Search Results (5,229)

Skin diseases, affecting one-third of the population, are a growing global health problem. The complexity of skin architecture, along with diverse symptomatology and intricate pathogenesis of dermatological disorders, highlights the urgent need for novel therapeutic strategies. Effective treatment of impaired wound healing and chronic skin diseases, including atopic dermatitis and psoriasis, remains challenging. Phytoterapeutics are increasingly investigated for their dermatologic potential, with numerous natural products of established use. Proanthocyanidins (PACs), a subclass of polyphenolic compounds, renowned for their anti-inflammatory and antioxidant properties, are promising candidates for novel solutions. This review article synthesizes the recent 25 years of research on biomolecular mechanisms, pharmacological effects, and phytochemical aspects of PACs, in the context of treating inflammatory-related skin problems. The available data highlight pro-regenerative, pro-angiogenic, antioxidative, and anti-inflammatory effects of PACs in accelerating wound closure. Preclinical data suggest their potent ability to mitigate chronic skin inflammatory disorders, including psoriasis and atopic dermatitis. Moreover, their photoprotective properties translate to the prevention of UV-induced skin inflammation. However, critical knowledge gaps remain regarding clinical verification and structure-activity relationships of PACs as dermatologic agents. Further optimization of topical formulation systems for PACs is also pressingly needed. Bridging traditional phytotherapy with novel discoveries in molecular pharmacology and pharmaceutical technology could help to design innovative PAC-based approaches for treating inflammatory skin diseases and impaired wound healing. Full article

Genome sequencing has possibly been the greatest step in the development of advanced tools for animal genetic improvement: knowledge of gene sequences and use of haplotype markers for productivity traits can provide important improvements in yield production and optimisation of reproductive program. Next-generation and, more recently, third-generation sequencing techniques enormously increased the ability to produce sequences from single individuals and increased the interest in exome or whole-genome sequencing as an alternative to SNP chips in breeding programs as these techniques allowed for the capture of a wider range of variations, including characterisation of rare variants, structural variations, and copy number changes. Here, we present a procedure, based on fast de novo assembly and a scaffolding step, to quickly build an almost complete genome starting from long reads obtained in a single sequencing run. The procedure, applied to sequences from five water buffaloes, was able to independently build, for each individual, an almost complete high-quality genome with highly continuous chromosome sequences; in most cases, over 90% of the length of the reference chromosome was covered by less than ten long contigs. Unlike other pipelines based on slower assemblers or which require many sequencing data, in 1–2 days, the proposed procedure can go from a single run to continuous genome assembly, supporting fast analysis of large chromosome structures, potentially useful for improving animal breeding and productivity. Full article

Background/Objectives: Chronic non-malignant pain (CNMP) affects 46.4% of adults in Saudi Arabia and often requires interdisciplinary care, including mental health services. Despite this need, mental health integration remains limited. This study explored healthcare providers’ perceptions of integrating mental health services into CNMP management and identified barriers and facilitators to interdisciplinary collaboration. Methods: A cross-sectional survey was conducted among 114 healthcare providers across Saudi Arabia. Using the Theoretical Domains Framework (TDF), domains such as knowledge, skills, beliefs about capabilities and consequences, reinforcement, and social influences were assessed. Data were analyzed using descriptive statistics, correlation analyses, and multiple regression. Results: Positive perceptions of mental health integration were significantly associated with beliefs about capabilities (r = 0.31, p = 0.001) and beliefs about consequences (r = 0.40, p < 0.001), as well as skills (r = 0.30, p = 0.001) and reinforcement (r = 0.26, p = 0.005). Multiple regression confirmed beliefs about capabilities (B = 0.208, p = 0.001) and consequences (B = 0.237, p < 0.001) as independent predictors, explaining 31.9% of the variance in perceptions (R² = 0.319, adjusted R² = 0.285). Emotional responses, such as stress, were potential barriers but did not independently predict perceptions. Systemic challenges included limited referral pathways and insufficient mental health resources. Conclusion: Confidence in professional abilities and recognition of the benefits of collaboration are key drivers of positive perceptions toward mental health integration in CNMP care. Interventions that enhance provider confidence, emphasize interdisciplinary benefits, and strengthen organizational support may improve engagement with mental healthcare services in Saudi Arabia. Full article

