Search Results (74)

Background/Objectives: Despite its well-established health benefits, adherence to the Mediterranean lifestyle (MedLife) has declined globally, including in its region of origin, alongside a significant shift toward ultra-processed food consumption. Understanding the factors associated with MedLife adherence is essential for developing targeted interventions and tailored policy recommendations. As part of the MEDIET4ALL PRIMA project, this cross-sectional study aimed to comprehensively examine geo-demographic, socio-economic, psychological, behavioral, and barrier-related factors associated with and potentially contributing to MedLife adherence. Methods: Data were collected from 4010 participants aged 18 years and above across ten Mediterranean and neighboring countries using the multinational MEDIET4ALL e-survey, which included the validated MedLife index, along with various other questionnaires. Results: Results indicate that only 22% of respondents demonstrated high adherence to the Mediterranean lifestyle (MedLife), with significant variability observed across countries, age groups, education levels, and health statuses. Spain had the highest proportion of participants with high adherence (38%). Factors associated with significantly higher adherence rates include older age, living in the Mediterranean region, higher education levels, a greater awareness of MedLife principles, lower perceived barriers, normal BMI, better health status, and stable economic and marital conditions (p-values ranging from 0.04 to <0.001). Additionally, individuals with high MedLife adherence exhibited more socially and physically active lifestyles and experienced less psychological strain (p < 0.001). Regression analyses identified MedLife awareness as the strongest positive predictor of adherence (β = 0.206), followed by social participation (β = 0.194) and physical activity (β = 0.096). Additional positive contributors include life satisfaction, sleep quality, living in the Mediterranean region, age, and education (β ranging from 0.049 to 0.093). Conversely, factors that are negatively associated with adherence include sedentary behavior, living environment, and barriers such as low motivation, taste dislike, price unaffordability, limited availability, and the time-consuming nature of preparing Mediterranean food (MedFood; β ranging from −0.036 to −0.067). Conclusions: These findings indicate that fewer than one in four adults across Mediterranean and neighboring countries demonstrate high adherence to MedLife, supporting prior evidence of suboptimal adherence even within Mediterranean regions. This study identified a range of behavioral, socio-demographic, and environmental factors—both positive and negative predictors—that can help guide the design of targeted, culturally adapted interventions to promote MedLife behavior. Future research should incorporate objective measurements and longitudinal monitoring to better understand underlying mechanisms, establish causality, and develop sustainable strategies for enhancing MedLife adherence in diverse populations. Full article

Background/Objectives: In the aftermath of the COVID-19 pandemic, individuals infected with SARS-CoV-2 have progressively displayed a range of symptoms linked to protracted COVID during the post-acute phase of illness. Concurrently, in several nations globally, the phenomenon of population aging has been intensifying. In this scenario, the aged population has become both vulnerable and high-risk during the acute phase of COVID-19, and faces significant dangers associated with long-COVID. This study seeks to analyze the incidence and spatial distribution of health complications in older people affected by COVID-19, in the first year of the pandemic (2020), in the State of Paraná, as well as to identify the factors associated with the development of cardiovascular, neurological, respiratory, and metabolic diseases. Method: An observational and retrospective study was carried out in the Brazilian state of Paraná. Participants were randomly selected from two databases. A total of 893 older people (≥60 years) participated in the study 12 months after acute COVID-19 infection. Telephone questionnaires were applied between March and December 2021. The Moran index test, logistic regression, and Poisson models were used to analyze the data. Results: In terms of age, most participants (66%) were between 60 and 69 years old, 25.8% were between 70 and 79 years old, and 8.2% were 80 years old or older. Most participants were female (51.2%), white (98.1%), had a partner (69.8%), and had been hospitalized due to COVID-19 (59.3%). Cardiovascular diseases were the most frequent in the population (39.5%), followed by metabolic diseases (27.3%). The long-term use of medication was associated with the development of metabolic diseases (aOR = 9.8), cardiovascular diseases (aOR = 6.6), and diseases in multiple organic systems (aOR = 3.2); living alone was associated with neurological diseases (aOR = 2.5), and the age group of 80 years or older (aOR = 2.4) was associated with cardiovascular events. The spatial distribution showed that complications in body groups are distributed randomly among the health regions of the state, with no influence from neighboring locations. Conclusions: Post-COVID-19 health complications are more frequent in older adults who have comorbidities and long-term medication use. Therefore, long-term monitoring of these individuals and investment in public policies for rehabilitation and prevention of complications are necessary. Full article

This paper introduces an unsupervised machine learning approach for student clustering and personalized recommendations in education. We employ the K-means clustering algorithm to identify distinct student groups based on behavioral engagement metrics. Unlike previous studies that relied on predefined categories, our methodology validated the number of clusters using both the elbow method and silhouette analysis, which ensured an optimal grouping structure. The clustering phase served as a foundation for deriving insights into student learning behaviors. To assess the clustering quality, we applied the silhouette score to quantify intra-cluster cohesion and inter-cluster separation, which provided statistical validation for our approach. Following the clustering process, we developed a recommendation system based on the user-based nearest neighbors collaborative filtering approach. This system tailors educational strategies to the unique characteristics of each cluster, enhancing student engagement and learning outcomes. Furthermore, we compared our methodology against alternative clustering and recommendation techniques to demonstrate its robustness and effectiveness. Our findings suggest that this combined clustering and recommendation framework offers a data-driven approach to personalized education, which can be extended beyond the KALBOARD360 dataset to other educational contexts. The overarching goal was to refine adaptive learning models that cater to the diverse needs of students, improving their academic success and participation. Full article

Background: Recent research highlights uncertainties surrounding the metabolic effects of nicotine in young adults, particularly among people who use e-cigarettes. While traditional smoking is known to alter body composition, the metabolic impact of using e-cigarettes remains less understood. Methods: In this cross-sectional study, body composition (via bioelectrical impedance analysis) and lifestyle data were collected from 60 university students (mean age: 21.7 ± 1.9 years), who were classified as people who use e-cigarettes exclusively, people who smoke cigarettes exclusively, or people who have never used nicotine products. To address confounding by sex and age, inverse probability of treatment weighting (IPTW) was applied. Results: After adjustment, people who use e-cigarettes had significantly higher body fat percentage compared to people who have never used nicotine (β = 5.45, p = 0.001), while no significant differences were found between people who smoke cigarettes and other groups. Energy drink consumption was also positively associated with body fat percentage and metabolic age. Machine learning models, particularly k-nearest neighbors, achieved moderate classification accuracy (up to 72%) in distinguishing people who use nicotine from people who have never used nicotine based on physiological and lifestyle features. Conclusions: It is important to note that the majority of participants were metabolically healthy, and the observed differences occurred within a clinically normal range. While these findings suggest associations between e-cigarette use and higher adiposity in young adults, no causal inferences can be made due to the observational design. Further longitudinal studies are needed to explore the potential metabolic implications of nicotine use. Full article

People in multilingual societies develop complex and interconnected food-making and food-discussing networks. On the basis of an experimental course titled “Food at Home, Food on the Move: Globalization and Regionalism in Modern Food Culture” taught at the University of Helsinki, we will show how the acquisition of culinary terminology puts forward the interconnectedness of languages and the dynamics between them in several sociolinguistic contexts. The lectures were grouped geographically: Eating with the Neighbors (Finnish cuisine and Swedish, Russian, Karelian and other influences); From the Baltic to Central Europe (Latvian, Lithuanian, Polish, German, and Hungarian food cultures); Formative Cuisines of the Mediterranean (French, Italian, Greek, Middle Eastern cuisine, etc.); and Eating Outside Europe (food culture influences from the US, Mexico, China, Japan, and India). The assignments included a critical lecture diary, an essay about eating experiences, or additional reading, a conversational analysis of a culinary show, or fieldwork in an ethnic restaurant. Raising awareness of linguistic and cultural diversity, motivating course participants to discuss the role and interaction of languages in their repertoire, makes them reflect on their multilingual identities. It allows educators to explore individuals’ DLCs in different contexts while navigating diverse global and local environments based on the principles of fairness and equality in education. Full article

Ethylene oxide (EtO) gas is a widely used industrial chemical and known health hazard. Multiple studies have determined that EtO exposure can be measured via hemoglobin adduct levels, and EtO exposure increases the risk of cancer and neurocognitive deficits, especially with occupational exposure. Emerging studies indicate that neighboring communities are also at risk. The purpose of this study is to explore the relationship of known covariates and EtO hemoglobin adduct levels to neurocognitive performance in older U.S. adults. This exploratory study drew its sample from the publicly available NHANES dataset. The 2013–2014 NHANES measured EtO exposure via hemoglobin adducts and the cognitive domain of neurocognitive function using the CERAD, Animal Fluency, and Digit Symbol Substitution (DSST) tests. Motor function was measured using grip strength. Participants were grouped into background (≤27.36 pmol/gHb) or elevated (>27.36 pmol/gHb) EtO exposure. Hierarchical linear regression, independent t-tests, and logistic regression analyses were performed. A total of 10,175 individuals were sampled: 489 were included in the cognitive analyses, and 436 were included in the motor analyss. Elevated EtO adduct levels significantly predicted low Animal Fluency, DSST, CERAD, and combined grip strength scores. Our findings are supported by the extant literature citing neurotoxic EtO exposure effects. Further study in known EtO-exposed communities is warranted. Full article

Background: Cardiovascular diseases (CVDs) are responsible for 32.4% of all deaths across the European Union (EU), and several CVD risk scores have been developed, with variable results. Retinal microvascular changes have been proposed as potential biomarkers for cardiovascular risk, especially in coronary heart diseases (CHDs). This study aims to identify the retinal microvascular features associated with CHDs and evaluate their potential use in a CHD screening algorithm in conjunction with traditional risk factors. Methods: We performed a two-center cross-sectional study on 120 adult participants—36 patients previously diagnosed with severe CHDs and scheduled for coronary artery bypass graft surgery (CHD group) and 84 healthy controls. A brief medical history and a clinical profile were available for all cases. All patients benefited from optical coherence tomography angiography (OCTA), the use of which allowed several parameters to be quantified for the foveal avascular zone and superficial and deep capillary plexuses. We evaluated the precision of several classification models in identifying patients with CHDs based on traditional risk factors and OCTA characteristics: a conventional logistic regression model and four machine learning algorithms: k-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM) and supervised logistic regression. Results: Conventional multiple logistic regression had a classification accuracy of 78.7% based on traditional risk factors and retinal microvascular features, while machine learning algorithms had higher accuracies: 81% for K-NN and supervised logistic regression, 85.71% for Naive Bayes and 86% for SVM. Conclusions: Novel risk scores developed using machine learning algorithms and based on traditional risk factors and retinal microvascular characteristics could improve the identification of patients with CHDs. Full article

Smoking behavior, encompassing both traditional tobacco and electronic cigarette use, is influenced by a range of demographic, familial, and social factors. This study examines the relationship between smoking habits and family dynamics through a cross-sectional survey of 100 participants, using an anonymous questionnaire to collect demographic data, smoking patterns, and familial interactions. Validated instruments, including the Penn State Electronic Cigarette Dependence Index and the Family Relationship Assessment Scale, were employed to assess smoking dependence and family dynamics. The analysis identified key patterns, such as increased smoking frequency among individuals experiencing higher family tension and variations in smoking habits across age and gender groups. Nocturnal smoking was linked to higher cigarette consumption, whereas early-day smokers exhibited a lower desire to quit. Machine learning models were applied to predict and classify smoking behaviors based on socio-demographic and familial variables, with an ensemble learning model achieving the highest accuracy (93.33%), outperforming k-nearest neighbors (90.00%), support vector machines (80.00%), and decision trees (83.33%). These findings underscore the complex interplay between family relationships and smoking behavior, providing insights for public health interventions. Additionally, this study highlights the potential of machine learning in behavioral research, demonstrating its utility in identifying and predicting smoking-related patterns. Full article

In a number of developing countries, low productivity and technical inefficiency, with climate change looming in the background, remain a severe challenge for the agricultural sector, especially smallholder farmers. To enhance smallholder farmers’ livelihoods in terms of agricultural productivity while mitigating the adverse effects of climate change, improving technical efficiency in a sustainable manner is a promising option. One possible alternative is the use of solar-powered small-scale irrigation systems in areas vulnerable to climate change to ensure sufficient access to water. This study uses stochastic frontier analysis to analyze technical efficiency and its determinants among smallholder farmers who benefit from a solar-powered small-scale irrigation scheme in Gitaraga, Bugesera District, Rwanda. Similar smallholders from a neighboring village, who were not participating in the irrigation scheme, represent the control group. The results suggest that inputs such as land, water, and labor are positively correlated to agricultural productivity. Farmers participating in the irrigation scheme are 31.2 percentage points more technically efficient compared to non-participants, despite similar climatic conditions. Thus, relaxing the water constraint on arable land will increase agricultural productivity. Explanatory inefficiency determinants include years of farming experience and market access. Subsequently, policy makers should continue to support programs that improve smallholder access to sustainable irrigation schemes, other infrastructure, extension services, and upstream value chains, as well as markets. Full article

Background/Objectives: This study evaluated the impact of influenza vaccination on mortality using real-world data and compared the effect of current-season-only vaccination versus continuous two-season vaccination. Methods: The 2017–2019 data from the Center for Disease Control and Prevention of Shenzhen, Guangdong, China, included 880,119 individuals aged ≥65 years. The participants were divided into vaccinated and unvaccinated groups and matched using propensity scores with a 1:4 nearest-neighbor approach. Vaccinated individuals were further divided into current-season-only and continuous two-season vaccination groups, matched 1:1. Cox’s multivariable proportional hazards regression models were used to assess the effect of vaccination on all-cause mortality, with Firth’s penalized likelihood method applied to correct for a few events. The Fine–Gray competing risk models were used to assess the effect of vaccination on cardio-cerebral vascular disease (CCVD) mortality. Sensitivity analyses, including caliper matching, a nested case–control design, and Poisson’s regression, were performed to test the robustness of the results. Results: Influenza vaccination reduced all-cause mortality by 39% (HR = 0.61, 95% CI: 0.47–0.80) and 55% (HR = 0.45, 95% CI: 0.33–0.60) in 2017–2018 and 2018–2019, respectively. Current-season-only vaccination showed stronger protective effects than continuous two-season vaccination (HR = 0.56, 95% CI: 0.31–0.99). Influenza vaccination reduced CCVD mortality by 46% (HR = 0.54, 95% CI: 0.34–0.84) in 2018–2019. The results were consistent across the sensitivity analyses. Conclusions: Influenza vaccination was associated with a reduced risk of all-cause and CCVD mortality in older adults, underscoring the importance of routine influenza vaccination in older populations. Stronger effects were observed for current-season-only vaccination, warranting further research to confirm the association and explore mechanisms. Full article

Using methods of DFT, we investigated the effect of electron withdrawing and electron donating groups on the relative stability of tentative glycosyl donor reaction intermediates. The calculation shows that by changing the stereoelectronic properties of the protecting group, we can influence the stability of the dioxolenium type of intermediates by up to 10 kcal mol⁻¹, and that by increasing nucleophillicity of the 4-O-Bz group, the dioxolenium intermediate becomes more stable than a triflate–donor pair. We exploited this mechanism to design galactosyl donors with custom protecting groups on O2 and O4, and investigated the outcome of the reaction with cyclohexanol. The reaction showed no change in the product distribution, which suggests that the neighboring group participation takes precedence over remote group participation due to kinetic barriers. Full article

Organic arsenic compounds such as p-aminophenylarsine oxide (p-APAO) are easier for structural optimization to improve drug-like properties such as pharmacokinetic properties, therapeutic efficacy, and target selectivity. In order to strengthen the selectivity of 4-(1,3,2-dithiarsinan-2-yl) aniline 7 to tumor cell, a thiourea moiety was used to strengthen the anticancer activity. To avoid forming a mixture of α/β anomers, the strategy of 2-acetyl’s neighboring group participation was used to lock the configuration of 2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl isothiocyanate from 2,3,4,6-tetra-O-acetyl-α-d-glucopyranosyl bromide. 1-(4-(1,3,2-dithiarsinan-2-yl) aniline)-2-N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranos-1-yl)-thiourea 2 can increase the selectivity of human colon cancer cells HCT-116 (0.82 ± 0.06 μM vs. 1.82 ± 0.07 μM) to human embryonic kidney 293T cells (1.38 ± 0.01 μM vs. 1.22 ± 0.06 μM) from 0.67 to 1.68, suggesting a feasible approach to improve the therapeutic index of arsenic-containing compounds as chemotherapeutic agents. Full article

Housing different animal groups in close-by facilities is common in wildlife centers. However, the impact on animal welfare is insufficiently studied in the literature. In this study, we analyzed the behavior of two adjacently housed chimpanzee groups to investigate how intergroup interactions may affect their behavior and, thus, their welfare. We recorded occurrences of abnormal and self-directed behaviors, two well-known indicators of stress in chimpanzees. Furthermore, we explored the social responses to said intergroup interactions by recording all inter- and intragroup affiliative and agonistic behaviors. Finally, we measured the number of vigilance occurrences that individuals directed towards other chimpanzees as an indicator of interest. Generalized Linear Mixed Models (GLMMs) were used to assess whether and how social interactions between neighboring groups might influence their behaviors, taking gender, age, group, and intergroup participation into account. Our results suggest that intergroup interactions promoted the occurrence of affiliative behaviors between group members. However, intergroup interactions caused the chimpanzees to exhibit a higher number of abnormal and self-directed behaviors and increased vigilance towards their group members when agonistic intergroup interactions occurred. Thus, adjacent housing does impact the chimpanzees’ behavior and welfare and should be continuously monitored and assessed to promote and maximize welfare. Full article

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication. Full article

Severe heavy rains caused by linear precipitation systems are occurring more frequently in Japan owing to climate change, and residents are being asked to evacuate more often. The purpose of this study was to identify factors associated with the willingness of people with mental health illness (PMHI) in group homes to disclose their illness when being evacuated. Participants were PMHI living in group homes in Japan. We conducted an original anonymous self-administered questionnaire based on previous research. Valid data from 119 people were analyzed. Factors associated with the willingness to disclose illness to supporters upon evacuation were “I can imagine living in a public shelter” (Odds Ratio [OR] 4.50, 95% Confidence Interval [CI]: 1.78–11.43), and “I socialize with neighbors” (OR 5.63, 95% CI: 1.74–18.22). Managers of group homes should encourage PMHI to imagine life in an evacuation zone by increasing opportunities for disaster training and for interaction with local residents. People who are less likely to socialize with neighbors should be especially careful, as they may not be able to disclose their illness, and those who support evacuees should pay special attention to these people. Full article