Systematic reviews are essential for evidence-based decision-making in endodontics, and their number has grown substantially in recent years. However, little is known about the publication patterns, methodological features and citation impact of these studies. To perform a bibliometric analysis of evidence synthesis reviews (including systematic, scoping, bibliometric and umbrella reviews) in endodontics published between 2018 and 2023, and to evaluate the associations between citation impact and demographic, article-related, author-related and journal-related variables. This bibliometric analysis was reported in accordance with the PRISMA 2020 guidelines and was registered in the Open Science Framework (https://osf.io/jf9et/). A comprehensive search was conducted in five databases (PubMed, Embase, Web of Science, Scopus and Cochrane Library). Inclusion criteria encompassed systematic, scoping, umbrella and bibliometric reviews in endodontics. Citation data were extracted from Scopus and Google Scholar. Data analysis included descriptive statistics and negative binomial regression to assess associations with citation counts. Of 9683 records identified, 511 endodontic reviews met the inclusion criteria. Most were published in 2022-2023, predominantly by authors from Asia, Europe and the Americas. Brazil had the highest publication volume, while the USA led in citations. PRISMA adherence was high (90%), but funding and conflict of interest disclosures were infrequent. Citation impact was positively associated with earlier publication year, the last author's h-index, the number of included studies and journal CiteScore. Methodological factors such as protocol registration and article-related variables like open access were not significantly associated with citations after adjustment. The citation impact of endodontic evidence synthesis reviews is primarily influenced by temporal factors, author academic standing and journal prestige rather than methodological rigour alone. These findings reveal a disconnect between indicators of research quality and citation performance and highlight the necessity of promoting transparency as a scientific value rather than as a surrogate for visibility. This study was registered in the Open Science Framework (https://osf.io/jf9et/).

Introduction Sleep quality is often thought to be a key determinant of cognitive performance, particularly in older adults who experience age-related changes in sleep architecture. However, the extent to which nightly variations in sleep quality impact next-day cognitive performance remains unclear—in part because it has only recently become practical to measure sleep over multiple nights. Methods In this study, we used an in-home wearable electroencephalography (EEG) device to monitor sleep patterns over ~10 nights in 17 healthy older adults, assessing metrics of sleep quality such as wake after sleep onset and the density of slow oscillations and sleep spindles. Next-day cognitive performance was evaluated using two computerized neuropsychological tasks measuring executive functions (inhibition and cognitive flexibility), and their relationships to sleep metrics were explored. Results Although participants placed the EEG device themselves, a high proportion of sleep data was usable (~71%), and clear nightly variations in sleep quality were captured. Sleep recordings showed considerable variability in sleep quality metrics across nights, with large inter-individual differences. However, we found no effects of either macro- or microarchitectural sleep metrics on executive task outcomes the following day. Discussion These results do not rule out the possibility that some aspects of cognitive performance may be affected by daily fluctuations in sleep quality; however, they suggest that inhibition and cognitive flexibility, which underlie reasoning and problem solving, may be relatively resilient to nightly sleep variability in older adults. The findings also demonstrate the feasibility of using emerging portable devices to extend sleep studies at home and over multiple nights in older adults, while providing variance estimates and effect sizes to guide power and sample size planning for future studies.

Impact of chronic cold stress on metabolism, colostrum quality, and lamb performance in locally adapted ewes.

Water sustainability plays a critical role in achieving the Sustainable Development Goals (SDGs), as it influences human well-being, ecosystem integrity, and long-term development pathways. Over the past three decades, a substantial body of research has emerged on water sustainability; however, there remains a limited synthesis of how sustainability has been assessed, how assessment approaches have evolved, and the extent to which they align with the multidimensional intent of the SDGs. This study addresses the gap by combining a systematic review conducted using the PRISMA framework and bibliometric analysis from 1995 to 2025. The results show a marked acceleration in research output after 2015 following the formal adoption of the SDGs, with concentrations in a small number of countries and research hubs. Water sustainability assessment is mainly shaped by technically oriented indicator-based frameworks that emphasise water availability, water quality, and management performance. While these approaches have enabled comparability and methodological consistency, they often provide a partial representation of sustainability with limited integration of governance processes, social equity, cultural contexts, indigenous knowledge, and ecosystem services. The findings highlight the need for assessment approaches that go beyond technical metrics to more integrative and context-sensitive frameworks that can inform policy, support adaptive decisions, and reflect the interconnected nature of sustainable development.

This study aimed to enhance ethyl hexanoate production in strong-flavor baijiu by developing a synergistic yeast co-culture system and investigating the underlying metabolic basis for flavor improvement. Wickerhamomyces anomalus CHXQ, a strain with relatively high ethyl hexanoate-producing capacity, was isolated from Daqu and co-cultured with Saccharomyces cerevisiae CH1-a. After optimization, ethyl hexanoate yield in the optimized co-culture liquid fermentation reached 10.03mg L-1, 2.33 times higher than Wickerhamomyces anomalus monoculture (4.31mg L-1). Transcriptomic analysis revealed upregulation of EEB1 and FAA2 in S. cerevisiae, suggesting enhanced precursor supply and intracellular esterification. In simulated solid-state fermentation, the co-culture system significantly increased alcohol (40.77%vol) and total ester content (0.80g L-1), with ethyl hexanoate rising to 1103.63mg L-1, representing a 1.53-fold increase over the control group. The co-culture system promotes ethyl hexanoate synthesis through metabolic interaction and transcriptional regulation, resulting in an enhanced ester profile conducive to improved baijiu flavor quality and fermentation performance.

The current investigation examined the effect of inclusion of Black soldier fly (Hermetia illucens; BSF) dry larvae in hens’ diet on egg quality and hen performance. A total of 260 brown egg-laying hens (RIR × RIW) were divided into four groups (65 hens/group; 5 pens/group) and fed with control and experimental diets (inclusion rates 3%, 6%, 9%). Although the four-week feeding period did not influence production parameters, yolk color responded positively to the test diets, showing a linear increase with the percentage of BSF inclusion rate (p < 0.001). This was reaffirmed by the increased levels of yolk carotenoids (astaxanthin and β-carotene) and α-tocopherol, with notable differences in 6% and 9% BSF-fed groups (p ≤ 0.003). Lipids are an important factor in carotenoid absorption and assimilation, and the combination of fat content and carotenoids in BSF suggests the potential of this system for egg enrichment. Along with the increased antioxidant levels, a novel finding is the positive correlation between BSF inclusion rates and hens’ egg yolk levels of C:15 and C:17 fatty acids, key players in the core mechanisms of cell health and longevity. Altogether, the results provide evidence of the potential of BSF for enrichment of eggs with carotenoids and vitamins with strong antioxidant activity, which would have a positive effect on human health.

Reproductive responses of Synodontis eupterus to different dietary melatonin levels under blue light conditions.

Pathlength effects on NIR Transflectance performance for polar and apolar liquid food analysis.

Cross-view discrepancy-driven dynamic weighting for missing view completion in incomplete multi-view clustering.

Insights into how milling methods affect the quality performance of proso millet dumplings: a perspective from starch hierarchical structure and technological properties

Compact bioretention cell for urban stormwater management: Assessment of hydrologic, hydraulic, and water quality performance via laboratory and SWMM modelling

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance.

The increasing adoption of digital systems in public sector organizations has positioned Customer Relationship Management (CRM) as a strategic instrument for improving service quality and organizational performance. This study aims to evaluate the organizational and strategic impacts of CRM implementation at Statistics Indonesia through the SILASTIK system. An applied research design with a case study approach was employed to examine CRM implementation within its institutional context. The evaluation was conducted using a multi-perspective performance evaluation framework focusing on organizational capital, human capital, customer retention, customer expansion, and customer perceived value. Data were collected through a structured questionnaire distributed to internal CRM users, open-ended survey responses, and a semi-structured interview with a key system stakeholder. Quantitative data were analyzed using descriptive analysis, while qualitative data were examined through thematic analysis and KPI categorization. The findings indicate that CRM implementation has contributed positively to organizational alignment, service coordination, and internal efficiency, particularly in terms of management commitment, knowledge sharing, and productivity. However, limitations were identified in behavior-oriented adoption, structured training mechanisms, system integration, and the systematic measurement of customer-oriented performance dimensions. Overall, the study demonstrates that CRM implementation at Statistics Indonesia has progressed beyond a technical system and now functions as an organizational enabler, although its strategic potential has not yet been fully realized. These findings provide empirical insights into CRM evaluation in public sector statistical institutions and offer a foundation for strengthening CRM governance and performance management.

