Search Results (6,149)

Background: Spreadability is a critical performance attribute for semisolid formulations, influencing patient compliance, dose uniformity, and product acceptability. Despite its importance, there is no standardized method for its assessment across pharmaceutical and cosmetic applications. Objective: This review uniquely integrates systematic literature mapping with an experimental comparison of five spreadability assessment techniques, providing evidence-based recommendations for harmonizing protocols and improving reproducibility in semisolid formulation testing. Methods: A systematic search of PubMed, Scopus, and Web of Science identified 211 records, of which 14 studies met the inclusion criteria. Techniques reviewed included parallel-plate, slip-and-drag, rheometry (flow curve and amplitude sweep), texture analysis, and frictiometry. An experimental comparison was conducted on ten commercial formulations using all five techniques to assess inter-method variability and formulation-dependent behavior. Results: Texture analyzer and amplitude sweep rheometry emerged as the most reproducible and predictive methods, showing strong correlation (r = 0.74) in both literature and experimental data. Flow curve yield stress negatively correlated with parallel-plate spreadability (r = –0.796). Frictiometry results varied significantly with formulation type, particularly for ointments. Creams consistently ranked highest in spreadability across methods. Conclusion: No single method universally captures spreadability. Amplitude sweep rheometry correlated well with texture analysis, while flow curve values were more variable. Parallel-plate testing showed strong agreement with rheological and tribological methods, though texture analysis diverged, capturing distinct mechanical attributes. A tiered approach integrating parallel-plate, amplitude sweep, and frictiometry is recommended, with flow curve retained for regulatory compliance. Texture analysis provides valuable orthogonal information. Standardization of parallel-plate protocols is needed to establish unified spreadability indices. Full article

There is a growing demand for digital innovation to facilitate authentic communication during the learning experience at Australian Universities. Student’s communication is considered ‘authentic’ in various ways, from using discipline-specific professional language to expressing personal values through honest self-reflection. Enhancing authentic rational decision-making during social learning online is one priority area now available for students developing clinical reasoning skills. Using a Design-based Research (DBR) methodological framework, 34 students, 26 educators, and 5 learning designers from Torrens University Australia provided iterative feedback on the development and implementation of a simple digital decision wheel tool, aimed at supporting independent and collaborative decision-making. Three DBR phases were implemented, encompassing an initial pilot and development stage with 3 subjects, and two subsequent action cycles with an additional 17 subjects that were incorporated using a decision wheel tool for independent and problem-based learning. Data were generated through 44 semi-structured interviews and 20 focus groups across twenty undergraduate subjects delivered in various learning modes across five 12-week DBR action cycles. Reflexive thematic analysis and bounded rationality theory guided analysis. Outputs reveal that a simple digital tool contributed positively to making authentic learning moments visible and promoted inclusive and formative dialogue. Benefits included development of psychological authenticity when preparing to make authentic industry decisions. The initiative aligns with broader educational goals for resourcing and developing tools to scaffold a ‘critical pause’ before articulating authentic thinking when engaging with humans and machines. Full article

This study presents a theoretical framework for modeling sorption and release kinetics of substances in polymeric materials with planar, cylindrical, and spherical geometries. Fick’s second law was expressed in dimensionless variables and solved numerically using a finite-difference approach to generate universal profiles for mass transfer. These profiles were fitted with double-exponential equations, yielding explicit expressions that allow for straightforward estimation of diffusion coefficients from experimental data. The method was validated using literature data for films, fibers, and microspheres, showing excellent agreement with reported values. Unlike classical analytical solutions, which are limited to planar systems under ideal conditions, the proposed approach is applicable to diverse geometries commonly employed in packaging, biomedical devices, controlled-release formulations, and environmental technologies. Full article

Background/Objectives: Periodontal disease may progress despite supportive periodontal therapy (SPT), and conventional clinical parameters exhibit limited predictive value. This study evaluated the prognostic utility of measuring the oral fluid lactate dehydrogenase (LD) activity using a chairside test strip to predict disease progression during SPT. Methods: A cohort of 92 patients (33 males and 59 females; median age, 68 years) undergoing SPT at a university clinic in 2023 were followed up for approximately one year. Oral fluid LD activity was measured using a test strip, and clinical periodontal parameters were assessed at baseline and follow-up. Periodontitis progression was defined as ≥4 sites showing a 2 mm increase in the probing pocket depth, which reached ≥4 mm. Receiver operating characteristic analyses and stepwise logistic regression were used to evaluate the predictive performance of oral fluid LD activity and develop a multivariate model. Results: Seventeen patients (18.5%) developed progressive periodontitis. Progressive cases demonstrated significantly higher baseline LD activity and periodontal parameters. The optimal cutoff value for LD activity was 3.5, yielding a sensitivity, specificity, and negative predictive value of 1.00, 0.53, and 1.00, respectively. Logistic regression identified oral fluid LD and medications as independent predictors, with the multivariate model achieving an area under the curve of 0.785. Conclusions: Oral fluid LD activity measured using the test strip provided prognostic information for periodontitis progression during SPT. Incorporating LD with information obtained from medical interviews did not markedly enhance the predictive accuracy. This rapid and noninvasive method may facilitate risk-based patient management and personalized supportive care. Full article

Large language models (LLMs) are rapidly being explored as tools to support learning and assessment in health science education, yet their performance across discipline-specific evaluations remains underexamined. This study evaluated the accuracy of two prominent LLMs on university-level pharmacognosy examinations and compared their performance to that of pharmacy students. Authentic exam papers comprising a range of question formats and content categories were administered to ChatGPT and DeepSeek using a structured prompting approach. Student data were anonymized and LLM responses were graded using the same marking criteria applied to student cohorts, and a Monte Carlo simulation was conducted to determine whether observed performance differences were statistically meaningful. Facility Index (FI) values were calculated to contextualize item difficulty and identify where LLM performance aligned or diverged from student outcomes. The models demonstrated variable accuracy across question types, with a stronger performance in recall-based and definition-style items and comparatively weaker outputs for applied or interpretive questions. Simulated comparisons showed that LLM performance did not uniformly exceed or fall below that of students, indicating dimension-specific strengths and constraints. These findings suggest that while LLM-resistant examination design is contingent on question structure and content, further research should refine their integration into pharmacy education. Full article

The rapid advancement of artificial intelligence (AI) technologies has created both opportunities and challenges in higher education. AI tools are increasingly regarded as potentially transformative, yet also contested, resources for enhancing teaching and learning. This study investigates the behavioral intentions and use behavior of Generative AI (GenAI) tools among Portuguese university students by applying the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. Seven predictors (performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value) were included to develop a model explaining the behavioral intention and use behavior of AI tools. Data were collected from 323 Portuguese university students and analyzed using partial least squares structural equation modeling. The results show that only hedonic motivation and habit significantly influence behavioral intention, with habit emerging as the strongest predictor. Behavioral intention, in turn, positively affects use behavior. Moreover, gender also moderates the relationship between habit and behavioral intention. These findings deepen our understanding of GenAI tools’ acceptance in higher education and underscore the central roles of enjoyment and routine in adoption processes. They further provide insights to support institutional strategies, inform curriculum design, and support the ethical integration of GenAI technologies in academic contexts. Full article

Chestnut (Castanea sativa Mill.) is a Mediterranean staple food valued for its cultural heritage, gastronomic identity, nutritional profile, bioactivities, and socio-economic and environmental relevance. This narrative review synthesizes current knowledge on chestnut fruits and by-products, linking ecophysiology and genetic diversity to chemical composition and functionality. It summarizes the nutrient profile (high starch and dietary fiber; gluten-free; B vitamins; essential minerals; and favorable fatty acids) and the diversity of phytochemicals—particularly phenolic acids, flavonoids, and ellagitannins (e.g., castalagin and vescalagin)—that underpin antioxidant, anti-inflammatory, antimicrobial, anti-proliferative, and metabolic effects demonstrated across in vitro, cellular, and in vivo models. We compare conventional and green extraction strategies (e.g., hydroethanolic, ultrasound-/microwave-assisted, and supercritical and subcritical water), highlighting method-dependent yields, composition, and bioactivity, and the valorization of shells, burs, and leaves within circular bioeconomy frameworks. Technological applications span functional foods (gluten-free flours, beverages, and emulsions), nutraceuticals, and cosmetics (skin-protective and regenerative formulations), and active packaging/biopolymers with antioxidant and antimicrobial performance. We discuss sources of variability (cultivar, environment, maturation, and processing) affecting bioactive content and efficacy, and outline future directions. Finally, this review emphasizes the importance of university-facilitated co-creation with companies and consumers—within the framework of Responsible Research and Innovation—as a pathway to strengthen the economic valorization and full utilization of the chestnut value chain, enhancing its societal relevance, sustainability, and health-promoting potential. Full article

