Search Results (10,777)

The functional beverage sector has experienced a remarkable transformation driven by evolving consumer decision-making patterns emphasizing therapeutic benefits alongside taste preferences. This comprehensive narrative review investigates how consumer psychology, neurobiological processes, and scientific product development converge through a hierarchical framework illustrating their dynamic interactions. Today’s consumers exhibit unprecedented sophistication when assessing bioactive ingredients, conducting independent research using scientific databases rather than relying on conventional marketing. Our analysis explores mechanisms underlying habit development, behavioral adaptation, and social proof factors driving functional beverage integration into daily routines. We trace evolution from broad-spectrum wellness drinks toward personalized nutrition solutions, recognizing individual metabolic requirements, with consumers viewing these products as preventive health investments requiring evidence-based validation. Key findings underscore the importance of clinically validated formulations at therapeutic dosages, nutritional transparency, and understanding consumer psychology for fostering lasting consumption behaviors driven by cost–benefit analysis. Results indicate future innovations must merge sophisticated bioactive delivery technologies with insights into consumer information-seeking patterns, social validation processes, and evidence-driven decision-making mechanisms. Full article

Background/Objectives: Stroke and arteriosclerotic diseases remain the main challenge for global healthcare. Carotid artery procedures aim to restore blood flow through the carotid arteries to prevent embolic events. The most common techniques include carotid endarterectomy (CEA) and carotid artery stenting (CAS). The choice of intervention depends on the severity of stenosis, the patient’s overall condition and the presence of comorbidities. The personalized approach, which includes sex-related differences, is crucial in optimizing the outcome. Methods: Sex-related differences in atherosclerosis risk factors and early carotid artery stenting treatment outcomes were evaluated in 271 patients. The goal of the study was to asses sex-related differences in early outcome of CAS, including success rate and complications. Results: The only significant difference in classical arteriosclerosis risk factors included a higher occurrence of smoking among males. The technical success rate of carotid artery stenting was high (94.46%). The sex-related differences in CAS involve using smaller sizes of implanted stents in females. There was a high incidence of complications (mostly minor), predominantly among females. They had a significantly higher frequency of bleeding and hypotension. Blood pressure and BMI significantly influenced the odds of complications. Conclusions: Females undergoing CAS have a higher complication risk with a similar success rate. Full article

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial Area Under the Curve (AUC). Predicting postprandial AUC in advance based on a person’s lifestyle factors, such as diet and physical activity level, and explaining the factors that affect postprandial blood glucose could allow an individual to adjust their behavioral choices accordingly to maintain normal glucose levels. In this work, we develop an explainable machine learning solution, GlucoLens, that takes sensor-driven inputs and utilizes advanced data processing, large language models, and trainable machine learning models to estimate postprandial AUC and predict hyperglycemia from diet, physical activity, and recent glucose patterns. We use data obtained using wearables in a five-week clinical trial of 10 adults who worked full-time to develop and evaluate the proposed computational model that integrates wearable sensing, multimodal data, and machine learning. Our machine learning model takes multimodal data from wearable activity and glucose monitoring sensors, along with food and work logs, and provides an interpretable prediction of the postprandial glucose patterns. GlucoLens achieves a normalized root mean squared error (NRMSE) of 0.123 in its best configuration. On average, the proposed technology provides a 16% better predictive performance compared to the comparison models. Additionally, our technique predicts hyperglycemia with an accuracy of 79% and an F1 score of 0.749 and recommends different treatment options to help avoid hyperglycemia through diverse counterfactual explanations. With systematic experiments and discussion supported by established prior research, we show that our method is generalizable and consistent with clinical understanding. Full article

Background and Objectives: Data on mental health in university students have been increasingly concerning, with high prevalence rates of clinical conditions such as anxiety, stress, and depression. This study aims to evaluate the risk factors associated with mental health status and to develop a predictive model. Materials and Methods: A total of 242 university students were recruited (74.8% women). Participants’ ages ranged from 18 to 56 years (M = 25.81; SD = 7.59). Data collection were conducted through the Depression, Anxiety, and Stress Scale (DASS-21), the Big Five Inventory-10 (BFI-10), and the Coping Orientation to Problems Experienced Inventory (COPE-28). Results: Overall, mean scores across the three clinical dimensions are within the moderate range, but anxiety shows the highest mean value (M = 8.67, SD = 5.69) and is categorized as “extremely severe.” Additionally, identifying as female, living with family or roommates, and having high scores on passive coping styles were significant risk factors for mental health deterioration. In contrast, identifying as male, living with a romantic partner (cohabitation), and having high scores on the Responsibility personality trait were identified as protective factors against mental health impairment. Conclusions: Additional research is warranted to explore additional mediating variables and to develop specific intervention protocols for improving university students’ psychological well-being. Full article