The purpose of this study was two-fold: (a) to examine the joint contribution of teachers’ knowledge of foundational language and literacy concepts and their perceived ability to teach reading to their students’ reading growth, and (b) to examine whether the effects of these factors were mediated by teachers’ perceived ability to differentiate instruction. A total of 32 language arts teachers and their 582 Grade 3 to 9 students (48% female) participated in the study. Teachers completed a survey on their knowledge of phonological awareness, phonics and morphology, and also rated their ability to teach different reading skills and to differentiate reading instruction. Children were assessed at the beginning and end of the school year on the Test of Word Reading Efficiency-2 and on the Test of Silent Reading Efficiency and Comprehension. Results of multilevel modeling indicated that teachers’ knowledge had a direct effect on students’ performance at the end of the school year, even after controlling for students’ earlier reading ability. Teachers’ perceived ability did not predict students’ reading growth either directly or indirectly. Taken together, these findings suggest that we need to invest in increasing teachers’ knowledge around foundational literacy skills. Full article

This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakerski–Fan–Vercauteren FHE scheme. To the best of our knowledge, this study represents the first systematic exploration of ChatGPT’s ability to implement these cryptographic algorithms. Fully homomorphic encryption, despite its theoretical and practical significance, poses significant challenges due to its computational complexity and efficiency requirements. This study evaluates ChatGPT’s capability as a development tool from both algorithmic and implementation perspectives. At the algorithmic level, ChatGPT demonstrates a solid understanding of the Rring Learning-with-Errors lattice encryption scheme but faces limitations in comprehending the intricate structure of the Brakerski–Fan–Vercauteren FHE scheme. At the code level, ChatGPT can generate functional C++ implementations of both encryption schemes, significantly reducing manual coding effort. However, debugging and corrections remain necessary, particularly for the more complex Brakerski–Fan–Vercauteren scheme, where additional effort is required to ensure correctness. The findings highlight ChatGPT’s potential and limitations in supporting cryptographic algorithm development, offering insights into its application for advancing implementations of complex cryptographic systems. Full article

Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN) framework that introduces directionality into hypergraph learning through flexible spectral Laplacian formulations. Unlike fixed-Laplacian methods such as the Generalized Directed Hypergraph Neural Network (GeDi-HNN), a tunable q-parameter in our framework balances local identity preservation with global diffusion, enabling robust and generalizable feature propagation. Experiments on five benchmark datasets show that HONN consistently matches or outperforms state-of-the-art baselines, achieving 84% on NTU-2012, 87.4% on WebKB Texas, and 86.2% on Cornell, while maintaining computational efficiency. Ablation studies confirm the crucial role of Laplacian selection, activation functions, and q-tuning in shaping model performance. By unifying directionality and higher-order reasoning, HONN provides a scalable foundation for predictive modeling in domains such as knowledge graphs, spatio-temporal networks, and recommendation systems. Full article

The present study developed and evaluated an integrated disaster nursing education program combining functional training and simulator-based learning to address limitations of traditional, theory-driven approaches. Overall, 49 senior nursing students completed the program using a four-stage repeated-measures design. The findings indicated a substantial enhancement in disaster nursing knowledge over time. However, problem-solving ability, learning self-efficacy, and motivation exhibited improvement only in post hoc comparisons. This contradictory yet fundamental finding suggests that knowledge acquisition occurs more directly, whereas problem-solving and motivational competencies require cumulative practice, feedback, and contextual immersion. Educator reflections and student debriefings further underscored the significance of teamwork, communication, and scenario relevance in facilitating learning transfer. Despite its limitations, including a single-site, female-dominated sample, reliance on self-reported measures, and a brief follow-up period, this study makes a significant contribution to the field of disaster nursing education by presenting a sustainable and adaptable model. Incorporation of multi-institutional and longitudinal designs, as well as qualitative analyses of learning processes will be crucial in future studies. This will ensure the study’s generalizability and long-term impact. Full article

The maintenance of gene integrity is important for all types of cells, but, in the case of early embryonic cells, it is absolutely essential. This is because it influences not only the further development of the embryo but also, in some respects, the offspring. The occurrence and incorrect repair of cellular abnormalities after DNA damage during this period are the primary causes of fetal developmental disorders. If DNA damage occurs in germ cells or the fertilized oocyte and the DNA lesions are not satisfactorily repaired, this can lead to the occurrence of chromosomal aberrations during early embryogenesis and eventually to genetic instability during embryonic development. This developmental ability is related to the level of the DNA damage. Therefore, examining the events related to DNA damage response at the sub-cellular levels is of the utmost importance. In this context, subcellular diagnostics of such events during the selection of embryos with the highest implantation potential applied in the practice of assisted human reproduction are key to successful outcomes. It is important to apply new relevant knowledge from basic research to clinical practice, as well as considering new technical possibilities or trends in this area. The aim of this review is to provide a general overview of the molecular events associated with DNA damage in the early embryo and to outline the possible use of this basic knowledge in assisted reproduction procedures. Full article

Environmental dissemination of antibiotics is a pressing global challenge, driving ecological imbalances and the proliferation of antibiotic resistance genes (ARGs). Conventional treatment technologies often fail to fully eliminate these micropollutants or are cost-prohibitive for widespread use. In this context, phytoremediation—using plants and their associated microbiota to remove, transform, or immobilize contaminants—has emerged as an effective and promising, low-impact, and nature-based approach for mitigating antibiotic pollution in aquatic and terrestrial environments. This review provides a comprehensive synthesis of the physiological, biochemical, and ecological mechanisms by which plants interact with antibiotics, including phytoextraction, phytodegradation, rhizodegradation, and phytostabilization. This review prioritizes phytoremediation goals, with attention to high-performing aquatic (e.g., Lemna minor, Eichhornia crassipes, Phragmites australis) and terrestrial plants (e.g., Brassica juncea, Zea mays) and their ability to remediate major classes of antibiotics. This study highlights the role of rhizosphere microbes and engineered systems in phytoremediation, while noting challenges such as variable efficiency, phytotoxicity risks, limited knowledge of by-products, and environmental concerns with antibiotic degradation. Future perspectives include the integration of genetic engineering, microbiome optimization, and smart monitoring technologies to enhance system performance and scalability. Plant-based solutions thus represent a vital component of next-generation remediation strategies aimed at reducing antibiotic burdens in the environment and curbing the rise in antimicrobial resistance. Full article

Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vector Autoregression (VAR) and Vector Autoregressive Integrated Moving Average (VARIMA), struggle with nonstationarity, temporal dependencies, inter-series correlations, and data anomalies such as trend shifts, seasonal variations, and missing data. Furthermore, their effectiveness in multi-step ahead forecasting is often limited. This article presents an Automated Machine Learning (AutoML) framework that provides an end-to-end solution for researchers who lack in-depth knowledge of time series forecasting or advanced programming skills. This framework utilizes Gated Recurrent Unit (GRU) networks, a variant of Recurrent Neural Networks (RNNs), to tackle multiple correlated time series forecasting problems, even in the presence of anomalies. To reduce complexity and facilitate the AutoML process, many model parameters are pre-specified, thereby requiring minimal tuning. This design enables efficient and accurate multi-step forecasting while addressing issues including missing values and structural shifts. We also examine the advantages and limitations of GRU-based RNNs within the AutoML system for multivariate time series forecasting. Model performance is evaluated using multiple accuracy metrics across various forecast horizons. The empirical results confirm our proposed approach’s ability to capture inter-series dependencies and handle anomalies in long-range forecasts. Full article