Background: The Canadian HIV Cure Enterprise (CanCURE) is a pan-Canadian research collaboratory, investigating approaches for achieving sustainable HIV remission. In preparation for the next research cycle, CanCURE researchers and the Community Advisory Board (CAB) co-designed a web-based survey to identify HIV research priorities from the perspective of people with HIV (PWH) in Canada. The current study examined gender-based differences in these priorities. Methods: From August to December 2024, we recruited PWH across Canada through community organizations and community members. We collected data using REDCap electronic data capture tools hosted at The Research Institute of the McGill University Health Centre. The survey included 36 demographic questions, 16 questions related to general knowledge about HIV and HIV cure-related concepts, and 21 questions ranking research priorities. Knowledge questions were multiple choice, while priorities could be ranked on a scale. We summarized participant characteristics via descriptive statistics, and the research priorities were further stratified according to gender. Results: Of 109 participants, 48.6% self-identified as men, 46.8% as women, and 4.6% as two-spirit, non-binary, agender, or other. The median age was 53 years old. Approximately one-third of participants had lived with HIV for ≤14 years, one-third for 15–24 years, and one-third for ≥25 years. Overall, the median knowledge score of respondents was 79%. Among the 78 participants with prior HIV research experience, three times as many men (61.1%) as women (19.0%) participated in interventional studies involving medication or medical procedures. Men ranked preventing HIV transmission to partners as a priority, studying where the virus hides as the second, and avoiding high comorbidity risks as the third. In contrast, women ranked not having to take pills daily as a priority and avoiding higher risks for comorbidities as the second priority. Both genders equally valued expanding community involvement in HIV cure research. However, men focused more on integrating social and behavioural research, while women emphasized the need for diverse ethnic representation in research. Conclusions: Although both men and women share some common priorities regarding HIV cure research, there are notable gender differences in their specific concerns. Furthermore, a significant gender gap in participation in interventional studies, essential for advancing HIV cure research, highlights the importance of aligning research priorities with concerns of both genders. Full article

Background/Objectives: Tinnitus affects approximately 15% of the population and lacks a universally effective treatment. Tinnitus Retraining Therapy (TRT) is widely used but requires 6–8 h of daily sound exposure for 1–2 years, limiting accessibility and adherence. This study evaluated the clinical feasibility and therapeutic effectiveness of Enriched Acoustic Environment therapy (EAE), a streamlined alternative using individualized sound stimulation with a markedly reduced treatment burden, and compared its time efficiency with published TRT outcomes. Methods: 82 adults with chronic tinnitus received standardized counseling and completed one of two EAE protocols (continuous or sequential). Participants listened to their personalized stimulus for 1 h/day over four months. Tinnitus severity (THI, TFI) and time-efficiency metrics (improvement per 10 listening hours) were assessed and compared with TRT studies reporting baseline and post-treatment THI. Results: EAE produced clinically relevant and statistically significant improvements, with 51.6% THI and 49.8% TFI reduction (p < 0.001). Both stimuli achieved similar outcomes with high responder rates. EAE yielded ~2.3 THI-point improvement per 10 h (~4.3% relative gain), demonstrating substantially greater time efficiency—approximately 20 times higher than values reported for standard TRT protocols. Conclusions: EAE achieved robust symptom reduction with dramatically lower treatment burden, high adherence, and strong clinical feasibility. These findings support EAE as an accessible, time-efficient alternative to TRT. Controlled long-term studies are warranted. Full article

Background: Rapid antimicrobial susceptibility testing (RAST) allows early detection of resistance directly from positive blood cultures, potentially improving outcomes in bloodstream infections (BSIs). We evaluated the performance of EUCAST RAST for Gram-negative ESKAPEEc pathogens and characterized carbapenemase genes in carbapenem-resistant Klebsiella pneumoniae (CRKP). Methods: A total of 354 positive blood cultures were screened, including 51 monomicrobial Gram-negative ESKAPEEc isolates. RAST results at 4, 6, 8, and 16–20 h were compared with standard antimicrobial susceptibility testing (AST) obtained using the BD Phoenix™ system. Categorical agreement (CA) and error frequency were calculated. Multiplex PCR and Sanger sequencing were performed on 15 CRKP isolates to identify carbapenemase genes and allelic variants. Results: 51 Gram-negative ESKAPEEc isolates met the inclusion criteria for RAST (15 E. coli, 19 K. pneumoniae, 11 A. baumannii, and 6 P. aeruginosa). Overall performance varied markedly by species and antibiotic. E. coli showed frequent unreadable or ATU zones at early timepoints and wide CA variability (50–100%), with high very major error (VME) rates for AMP, TZP, and CAZ, particularly at 6–8 h. K. pneumoniae displayed consistently high CA (mostly 100%) for carbapenems, CAZ, and TZP. A. baumannii demonstrated excellent agreement (100% for most agents), except for GEN at 6–8 h. P. aeruginosa could be evaluated only at 16–20 h, showing high CA for AMK, CAZ, and CIP; lower CA for MEM (83%); non-calculable CA for IMI due to universal ATU readings; and a CA value of 0% for TZP due to the predominance of the ATU results. VMEs ranged from 0% to 26.1% across species and reading times, but carbapenems did not generate VMEs. Molecular analysis revealed bla_KPC in 66.7%, bla_NDM in 46.7%, and bla_OXA-48 in 33.3% of isolates, with co-occurrence in several strains. Sequencing identified bla_KPC-2 and bla_NDM-1 as the predominant variants, with one isolate harboring bla_NDM-5. Conclusions: EUCAST RAST markedly accelerates susceptibility reporting from positive blood cultures, but its accuracy is species- and time-dependent. Performance was excellent for K. pneumoniae (including CRKP) and A. baumannii and acceptable for P. aeruginosa at 16–20 h. In contrast, E. coli showed frequent ATU results at early timepoints and high ME/VME rates, making readings before 8 h unreliable for clinical decisions. Overall, RAST can effectively support rapid antimicrobial stewardship when species-specific limitations are recognized, and early-timepoint results are interpreted with caution. Full article

This paper proposes a quantum multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for image transmission, which combines quantum multi-qubit encoding with spatial and frequency diversity to enhance noise resilience and image quality. The system utilizes joint photographic experts group (JPEG), high efficiency image file format (HEIF), and uncompressed images, which are first source-encoded (if applicable) and then processed using classical channel encoding. The channel-encoded bitstream is mapped into quantum states via multi-qubit encoding and transmitted through a 2 × 2 MIMO system with varied diversity schemes. The spatially mapped qubits undergo the quantum Fourier transform (QFT) to form quantum OFDM subcarriers, with a cyclic prefix added before transmission over fading quantum channels. At the receiver, the cyclic prefix is removed, the inverse QFT is applied, and the quantum MIMO decoder reconstructs spatially diverged quantum states. Then, quantum decoding reconstructs the bitstreams, followed by channel decoding and source decoding to recover the final image. Experimental results show that the proposed quantum MIMO-OFDM system outperforms its classical counterpart across all evaluated diversity configurations. It achieves peak signal-to-noise ratio (PSNR) values up to 58.48 dB, structural similarity index measure (SSIM) up to 0.9993, and universal quality index (UQI) up to 0.9999 for JPEG; PSNR up to 70.04 dB, SSIM up to 0.9998, and UQI up to 0.9999 for HEIF; and near-perfect reconstruction with infinite PSNR, SSIM of 1, and UQI of 1 for uncompressed images under high channel noise. These findings establish quantum MIMO-OFDM as a promising architecture for high-fidelity, bandwidth-efficient quantum multimedia communication. Full article

Ranking research groups plays a crucial role in various contexts, such as ensuring the fair allocation of research grants, assigning projects, and evaluating journal editorial boards. In this paper, we analyze the distribution of h-indexes within research groups and propose a single metric to quantify their overall performance, termed the α-index. This index integrates two complementary aspects: the homogeneity of members’ h-indexes, captured by the Gini coefficient (g), and the h-group, an extension of the individual h-index to groups. By combining both uniformity and collective research output, the α-index provides a consistent and equitable metric for comparative evaluation, essentially calculated as the average relative h-group multiplied by $(1-g)$ and normalized by the maximum value of this quantity across all analyzed groups. We describe the full procedure for computing the index and its components and illustrate its application to computer science conferences, where program committees are compared through a resampling procedure that ensures fair comparisons across groups of different sizes. Additional results are presented for postgraduate programs, further demonstrating the method’s applicability. Correlation analyses are used to establish rankings; however, our primary goal is to recommend a fairer index that reduces deviations from those currently used by governmental agencies to evaluate conferences and graduate programs. The proposed approach offers a more nuanced assessment than simply averaging members’ h-indexes and can be applied broadly–for example, to university departments and research councils–contributing to a more equitable distribution of research funding, an issue of increasing importance. Full article

