Search Results (86,054)

Background: Medication adherence is a major determinant of treatment effectiveness in psychiatric care and is influenced by patients’ attitudes toward medication and beliefs about treatment. Objective: This study aimed to evaluate medication adherence, drug attitudes, and beliefs about medicines, and to examine their relationships in the study population. Methods: A total of 300 participants were assessed using the Medication Adherence Rating Scale (MARS), Drug Attitude Inventory-10 (DAI-10), and Beliefs about Medicines Questionnaire (BMQ-General and BMQ-Specific). Descriptive statistics, independent-samples t-tests, Pearson correlation analyses, and multiple linear regression were performed. Results: The mean DAI-10 score was 3.57 ± 3.44, indicating an overall positive attitude toward medication, although 27.33% of participants had neutral or negative attitudes. The mean MARS score was 6.27 ± 2.24, suggesting moderate adherence. Mean BMQ-General and BMQ-Specific scores were 21.70 ± 5.81 and 31.64 ± 6.13, respectively. Significant gender differences were found across all scales. DAI-10 was positively correlated with MARS, while BMQ-General was negatively correlated with MARS. Multiple regression showed that DAI-10, BMQ-General, and BMQ-Specific significantly predicted MARS scores, explained 30.8% of variance after adjustment. Conclusions: Medication adherence was moderate and was significantly associated with treatment attitudes and beliefs about medicines. The findings support multidimensional assessment and targeted interventions addressing both positive attitudes and negative medication beliefs. Full article

Environmental sustainability has become a critical priority for higher education institutions, which play a key role in promoting awareness and shaping students’ perceptions of sustainable practices. Understanding students’ awareness and perceptions is essential for evaluating the effectiveness of institutional sustainability initiatives. This study aimed to assess students’ awareness and perceptions of environmental sustainability at Prince Sattam bin Abdulaziz University and to examine the influence of demographic factors and the relationship between awareness and perception. A quantitative cross-sectional survey was conducted among 323 university students. Data were collected using a structured questionnaire measuring environmental awareness (18 items) and perception of sustainability practices (14 items) on a 5-point Likert scale. Composite scores were computed as the means of item responses. Descriptive statistics, independent t-tests, one-way ANOVA, and multivariable linear regression analyses were performed. Students demonstrated a moderate level of environmental awareness (mean = 3.116 ± 0.403) and moderate perceptions of sustainability practices (mean = 2.887 ± 0.199). Environmental awareness was significantly higher among female students and those in science-related disciplines (p < 0.001). Perception of sustainability was significantly associated with field of study and level of study (p < 0.001). In multivariable analysis, gender and field of study remained significant predictors of awareness, while gender, field of study, and level of study predicted perception. A significant but negative association was observed between awareness and perception of environmental sustainability (B = −0.496, p < 0.001). While students demonstrated a moderate level of environmental awareness, perceptions of sustainability practices were inconsistent. The findings highlight the need for enhanced sustainability education and engagement initiatives within universities. Future research should explore how awareness and perception translate into meaningful engagement with sustainability practices. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

Background and Objectives: Quality of life (QoL) is an important issue for breast cancer (BC) survivors. The objective of this study was to assess health-related QoL (HRQoL) of BC patients and investigate the impact of different demographic and clinical factors on physical and social functioning and BC-related symptoms. Materials and Methods: In this cross-sectional study, 107 BC patients undergoing chemotherapy in Greece completed a questionnaire collecting sociodemographic and clinical information and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–Core 30 (EORTC QLQ-C30) in order to assess HRQoL. Descriptive statistics and multiple linear regression analyses were used to identify factors linked to HRQoL outcomes. Results: Overall, participants reported moderate HRQoL, with high physical and social functioning and moderate emotional, cognitive, and role functioning. Fatigue was the most common symptom, whereas other symptoms were generally uncommon. Multiple regression analyses showed that marital status, place of residence, time since diagnosis, and type of surgery were significantly associated with the global QLQ-C30 score (R² = 0.337, p < 0.001). Physical functioning was associated with comorbidity burden, time since diagnosis, and employment status (R² = 0.155, p = 0.035), and social functioning with marital status and type of surgery (R² = 0.171, p = 0.011). Emotional functioning showed exploratory associations with place of residence and type of surgery; however, the overall regression model for emotional functioning did not reach statistical significance. No symptom model reached overall significance, but time since diagnosis, treatment type, and surgery were linked to distinct symptoms. Conclusions: BC patients undergoing chemotherapy in Greece report an overall moderate level of HRQoL, which is significantly influenced by a combination of demographic and clinical factors; physical and social functioning were high, with moderate emotional, cognitive, and role functioning. These findings highlight the importance of individualized supportive care strategies in order to improve QoL of BC patients. Full article