Background/Objectives: When a relationship ends due to abuse, a favorable attitude toward reconciliation may become a risk factor. The objective of this study was to develop and validate an instrument to measure the attitude toward returning to an ex-partner. Methods: A pilot study was conducted to evaluate the dimensionality and psychometric quality of the items. The main study involved 55 women who had been victims of gender violence. Results: Following item analysis and assessments of reliability (α = 0.93) and validity, a unidimensional 16-item scale was developed. The instrument, named the “Attitude Toward Returning to an Ex-partner Scale” (ATRES), allows for the identification of predispositions to return to a relationship in which serious abuse has occurred. Moreover, the findings revealed that a heightened perception of danger, along with forgiveness directed toward oneself, the other person, and the situation, was associated with a less favorable attitude toward reconciliation. Conversely, high religiosity predisposed individuals to rekindle the relationship. Conclusions: The scale could serve to facilitate interventions, mainly in situations where restoring the relationship can be a risk. The assessment of the predisposition to forgive the ex-partner—namely, the individual who perpetrated the abuse—as well as the victim’s attitude toward re-engaging in the relationship, constitute important considerations for preventing revictimization. The ATRES is the first self-report measure designed to assist researchers and professionals in the precise assessment of specific beliefs and myths underlying the reinstatement of a relationship. Full article

Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations. Full article

To address the problem of inaccurate matching between personalized exercise recommendations and learners’ mastery of knowledge concepts/learning abilities, we propose the Dynamic Multidimensional Memory Augmented knowledge tracing model (DMMA). This model integrates a dynamic key-value memory neural network with the Ebbinghaus Forgetting Curve. By incorporating time decay factors and knowledge concept mastery speed factors, it dynamically adjusts knowledge update intensity, effectively resolving the insufficient personalized recommendation capabilities of traditional models. Experimental validation demonstrates its effectiveness: on Algebra 2006–2007, DMMA achieves 82% accuracy, outperforming CRDP-KT by 6%, while maintaining 53–55% accuracy for cold-start users (0–5 interactions), which is 25% higher than CoKT. The model’s integration of the Ebbinghaus forgetting curve and K-means-based concept classification enhances adaptability. Genetic algorithm optimization yields a diversity score of 0.79, with 18% higher 30-day knowledge retention. The FastDTW–Sigmoid hybrid similarity calculation (weight transition 0.27–0.88) ensures smooth cold-start adaptation, while novelty metrics reach 0.65 via random-forest-driven prediction. Ablation studies confirm component necessity: removing time decay factors reduces accuracy by 2.2%. These results validate DMMA’s superior performance in personalized education. Full article

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption. Full article

Introduction: The aim of this study was to investigate the spatial relationship between the incisive canal (IC) and apical region of the maxillary central incisors in the Korean population, using cone-beam computed tomography (CBCT) imaging. The findings are intended to inform and improve the planning and execution of immediate implant placement in the maxillary esthetic zone. Materials and methods: CBCT data were collected from 94 patients (48 men, 46 women) aged 20–79 years at Gangnam Dental Clinic, Seoul, South Korea. The sample was divided according to age into three groups: 20–39 years, 40–59 years, and 60–79 years. Exclusion criteria included missing maxillary anterior teeth, severe crowding, periodontitis, pathology, and image artifacts. Measurements of the distance from the root apex to the incisive canal (RIC-11-P, RIC-21-P) and from the root apex to the buccal bone (RBB-11-B, RBB-21-B) were taken from CBCT images. Statistical analyses were conducted using Welch’s t-test, ANOVA, and Pearson correlation, with significance set at p < 0.05. Results: The mean distances from the root apex to the incisive canal were 3.77 mm (RIC-11-P) and 3.62 mm (RIC-21-P), while the mean distances to the buccal bone were 0.86 mm and 0.94 mm, respectively. Males exhibited significantly greater distances compared to females, both in the NPC-to-root apex and buccal bone measurements. Age-related variations were observed, with younger individuals showing shorter distances from the IC to the root apex. However, ANOVA tests and Pearson correlation analysis indicated no statistically significant correlation in these distances across different age groups. The study highlights significant gender differences in maxillary central incisor anatomy, with males having larger distances from the root apex to both the IC and buccal bone, which has implications for implant placement. While age-related changes were observed, they did not significantly affect the mean distances in a statistically meaningful way. Conclusions: These findings underscore the need for personalized treatment planning in immediate implant placement, particularly in relation to gender and age. Comparisons with other population studies suggest that these anatomical differences may be consistent across various ethnic groups, though individual variance factors should still be considered. Full article

The 2022 global mpox outbreak, caused by clade IIb of the monkeypox virus (MPXV), prompted emergency use authorisation of the Modified Vaccinia Ankara–Bavarian Nordic (MVA-BN) vaccine, previously approved for smallpox prevention. Understanding immune responses to the MVA-BN vaccine is critical to inform both current and future mpox vaccine policy, particularly amid reports of breakthrough infections in vaccinated persons, uncertainty about the durability of vaccine-induced protection, and the emergence of further outbreaks of mpox from different viral clades, including the clade I-driven public health emergency of international concern. MVA-BN elicits binding and neutralising antibody, memory B cells, and T cell responses. Immune responses vary by host factors, prior orthopoxvirus exposure, and dosing regimens. While seroconversion is generally robust, circulating antibody titres often wane rapidly, particularly in vaccinia-naïve and/or immunocompromised individuals, including people with HIV. Vaccine-induced neutralising antibody responses to MPXV are frequently lower than to vaccinia virus, and their role in protection remains ill-defined. In contrast, T cell responses appear more sustained and may support long-term immunity in the absence of persistent antibody titres. This narrative review synthesises current evidence on the immunogenicity and durability of MVA-BN vaccination, highlights challenges in assay interpretation, and outlines key research priorities, including the need to explore correlates of protection, booster strategies, and next-generation vaccine design. Full article

Background: Acute kidney injury (AKI) following cardiac surgery can lead to chronic kidney disease, increased hospitalization costs, and higher mortality risk. Our retrospective study identified risk factors of severe AKI (AKI 3) in patients undergoing on-pump surgical aortic valve replacement (SAVR). Additionally, we analyzed the significance of inflammatory indexes and risk scores in predicting AKI 3, focusing on sex differences. These findings could provide cost-efficient tools for clinical practice to identify patients at risk, improve preoperative risk stratification, and personalize monitoring. Methods: We reviewed the on-pump SAVR patients from our tertiary center between 2022 and 2024. Results: Out of 422 patients, 121 (28.67%) experienced AKI, including 27 (6.39%) AKI 3 patients. The multivariable binary logistic regression identified AKI 3 independent risk factors: hemostasis reintervention (OR9.76, CI95%:3.565–26.716, p = 0.001), early postoperative vasoactive-inotropic score (VIS) (OR1.049, CI95%:1.013–1.086, p = 0.007), postoperative lymphocyte (OR2.252, CI95%:1.224–4.144, p = 0.009). Preoperative systemic inflammatory response index (AUC0.700, p = 0.019), preoperative aggregate index of systemic inflammation (AUC0.712, p = 0.011), postoperative platelet-to-lymphocyte ratio (PLR) (AUC 0.759, p = 0.001), and the delta value of preoperative-to-postoperative PLR (AUC0.752, p = 0.001) were better predictors of AKI 3 occurrence in female SAVR patients than the additive EuroSCORE (AUC0.692, p = 0.011), but were less accurate compared to EuroSCORE II (AUC0.841, p = 0.001). None of the studied inflammatory indexes or additive EuroSCORE predicted our endpoint in male SAVR patients, while Thakar score was able to predict it exclusively in males. Conclusions: Early postoperative VIS, lymphocyte count, and hemostasis reintervention were independent risk factors for severe AKI in SAVR patients. There is a differentiation between males and females from the AKI prediction perspective. Full article

