Iskanje po katalogu digitalne knjižnice

Ključne besede: state policy, institutions, innovation, R and D, cluster, cluster policy
Objavljeno v DKUM: 06.11.2025; Ogledov: 0; Prenosov: 0
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Opis: Physical and mental health show strong associations with health-related behavior. Uni versity students are one of the at-risk groups who are in a vulnerable transition phase from adolescence to adulthood, significantly affecting their health-related lifestyle. This study aims to identify different groups of university students with homogeneous health related behavior, considering their dietary habits, physical activity, alcohol and tobacco consumption, mental well-being and lifestyle change motives. For data collection, an anonymous, closed-ended paper-and-pencil questionnaire was administered to a sample of 171 university students. Data analysis was performed using descriptive statistics, a t-test for independent samples, a chi-square test, Spearman correlation and hierarchical cluster analyses (Ward’s method, Dendrogram). On average, students reported good health (M=4.84), including daily physical activity (M = 31.35 min) and regular consumption of fruits (M = 4.02) and vegetables (M = 4.19). The hierarchical cluster analysis revealed two distinct patterns among the students: “Caring for a healthy lifestyle” (N = 69) and “Physically inactive with poor mental well-being” (N = 62). Better health-related behavior was found among male students enrolled in higher study years (p ≤ 0.01). These findings provide newinsights into the different patterns of health-related behavior among university students that require targeted health promotion actions. Universities should develop and implement courses in healthy lifestyles and sustain them in the curricula.
Ključne besede: higher education, university students, healthy lifestyle, mental well-being, cluster analyses
Objavljeno v DKUM: 10.07.2025; Ogledov: 0; Prenosov: 18
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Opis: Background and purpose: The aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) in different Parkinson’s disease (PD) non-motor subtypes, classified using newly established criteria and a data-driven approach. Methods: A total of 168 patients with idiopathic PD underwent comprehensive NMS and MS examinations. NMS were assessed by the Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), Hamilton Depression Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), Starkstein Apathy Scale (SAS) and Fatigue Severity Scale (FSS). Motor subtypes were classified based on Stebbins’ method. Patients were classified into groups of three NMS subtypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency. Results: We identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Using a data-driven approach, we identified five different clusters. Three corresponded to the cortical, limbic, and brainstem subtypes, while the two additional clusters may have represented patients with early and advanced PD. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (χ2(8) = 175.001, p < 0.001, Cramer’s V = 0.722). The demographic and clinical profiles differed between NMS subtypes and clusters. Conclusion: Using the actuarial and clustering approach, marked differences between individual NMS subtypes were found. The newly established criteria have potential as a simplified tool for future clinical research of NMS subtypes of Parkinson’s disease.
Ključne besede: Parkinson’s disease, non-motor symptoms subtypes, a priori classification, cluster analysis
Objavljeno v DKUM: 07.04.2025; Ogledov: 0; Prenosov: 9
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Opis: The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.
Ključne besede: big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality
Objavljeno v DKUM: 14.01.2025; Ogledov: 0; Prenosov: 7
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Opis: Liquid biopsy is becoming an important source of new biomarkers during the treatment of metastatic cancer patients. Using size-based microfluid technology, we isolated circulating tumor cells (CTCs) from metastatic breast cancer patients to evaluate their presence and cluster formation, as well as the presence of megakaryocytes and immune-inflammatory blood cells, and to correlate their presence with clinicopathological data and overall survival (OS). In total, 59 patients (median age 60.4 years) were included in the study: 62.7% luminal A/B-like, 20.3% HER2-positive, and 17% triple-negative. Our results showed that at least one CTC was present in 79.7% and ≥5 CTCs in 35.2% of the patients. CTC clusters were present in patients with ≥5 CTCs only (in 19.2% of them), and megakaryocytes were present in 52% of all patients. The presence of CTC clusters and megakaryocytes was positively associated with the CTC count. Patients with low pan-inflammatory value (PIV), low systemic immune-inflammatory index (SII), and low relative change from baseline (ΔPIV%, ΔSII%) were associated with significantly higher OS than their counterparts. ΔPIV%, the presence of infection in the last month, and a long duration of metastatic disease were identified as independent prognostic factors for OS. The interplay of CTCs, CTC clusters, megakaryocytes, and PIV needs to be further explored.
Ključne besede: breast cancer, circulating tumor cell, cluster, megakaryocyte, pan-inflammatory value, overall survival
Objavljeno v DKUM: 19.04.2024; Ogledov: 253; Prenosov: 23
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