Background: This study aimed to compare a limited set of predefined diet-, lifestyle-, knowledge-, and psychosocial indicators across school youth, students, and adults in Poland, and to examine their associations with three predefined outcomes: BMI ≥ 25 kg/m², poorer mental well-being, and high stress/overload. Diet quality, daily health-related practices, psychosocial well-being, and stress/overload may co-occur across different life stages, but online survey data require a focused analytical framework to avoid overinterpretation. Methods: This cross-sectional anonymous online survey included 360 respondents: 154 school youth aged 15–19 years, 127 students aged 20–29 years, and 79 adults aged 30 years or older. Dietary assessment was based on the KomPAN questionnaire and included the pro-healthy diet index, non-healthy diet index, and Diet Quality Index. Study-specific scores were used for knowledge, healthy practices, psychosocial well-being, and stress/overload. Analyses were restricted to predefined group comparisons, selected correlations, and three whole-sample adjusted logistic regression models. Results: Adults had the highest BMI and waist/hip circumference, whereas school youth showed the highest non-healthy diet index and more frequent high processed-food intake. Among the knowledge and psychosocial indicators, only obesity knowledge differed significantly between groups, with the highest mean value among students. Stress/overload was inversely associated with psychosocial well-being, and DQI was positively associated with psychosocial well-being after adjustment for age, sex, and group. In adjusted whole-sample models, BMI ≥ 25 kg/m² was positively associated with age and DQI and inversely associated with physical activity frequency and regular meals; the positive DQI–BMI association was interpreted cautiously as potentially reflecting reverse causality, reporting bias, or compensatory dietary modification among respondents with excess body weight. Poorer mental well-being was associated with higher stress/overload and inversely associated with DQI, physical activity frequency, and family meals. High stress/overload was positively associated with highly processed food intake and inversely associated with regular meals. Conclusions: The findings suggest that diet quality, behavioral regularity, and psychosocial burden may be more informative than knowledge alone when describing health-related profiles across age-defined groups. Because the study was cross-sectional, self-reported, anonymous, and based on a modest sample, the results should be interpreted as preliminary and hypothesis-generating rather than causal. Full article

High levels of low-density lipoprotein cholesterol (LDL-c) have been recognized as the main causal factor of atherosclerotic cardiovascular disease (ASCVD) and are influenced by both genetic and environmental factors. Among genetic determinants, Familial Hypercholesterolemia (FH) is the most common monogenic disorder, caused by rare high-impact variants in genes involved in LDL uptake. Other monogenic causes of hypercholesterolemia include sitosterolemia, cerebrotendinous xanthomatosis and lysosomal acid lipase deficiency (LALD). However, monogenic disorders only account for a small proportion of inherited hypercholesterolemia. In many individuals, increased LDL-c levels are caused by the contemporary presence of different single-nucleotide polymorphisms (SNPs) with a moderate/low impact. These SNPs could be summarized through polygenic risk scores (PRS) that attribute relative weight to each of these. Another genetic determinant of hypercholesterolemic phenotypes is high levels of lipoprotein(a)—Lp(a). Lp(a) is an LDL particle modified by the binding of apolipoprotein(a)—apo(a)—which represents an independent risk factor for ASCVD. Lp(a) levels are mainly genetically determined by variation in the number of kringle IV type 2 (K-IV₂) repeats, as well as by several SNPs, and remain stable throughout life. The aim of this narrative review is to report an updated overview of the genetic mechanisms underlying hypercholesterolemia, including monogenic disorders, PRS and Lp(a), focusing on their potential repercussion in clinical practice by the integration into cardiovascular risk stratification beyond traditional clinical assessment. This integration could lead to a more comprehensive and individualized approach to cardiovascular prevention, with emerging perspectives including the possible use of artificial intelligence (AI). Full article