Designing laboratory experiences that support both skill development and conceptual understanding is a persistent challenge in introductory chemistry education—especially within accelerated or compressed course formats. To address this need, we developed and implemented a systems-thinking-based laboratory module on error analysis for a large introductory chemistry course at Brown University, composed primarily of first-year students (approximately 150–200 students in the spring semesters). Unlike traditional labs that isolate single techniques or concepts, this module integrates calorimetry, precipitation reactions, vacuum filtration, and quantitative uncertainty analysis into a unified experiment. Students explore how procedural variables interact to affect experimental outcomes, promoting a holistic understanding of accuracy, precision, and uncertainty. The module is supported by multimedia pre-lab materials, including faculty-recorded lectures and interactive videos developed through Brown’s Undergraduate Teaching and Research Awards (UTRA) program. These resources prepare students for hands-on work while reinforcing key theoretical concepts. A mixed-methods assessment across four semesters (n > 600) demonstrated significant learning gains, particularly in students’ ability to analyze uncertainty and distinguish between accuracy and precision. Although confidence in applying significant figures slightly declined post-lab, this may reflect increased awareness of complexity rather than decreased understanding. This study highlights the educational value of integrating systems thinking into early-semester laboratory instruction. The module is accessible, cost-effective, and adaptable for a variety of institutional settings. Its design advances chemistry education by aligning foundational skill development with interdisciplinary thinking and real-world application. Full article

The main objective of this article is to conduct a literature review on the use of open innovation (OI) for green transition to identify tools and methods that can make green transition more effective, efficient, and socially acceptable. This review is accompanied by an attempt to answer the following research questions: R1. How can open innovation be used in the economy and by individual entities to achieve the goals of the green transition? R2. How can individual stakeholders be activated and motivated to participate in the process of creating open innovation for the green transition? and R3. What are the real effects of using open innovation on a macroeconomic, social, and individual scale? The results allow concluding that OI is used by enterprises, cities, regions, and entire economies. Among the methods of activating and motivating individual stakeholders to engage in the process of creating OI for green transition, the following can be selected: (1) internal resources and competencies (knowledge management, internal programs, open leadership, trust, complementarity of resources); (2) partnership characteristics (modern business models, involvement of partnership intermediaries, strengthening relationships with suppliers and customers, involvement of prosumers, cooperation with universities and research institutions); (3) external legal and regulatory conditions (protection of intellectual property rights, pro-innovation and pro-environmental education systems, creation of a legal framework for cooperation between science and business); and (4) external technical and organizational solutions (online platforms, social media, Living Labs, external sources of knowledge). The most frequently mentioned individual effects of open innovation in the energy sector include: improved efficiency, effectiveness and competitiveness in environmental management and the implementation of sustainable development, as well as the use of modern technologies. At the economic level, OI supports investment and economic growth. It can also have a positive impact on reducing energy poverty and developing renewable energy sources, including in emerging economies. This form of innovation also promotes social integration and the creation of social values. The findings of this review can be utilized by scholars to identify current and future research directions. They may also prove valuable for practitioners as both an incentive to engage in open innovation and guidance for its design and implementation. Furthermore, the results can contribute to disseminating knowledge about open innovation and its role in the green transformation. Full article

Even though many nations invest heavily in education to boost economic growth and development, evidence continues to show that university graduates struggle to find employment and eke out a living, obscuring our understanding of the true value of education. Comprehending the experiences of university graduates can assist in determining the support needed for their personal and economic growth. However, research in Zimbabwe on the experiences of graduates after university education is limited. This study explores the experiences of 14 university graduates, drawn from one university in Zimbabwe, who were in their fifth year after graduating. Data were generated from semi-structured interviews and analysed through the lens of credentialism theory. Findings revealed a significant discrepancy between education and employment wherein the value of education is being re-evaluated by people, with many questioning the assumption that higher education guarantees job opportunities. The disconnect highlights the need for policymakers to reassess the way education is delivered and aligned with labour market needs. The study proposes that universities should draw insights from industry leaders when crafting their curriculum and determining enrolment figures. The research extends scholarship on the interplay between education and employment, thereby guiding educational policy formulation in developing countries. Full article