameters due to its prolonged computation times, slow convergence, and substantial memory consumption. By harnessing the deterministic variational Bayesian framework, we convert the complex parameter estimation into a more manageable deterministic optimization problem. This is achieved by leveraging the hierarchical structure of the QNDLVM and the principle of efficiently optimizing approximate posterior distributions within the variational Bayesian framework. We further optimize the evidence lower bound using the coordinate ascent algorithm. To specify propensity scores for missing data manifestations and covariates, we adopt logistic and probit models, respectively, with conditionally conjugate mean field variational Bayes for logistic models. Additionally, we utilize Bayesian local influence to analyze the Ecological Momentary Assessment (EMA) dataset. Our results highlight the variational Bayesian approach’s notable accuracy and its ability to significantly alleviate computational demands, as demonstrated through simulation studies and practical applications. Full article

Paid domestic work (PDW) is an important source of employment for the global female workforce. It is frequently performed under precarious working conditions and occupational risks that are often associated with depressive symptoms (DSs). Although 10% (2.2 million) of Mexican working women are paid domestic workers (PDWs), their mental health has remained understudied. This article analyzes the occurrence and factors associated with DSs in a sample of Mexican workers segmented into six workforce groups, including PDWs. A three-stage statistical analysis was performed on national health survey data from Mexico (ENSANUT 2012): 1. Tabulation of inferential statistics; 2. Multiple logistic regression modeling of DSs; 3. Postestimation of DSs prevalence. Our findings suggest a context of social disadvantages based on gender, education, and labor market segregation that manifests in PDWs having the highest adjusted prevalence of DSs (14.1%, 95%CI = 9.7–18.4). Significant differences in prevalence were observed when compared with other workforce groups, such as formal employees. Among working women, after adjusting for relevant covariates, the odds of DSs were higher among PDWs as compared with formal employees (OR = 1.65, 95%CI = 1.04–2.61). Ongoing efforts for PDW formalization should be maintained in Mexico as an employment policy and mechanism to achieve PDWs’ social well-being and mental health. Full article

Introduction: Melanoma is a deadly type of skin cancer that develops from melanocytes and can manifest on the skin or other regions of the body. Its incidence is increasing rapidly, with approximately 100,000 diagnoses and 7000 deaths per year in the US alone. We conducted a cross-sectional study with the aim of determining an association between the cost of care for invasive melanoma and the specialty involved in the treatment to adequately guide future treatment. Methods: We analyzed data from 3817 patients (2013–2018) using the Florida inpatient/outpatient dataset, CMS cost reports, and the National Plan and Provider Enumeration System. Covariates included age, sex, race/ethnicity, insurance type, region, county rurality, the number of procedures, the comorbidity index, obesity, metastatic cancer presence, hospital size, and physician volume. Multivariable mixed linear regression was used to analyze the data, and the cost was adjusted to the 2019 USD. Results: Dermatology had the largest decrease in the overall and outpatient costs compared to general surgery, followed by plastic surgery. The inpatient costs for dermatology and plastic surgery were lower than those for general surgery, but not significantly so. Conclusions: The costs associated with surgical procedures may vary depending on the specialty of the physician treating the patient. Dermatology was associated with lower treatment costs for invasive melanoma compared to other specialties, indicating that physician specialty influences the cost of care. Full article

The increase in cases of bullying and cyberbullying has raised concerns about its impact on the mental health of young people, particularly its relationship with test anxiety, underscoring the need to delve deeper into this issue. Therefore, the aim of this study was to examine whether suffering and perpetrating aggressive acts (bullying and cyberbullying) are related to different dimensions of test anxiety. A total of 912 Spanish students (girls 52.7%) aged between 10 and 16 years (13.43 ± 1.73) participated in this study. The European Bullying Intervention Project Questionnaire, the European Cyberbullying Intervention Project Questionnaire, and the Test Anxiety Questionnaire (CAEX-A) were used. The association between test anxiety and bullying/cyberbullying (as victims and aggressors) was calculated via analysis of covariance (ANCOVA) and binary logistic regression. All analyses were performed separately for boys and girls and adjusted for age, body mass index, maternal educational level, and academic performance. Students in bullying or cyberbullying contexts achieved significantly higher values in practically all anxiety factors analyzed (p < 0.05), especially in cognitive responses: 33.14% and 22.56% for bullying and cyberbullying victims, respectively, and 22.56% and 23.9% for aggressors. Victims of cyberbullying harassment had a high risk (OR: 8.311) of suffering diarrhoea, palpitations, chest tightness, nausea and fainting during exams, as well as avoidance behaviors (OR: 5.106) (both p < 0.001). The results, disaggregated by gender, showed that the relationship between feeling bullied in face-to-face interactions and experiencing test anxiety was only evident for girls, which seems to place them in a more vulnerable situation. Female victimization, although less frequent, could have lasting and harmful consequences. These findings highlight the need not only to implement intervention strategies in the school setting aimed at preventing and reducing bullying and cyberbullying but also to address anxiety and its physical and cognitive manifestations in both victims and aggressors. Full article

The meaning of the quantum minimum effective length that should distinguish the quantum nature of a gravitational field is investigated in the context of manifestly covariant quantum gravity theory (CQG-theory). In such a framework, the possible occurrence of a non-vanishing minimum length requires one to identify it necessarily with a 4-scalar proper length s.It is shown that the latter must be treated in a statistical way and associated with a lower bound in the error measurement of distance, namely to be identified with a standard deviation. In this reference, the existence of a minimum length is proven based on a canonical form of Heisenberg inequality that is peculiar to CQG-theory in predicting massive quantum gravitons with finite path-length trajectories. As a notable outcome, it is found that, apart from a numerical factor of $O\left(1\right)$, the invariant minimum length is realized by the Planck length, which, therefore, arises as a constitutive element of quantum gravity phenomenology. This theoretical result permits one to establish the intrinsic minimum-length character of CQG-theory, which emerges consistently with manifest covariance as one of its foundational properties and is rooted both on the mathematical structure of canonical Hamiltonian quantization, as well as on the logic underlying the Heisenberg uncertainty principle. Full article

We investigated whether developing an autoimmune disorder (AID) following a high-grade epithelial ovarian cancer diagnosis improves overall survival. This retrospective study included data from women treated for high-grade serous, endometrioid, or transitional cell ovarian, fallopian tube, or peritoneal cancer FIGO stage III or IV at a Swiss cantonal gynecological cancer center (2008–2023). We used Kaplan–Meier estimates and the Cox proportional hazards model using time-varying covariates for the survival function estimation. In all, 9 of 128 patients developed an AID following a cancer diagnosis. The median time from cancer diagnosis to AID was 2 years (IQR 2–5). These women survived for a median of 3031 days (IQR 1765–3963) versus 972 days (IQR 568–1819) for those who did not develop an AID (p = 0.001). The median overall survival of nine women with a pre-existing AID was 1093 days (IQR 716–1705), similar to those who never had an AID. The multivariate analyses showed older age (p = 0.003, HR 1.04, 95% CI 1.013–1.064) was associated with a poorer prognosis, and developing an AID after a cancer diagnosis was associated with longer survival (p = 0.033, HR 0.113, 95% CI 0.015–0.837). Clinical manifestations of autoimmune disorders following ovarian cancer diagnoses were associated with better overall survival (8 versus 2.7 years), indicating an overactive immune response may improve cancer control. Full article