Graphene and carbon nanotube (CNT) membranes are promising for filtration, desalination, and water treatment, yet their performance requires the joint interpretation of their architecture, nanoconfined transport, selectivity, fouling, swelling, defects, stability, and operating conditions. Here, GCMembrane-LLM was developed as an evidence-grounded domain-specific large language model. A curated 582-paper corpus generated 12,208 cleaned membrane-specific question–answer pairs for Low-Rank Adaptation (LoRA)-based supervised fine-tuning of Llama-3.1-8B-Instruct, and retrieval-augmented generation provided article-title and page-level traceability. GCMembraneBench included 100 application-oriented questions on graphene oxide (GO) membranes, CNT membranes, GO/CNT hybrids, and cross-material reasoning. Under direct answering without retrieval context, the anonymized and shuffled automatic evaluation showed that GCMembrane-LLM achieved a mean weighted score of 4.237/5.0, exceeding Llama-3.1-8B-Instruct and Doubao-1.5-lite. A stratified 30-question blinded manual assessment showed the same ranking. The application cases further yielded membrane science conclusions: CNT-assisted GO/CNT transport should be evaluated with dispersion, interfacial compatibility, defects, and stability; GO desalination depends on swelling control, interlayer spacing, and defect suppression; and CNT high flux requires joint examination of pore diameter, entrance chemistry, hydration barriers, ion rejection, and operating conditions. GCMembrane-LLM supports source-traceable evidence organization and preliminary hypothesis formulation before experimental validation. Full article

Background: Dry Eye Disease (DED) is a multifactorial ocular surface disorder characterized by tear film instability and inflammation. Cyclosporine A, an immunomodulator, and Diquafosol sodium, a mucin secretagogue, represent two distinct therapeutic pathways. However, current evidence directly comparing their clinical efficacy is inconsistent. This meta-analysis aimed to compare treatment outcomes and efficacy between 0.05% Cyclosporine A and 3% Diquafosol sodium in patients with moderate-to-severe DED. Methods: In January 2026, we conducted a systematic search of PubMed, Scopus, Web of Science, and the Cochrane Library for randomized controlled trials directly comparing 0.05% Cyclosporine A to 3% Diquafosol sodium in adult patients with moderate-to-severe DED. For the meta-analysis, we used R 4.5.0 with R Studio 2024.12.1+563. Results: We included six RCTs with a total of 859 patients. No significant differences were found between Cyclosporine A and Diquafosol sodium in Tear Break-Up Time (TBUT) at 4, 8, or 12 weeks. Cyclosporine A showed a suggestive greater improvement in Schirmer test scores at 4 weeks (SMD = 0.35, 95% CI 0.07 to 0.63). A modest benefit in symptom scores favoring Diquafosol sodium was observed at 12 weeks (SMD = 0.23, 95% CI 0.06 to 0.41). Subgroup analysis suggested this symptomatic benefit may be more pronounced in patients with severe disease, although subgroup interaction tests were not statistically significant. There were no significant differences in corneal or conjunctival staining at any time point. The risk of adverse events did not differ significantly between treatments. Conclusions: Early improvement in tear production showed a potential benefit for Cyclosporine A, while longer-term symptomatic relief showed a potential benefit for Diquafosol sodium, with suggestive evidence in severe disease. However, these findings should be interpreted cautiously, given the methodological limitations and inconclusive TSA evidence for several outcomes. Future large-scale, standardized trials with extended follow-up are warranted to confirm these findings. Full article

Introduction: Compassion is defined as the emotional response that arises when an individual perceives another’s suffering and is motivated to alleviate it. Purpose: To explore levels of self-compassion among nurses working in pediatric hospitals and examine their associations with nurses’ characteristics. Materials and Methods: This cross-sectional study included a convenience sample of 208 nurses from a public pediatric hospital. Data were collected through interviews using the Neff Self-Compassion Scale (SCS) which includes the following subscales: Self-Kindness, Common Humanity, Mindfulness, Self-Judgment, Isolation, and Over-Identification. The Greek-validated version of the instrument was used with acceptable internal consistency in the present sample (Cronbach’s alpha = 0.849). Data analysis included descriptive statistics and inferential tests (non-parametric comparisons and multiple linear regression), with statistical significance defined as p < 0.05. Results: The mean total Self-Compassion score was 83.24 ± 12.6 (range: 26–130). Regarding family-related factors, total Self-Compassion (p = 0.029), Common Humanity (p = 0.033), and Over-Identification (p = 0.041) were associated with the number of children. In relation to age, Self-Kindness (p = 0.033), Isolation (p = 0.005), and Over-Identification (p = 0.005) showed significant associations. Professional factors were also relevant, as Isolation was associated with total years of nursing experience (p = 0.032) and choice of nursing as a profession (p = 0.004), while Over-Identification was associated with years of experience in pediatric settings (p = 0.004) and choice of nursing as a profession (p = 0.049). Additionally, marital status was associated with Over-Identification (p = 0.045). Conclusions: Demographic and professional characteristics appear to influence the expression of Self-compassion. Healthcare organizations should implement targeted training programs to individualize professional development. Future research should explore work-related and personal factors influencing self-compassion to improve care quality and outcomes. Full article