Pulmonary emphysema, the main component of chronic obstructive pulmonary disease, is a chronic lung inflammatory disease characterized by the loss of lung elasticity and impaired gas exchange due in large part to the destruction of alveolar walls. Cigarette smoking represents the most frequent etiologic factor, but other factors involving environmental pollution and respiratory infections contribute to disease pathogenesis and worsening. In this review, we provide a review about emphysema covering risk factors; underlying mechanisms of disease pathogenesis; experimental models that mimic, as closely as possible, human disease features; and available therapeutics. Lastly, exploratory therapeutic approaches aimed at improving patient health through evidence-based and personalized medicine are presented as well. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis. T cell exhaustion (TEX) is a key factor in tumor immune evasion and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to characterize TEX-related transcriptional features in HCC. Methods: We first computed TEX scores for each sample using a curated 65-gene signature and classified them into high-TEX and low-TEX groups by the median score. Differentially expressed genes were identified separately in scRNA-seq and bulk RNA-seq data, then intersected to retain shared candidates. A 26-gene prognostic signature was derived from these candidates via univariate Cox and LASSO regression analysis. Results: The high-TEX group exhibited increased expression of immune checkpoint molecules and antigen presentation molecules, suggesting a tumor microenvironment that is more immunosuppressive but potentially more responsive to immunotherapy. Functional enrichment analysis and protein–protein interaction (PPI) network construction further validated the roles of these genes in immune regulation and tumor progression. Conclusions: This study provides a comprehensive characterization of the TEX landscape in HCC and identifies a robust gene signature associated with prognosis and immune infiltration. These findings highlight the potential of targeting TEX-related genes for personalized immunotherapeutic strategies in HCC. Full article

The general objective of this research was to investigate the attitudes of Bucharest students toward artificial intelligence (AI)—in particular, ChatGPT—in relation to their personality traits, coping strategies, and personal values to identify psychosocial approaches for students’ effective reporting toward this AI product. As there was no instrument validated and calibrated on Romanian students, the scale constructed by Acosta-Enriquez et al. in 2024 was adapted to students from Bucharest (N = 508). Following the item analysis, the adapted scale was reduced to 16 items, and, following the factor analysis (EFA–0.81 < α < 0.91), the structure with three factors (cognitive, affective, and behavioral components), explaining 53% of the variation in Bucharest students’ attitudes toward ChatGPT, was maintained considering the results of the confirmatory factor analysis—CFA (χ²(79) = 218.345, p < 0.001; CMIN/DF = 2.486; CFI = 0.911; TLI = 0.900; RMSEA = 0.058 (90% CI: 0.50–0.065). The present study showed that 85.53% of the research subjects used ChatGPT at least once, of which 24.11% have a positive/open attitude toward ChatGPT, and that there are correlations (p < 0.01; 0.23 < r² < 0.50) between students’ attitudes toward ChatGPT and several personality traits, coping strategies, and personal values. It also proves that the three components of the attitude toward ChatGPT (cognitive, affective, and behavioral) are correlated with a series of personality traits, coping strategies, and personal values of students. Although the general objective was achieved and the adapted scale has adequate psychometric qualities, the authors propose in future studies to expand the group of subjects so that the scale can be validated at the level of the Romanian population. In this research, at the end, several concrete approaches are proposed for the effective reporting of students toward this AI product, which, beyond the ethical challenges, also recognizes the benefits of technology in the evolution of education. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing public health concern influenced by both genetic and metabolic factors. Polygenic risk scores (PRSs), which combine the effects of known single-nucleotide polymorphisms (SNPs), may improve early risk stratification. We conducted an observational study on 298 MASLD patients: 148 from a Hepatology Unit and 150 from a Bariatric Surgery Unit. Genotyping was performed for the PNPLA3, TM6SF2, MBOAT7, and GCKR variants. A PRS was calculated and used to stratify patients by genetic risk. Liver fibrosis was assessed using the FIB-4 index, and a subset also underwent transient elastography. Clinical, biochemical, and anthropometric data were analyzed across genetic strata. PRSs showed positive correlations with AST, ALT, and FIB-4, indicating increased liver injury and fibrosis risk with higher genetic burden. Transaminases increased significantly across PRS quartiles (p < 0.05), and individuals with PRS > 0.532 exhibited elevated AST, ALT, and borderline FIB-4. Variant-specific associations included PNPLA3 with increased AST and MBOAT7 with higher hepatic steatosis (CAP). Subgroup analyses revealed distinct genetic and phenotypic patterns between the two clinical cohorts. These findings support the additive role of genetic risk in MASLD progression and underscore the value of polygenic profiling for the early identification and personalized management of high-risk patients. Full article