Amid the rise of the knowledge economy, accelerated informatization, and the emergence of artificial intelligence, Philosophy for Children (P4C) has been promoted as an effective educational project to enhance children’s cognitive development, especially higher-order thinking skills. However, empirical evidence regarding its efficacy remains inconclusive. This three-level meta-analysis synthesizes 53 effect sizes derived from 33 experimental and quasi-experimental studies involving 4568 participants to assess P4C’s cognitive effects and potential moderators. The results reveal a statistically significant and moderate-to-strong overall effect (g = 0.59). Significant and robust effects were specifically observed for reasoning, critical thinking, and creativity. Subgroup and meta-regression analyses identified sample size as a significant moderator: smaller samples tended to report larger effect sizes. Additionally, cultural context and session length showed marginally significant moderating effects. Crucially, P4C’s cognitive impact remained consistent across grade levels, research designs, and publication years, demonstrating robustness and stability across diverse implementation conditions. These findings provide updated and nuanced evidence for the effectiveness of P4C, underscoring its cross-contextual robustness and specific value in fostering cognitive abilities. Implications for policymakers, educators, and future researchers aiming to implement or investigate P4C in varied educational settings are discussed. Full article

Low salinity-enhanced oil recovery has gained universal recognition regarding its ability to provide an environmentally friendly and low-cost method of improved oil recovery. Research findings so far based on experimentation and simulation suggest that the success of the scheme stems considerably from double layer expansion and wettability enhancement, among others. However, while the double layer expansion and wettability effects have robust theoretical foundations that can be sought within the Mean Field Poisson–Boltzmann theory, there is hardly any published research work that has tackled this task. In this paper, we fill the knowledge gap by using the MFPB theory to calculate electric double layer (EDL) parameters as functions of salinity and to successfully correlate theoretical findings to literature-based experimental observations. Additionally, we have, for the first time integrated the concept of free energy of formation of the EDL in LSWFOR research, given its intimate relationship to EDL parameters. The theoretical findings are, therefore, indicators that theoretical foundations also provide reliable and alternative means of understanding and predicting the success of LSWFOR. Full article

In this paper, the iterative Gerchberg–Saxton (GS) phase-retrieval algorithm is employed to reconstruct the amplitude spread function (ASF) of hololenses (HLs) recorded on a sustainable PVA/acrylate-based photopolymer, Biophotopol, when working with a CCD sensor. The main objective of this work is to characterize the spatial resolution of HLs, which are key components in a wide range of optical systems, including augmented reality (AR) glasses, combined information displays, and holographic solar concentrators. The GS algorithm, known for its efficiency in phase retrieval without prior knowledge of the phase of the optical system, is used to reconstruct the ASF, which is critical for mitigating information loss during imaging. Spatial resolution is quantified by convolving the ASFs obtained with two resolution tests (objective and subjective) and analyzing the resulting image using a CCD sensor. The convolution process allows an accurate assessment of lens performance, highlighting the resolution limits of manufactured lenses. The results show that the iterative GS algorithm provides a reliable method to improve image quality by recovering phase and amplitude information that might otherwise be lost, especially when using CCD or CMOS sensors. In addition, the recorded hololenses exhibit a spatial resolution of 8.9 lp/mm when evaluated with the objective Siemens star chart, and 30 cycles/degree when evaluated with the subjective Random E visual acuity test, underscoring the ability of Biophotopol-based HLs to meet the performance requirements of advanced optical applications. This work contributes to the development of sustainable high-resolution holographic lenses for modern imaging technologies, offering a promising alternative for future optical systems. Full article

This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, and explanatory queries derived from a real offshore oilfield dataset. Results show that the dual-layer graph consistently outperforms a single-layer baseline. Answer accuracy improves from 0.65 to 0.70, faithfulness from 0.54 to 0.61, and context relevance from 0.69 to 0.72, confirming that the system retrieves factual parameters more reliably and provides conceptually grounded explanations. Gains in evidence recall and coverage are more modest, highlighting areas for further optimization. A case study illustrates the framework’s ability to expand petroleum terminology (e.g., “sandstone → clastic rock”), producing responses that are not only quantitatively more reliable but also qualitatively more informative. The dual-layer design effectively addresses the semantic consistency gap in petroleum QA, offering practical value for reservoir evaluation, lithology interpretation, and technical decision support. These findings demonstrate the potential of GraphRAG to enhance knowledge management and intelligent services in petroleum engineering. Full article

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments. Full article