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context. Full article

Background/Objectives: The gut–liver axis is central to sepsis, but assessing mesenteric perfusion remains challenging. Indocyanine green (ICG) clearance reflects hepatic blood flow. Since portal flow is derived from mesenteric circulation and supplies most of the liver, reduced ICG clearance may indicate mesenteric hypoperfusion, which can lead to enteral feeding intolerance (EFI). This study examines whether reduced ICG clearance in septic patients without overt liver injury is associated with EFI. Methods: This study is a secondary analysis of a prospective cohort study (March–May 2024, 20-bed ICU). Septic patients without sepsis-related liver injury or recent abdominal surgery were included. ICG plasma disappearance rate (ICG-PDR) was measured at admission; patients were grouped by ICG-PDR (≤18%/min vs. >18%/min). The primary outcome was EFI within 7 days. Multivariate logistic regression and correlation analyses were performed. Results: Among 77 patients (44 with ICG-PDR > 18%/min, 33 with ≤18%/min), the decreased ICG-PDR group had higher SOFA scores (8.4 ± 4.2 vs. 5.4 ± 3.5, p = 0.001) and higher EFI rates (66.7% vs. 43.1%, p = 0.041). Univariate analysis showed ICG-PDR ≤ 18%/min associated with EFI (OR = 2.632, p = 0.043), but this was attenuated after SOFA adjustment (OR = 2.247, p = 0.171). Reduced ICG-PDR correlated with central venous pressure (CVP) (r = 0.626, p < 0.001) but not with mean arterial pressure (r = −0.175, p = 0.129). Conclusions: In septic patients with preserved hepatocyte function, reduced ICG clearance is associated with EFI, but this relationship is largely explained by disease severity (SOFA). Reduced ICG clearance correlates with CVP; however, ICG-PDR cannot distinguish between portal venous and arterial inflow components. The exact mechanism remains speculative. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

Impacted mandibular third molars present a common challenge in oral surgery, often associated with postoperative complications such as delayed healing and periodontal defects; therefore, optimizing adjunctive therapies is clinically important. In this study, we aimed to evaluate the efficacy of platelet-rich fibrin (PRF) and preoperative azithromycin in modulating inflammation and enhancing wound healing following surgical extraction of impacted mandibular third molars. In this prospective clinical study, healthy subjects aged 18–50 years were randomly assigned to three groups: a control group receiving standard postoperative amoxicillin therapy, a PRF group receiving PRF with standard therapy, and a PRF-plus-azithromycin group receiving PRF, standard therapy, and a single preoperative dose of azithromycin. Clinical parameters were assessed and cytomorphometric analysis was performed preoperatively and postoperatively. Clinical parameters generally improved over time in all groups (p < 0.001). Differences between groups were observed for interincisal distance, Landry Index, and pain scores, with a trend toward more favorable outcomes in the combined-therapy group. Cytomorphometric analysis revealed cellular alterations in the control group, relative stability in the PRF group, and intermediate changes in the combined-therapy group. Within the limitations of this study, the combination of PRF and preoperative azithromycin showed potential benefits in several postoperative outcomes. However, given the study design and sample characteristics, these findings should be considered preliminary and require confirmation in larger prospective studies before definitive clinical recommendations can be made. Full article

Background/Objectives: Evidence on the associations between adherence to different plant-based diet indices and depression in young adults remains limited. This study aimed to analyze the associations of overall, healthy, and unhealthy plant-based diet indices with depressive symptoms in university students. Methods: A cross-sectional study was conducted in 2023 with self-reported data from university students in Cuenca, Spain. Adherence to the overall plant-based diet index (PDI) and to the healthy (hPDI) and unhealthy (uPDI) plant-based diet indices were calculated using data from a 137-item food-frequency questionnaire. Mild-to-severe depression was defined as a Beck Depression Inventory II score > 13 points. Linear and logistic regression models were adjusted for sociodemographic and lifestyle-related confounders. Results: A total of 392 students (mean age: 20.9 ± 2.4 years; 70.4% female) were included. The prevalence of mild-to-severe depression was 36.0%. Higher hPDI and overall PDI scores were associated with lower depressive symptom scores, whereas uPDI scores showed a positive but non-significant association after full adjustment. In logistic regression analyses, high adherence to the hPDI was associated with lower odds of mild-to-severe depression (OR = 0.51; 95% CI: 0.28–0.95; p-for-trend = 0.030). In contrast, higher uPDI adherence was associated with greater odds of depression, although the association was attenuated after adjustment for lifestyle-related variables. Conclusions: Greater adherence to a healthy plant-based diet was associated with lower depressive symptoms and lower odds of mild-to-severe depression among university students. These findings highlight the importance of plant food quality, rather than plant-based diets per se, in relation to depression in young adults. Full article