Introduction: Sleep and recovery are essential for optimizing exercise performance. However, the effectiveness of melatonin supplementation in improving sleep quality and next-day physical performance remains uncertain. Research has demonstrated the ergogenic effect of melatonin (N-acetyl-5-methoxytryptamine) (MEL) in increasing exhaustive aerobic activity. Associated with the effects of MEL, adult tissue stem cells (mesenchymal stem cells) mediate homeostasis and regeneration of tissues and organs, integrating signaling cues and metabolic inputs with the release of exosomes and microRNAs to enhance athletic performance. Objective: It was demonstrated through a systematic review study the regulation of melatonin and gut microbiota by cellular and molecular metabolic pathways, such as microRNAs and exosomes, in the process of muscle regeneration and increased sports performance. Methods: The PRISMA Platform systematic review rules were followed. The research was carried out from July to August 2025 in the Scopus, Embase, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 136 articles were found, and 55 articles were evaluated in full and 29 were included and developed in the present systematic review study. Considering the Cochrane tool for risk of bias, the overall assessment resulted in 25 studies with a high risk of bias and 12 studies that did not meet GRADE and AMSTAR-2. Most studies showed homogeneity in their results, with X2=82.9%>50%. It was concluded that administering 6 mg of melatonin at night improved performance during high-intensity exercise the following day and enhanced perceived recovery up to 72 hours after exercise. Melatonin intake during training has beneficial effects on physical performance and protects tissues against the deleterious effects of reactive oxygen species and cellular damage. Furthermore, nocturnal melatonin supplementation during an athlete's intense training session alleviated oxidative stress, leukocytosis, and cellular damage, and improved performance recovery. Melatonin plays important roles in regulating the regenerative activities of mesenchymal stem cells, which, along with nutrients, modulate the activities of exosomes and microRNAs in the muscle regeneration process.

Association of sleep quality and memory performance among medical students in coastal South India

Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.

Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing to its speed, simplicity and minimally invasive nature1-9. However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation10-21. Although many artificial intelligence (AI)-powered systems have been proposed to improve consistency22-26, none have achieved fully autonomous, clinical-grade performance. Existing approaches serve as assistive tools and still rely on human oversight for interpretation and decision-making22-26. Here we present a clinical-grade autonomous cytopathology pipeline that combines high-resolution, real-time optical whole-slide tomography with edge computing to deliver end-to-end automation. The system achieves practical performance in imaging speed, quality and data volume, with localized data compression enabling streamlined storage and accelerated AI-driven analysis. In addition to supporting cell-level classification, the platform enables flow cytometry-like, population-wide morphological profiling for comprehensive interpretation of cellular distributions and patterns. A vision transformer achieved area under the receiver operating characteristic(ROC) curve(AUC) values exceeding 0.99 at the single-cell level for detecting low-grade squamous intraepithelial lesions (LSILs), high-grade squamous intraepithelial lesions (HSILs) and adenocarcinoma. In a multicentre evaluation of 1,124 cervical liquid-based cytology samples across four centres, the AI model achieved slide-level AUC values of 0.86-0.91 for LSIL+ and 0.89-0.97 for HSIL+, with LSIL counts correlating strongly with human papillomavirus positivity and HSIL counts scaling with diagnostic severity. The system enables autonomous triage cytology, offering a foundation for routine, scalable and objective diagnostics.

Purpose This study aims to systematically investigate the effects of alumina powder properties (including solid loading and particle size distribution), photosensitive resin formulation and additives on the rheological behavior of ceramic slurries for digital light processing (DLP) 3D printing, as well as their subsequent influences on the performance of sintered alumina ceramics. Design/methodology/approach A comprehensive review was performed to clarify how key parameters govern slurry rheological behavior, along with their inherent links to printing quality (interlayer bonding, forming accuracy) and the final ceramics’ mechanical properties and microstructure. Moreover, recent core technical challenges in advancing alumina slurries for DLP 3D printing were outlined, and corresponding optimization strategies were put forward. Findings These parameters directly determine slurry rheology, which governs printing precision and interlayer bonding and thus ultimately controls the mechanical properties and structural reliability of sintered ceramics. