Chang Gung University of Science and Technology (CGUST; Chinese: 長庚科技大學) is a private university in Guishan District, Taoyuan City, and Puzi City, Chiayi County of Taiwan.
BACKGROUND:The lack of a comprehensive assessment tool to evaluate patient understanding of the benefits and risks of complementary therapies (CTs) in diabetes management may lead to the unsafe use of CTs alongside conventional treatments, increased risk of misinformed decision-making, potential adverse interactions, and compromised health outcomes. PURPOSE:In this study, an instrument was developed to assess patient understanding of the benefits and risks of using CTs in diabetes management and its psychometric properties were evaluated. METHODS:A two-phase design, including scale development and psychometric validation, was used. In Phase 1, the initial scale items were revised and confirmed by a panel of experts for content validity. In Phase 2, a cross-sectional survey was conducted to assess the scale's psychometric properties, including reliability and validity analyses. A sample of 307 outpatients with diabetes who had used CTs all completed a sociodemographic, clinical characteristics, and CT usage data sheet, as well as the Understanding the Benefits-Risks of CT Use in Diabetes Scale and the Diabetes Empowerment Scale. The developed scale was validated using exploratory and confirmatory factor analyses that used structural equation modeling to confirm construct validity, Person correlation to confirm criterion-related validity, and Cronbach's α coefficient to confirm reliability. RESULTS:The initial 16-item Understanding the Benefits-Risks of CT Use in Diabetes Scale was assessed as having a content validity index of .88. Factor analyses reduced the scale to 15 items in four dimensions, including the patient's medical condition for CT use (four items), the benefit-risk assessment of CTs use (four items), the suitability of CT use (five items), and the support from health care professionals (two items). The model met all goodness-of-fit indices (GFI=.90, AGFI=.90, CFI=.94, NFI=.926, RMSEA=.08, and χ 2 / df =3.14). The reliability analysis indicated good internal consistency (α=.912) and correlation with the Diabetes Empowerment Scale ( r =.425). CONCLUSIONS/IMPLICATIONS FOR PRACTICE:The Understanding the Benefits-Risks of CT Use in Diabetes Scale offers valuable insights for both patients and health care professionals. By providing a comprehensive assessment of patient knowledge and awareness of their CT use, this tool helps health care professionals identify gaps in patient understanding, tailor patient education, and ensure safe and effective integration of CTs into diabetes management programs.
The present study provides the first insights into the biological activities and chemical profile of the newly described desert truffle Tirmania sahariensis. The bioactive properties of T. sahariensis were investigated, focusing on its antioxidant, acetylcholinesterase inhibitory, antimicrobial, anti-allergic and anti-inflammatory activities. Antioxidant capacity was assessed using DPPH, CUPRAC, and FRAP assays, revealing that the ethyl acetate extract exhibited stronger DPPH scavenging activity (IC₅₀ = 158.12 µg/mL) and higher FRAP reducing power (A₀.₅₀ = 72.40 µg/mL) than the methanolic extract. In contrast, the methanolic extract showed greater activity in the CUPRAC assay (A₀.₅₀ = 208.19 µg/mL). Both extracts displayed acetylcholinesterase inhibitory activity comparable to that of the reference control. While the methanolic extract exhibited limited antimicrobial effects, the ethyl acetate extract showed strong in vitro antimicrobial activity under the tested conditions, particularly against Staphylococcus aureus (50.33 mm) and Enterococcus faecalis (30 mm), as well as notable antifungal activity against Candida albicans (40.33 mm). In addition, both extracts demonstrated moderate anti-inflammatory activity by inhibiting superoxide anion generation and elastase release in human neutrophils, with the ethyl acetate extract showing slightly stronger effects. The chemical composition of T. sahariensis was characterized using gas chromatography–mass spectrometry (GC–MS), revealing major constituents such as pentanoic acid 2-phenylethyl ester (18.27
The rapid advancement of artificial intelligence (AI) is reshaping talent management by enabling data-driven approaches to recruitment, skill development, and workforce planning. This study introduces the InsightConnect AI Empowerment System, an integrated digital platform designed to optimize talent-project matching, recruitment forecasting, and knowledge sharing through predictive analytics, natural language processing (NLP), and graph-based learning. Grounded in a Design Science Research (DSR) framework, the system was developed and validated using anonymized datasets comprising 10,000 user profiles and approximately 2,000 enterprise projects (1,840 completed projects used for evaluation).The hybrid recommendation model, combining content-based, collaborative, and graph-embedding techniques, achieved a 12.7% improvement in precision and a 10.4% increase in recall over traditional baselines, while the predictive module attained a Root Mean Square Error (RMSE) of 0.083, indicating strong forecasting accuracy. Prototype deployment results revealed a 24% rise in successful talent-project matches and a 30% reduction in search time, enhancing both organizational efficiency and user satisfaction.The findings highlight how AI-enabled ecosystems can advance workforce intelligence, improve data-informed decision-making, and support policy innovation for sustainable human capital development.
Cognitive behavioral therapy (CBT) is an effective treatment for anxiety disorders; however, treatment engagement, adherence, and premature dropout remain persistent clinical challenges. Motivational interviewing (MI) has increasingly been integrated with CBT to address ambivalence, enhance treatment readiness, and support treatment engagement across different phases of therapy. This scoping review mapped and synthesized existing models of MI-CBT integration and their reported clinical and methodological characteristics. This scoping review followed Joanna Briggs Institute methodology and PRISMA-ScR guidelines. Searches were conducted in PubMed, CINAHL, PsycINFO, and Web of Science for studies published between January 2005 and December 2025. Eligible studies examined integrated MI-CBT interventions for anxiety disorders. Data were synthesized descriptively. Based on recurrent implementation patterns identified across the included studies, a preliminary conceptual synthesis was developed to organize MI-CBT integration according to treatment sequencing, phase-specific engagement functions, delivery formats, and implementation characteristics. Nine empirical studies met the inclusion criteria. Three broad MI-CBT integration approaches were identified: pretreatment MI, concurrent/embedded MI, and intermittent/responsive MI. Considerable heterogeneity was observed across studies in treatment sequencing, MI content, dosage, therapist training, fidelity monitoring, and implementation procedures. Across studies, MI was primarily implemented with the reported aim of enhancing and address motivational challenges, with several studies reported improved in treatment readiness, adherence, attendance, therapeutic alliance, and resistance management. Evidence for incremental symptom improvement beyond standard CBT remained inconsistent. Based on recurrent descriptive patterns identified across the included studies, a preliminary conceptual framework was developed to organize the observed MI-CBT integration approaches. The reviewed literature suggests that MI may have potential value for addressing ambivalence, strengthening the therapeutic alliance, managing resistance, and supporting engagement during clinically challenging phases of CBT. However, the current evidence base remains limited and heterogeneous. Future research should evaluate this preliminary conceptual framework across diverse clinical populations and implementation settings while clarifying optimal sequencing strategies, fidelity procedures, dosage parameters, and the role of therapist-supported and asynchronous digital CBT interventions.
Traditional facial massage instruction relies on demonstration and imitation, providing no objective feedback on force or movement quality. This study presents a real-time facial pressure-matrix feedback system designed to support skill acquisition in facial massage training. A flexible $\mathbf{4 x 4}$ pressure sensor array captures force distribution and directional motion across key facial regions. The data are transmitted wirelessly and visualized as heatmaps and motion trajectories, enabling learners to compare their performance with expert patterns. The system provides measurable feedback on force level, direction consistency, coverage, and rhythmic stability, thus addressing the limitations of conventional training and practice mannequins. This approach supports standardized, reproducible, and feedback-driven learning of massage techniques.
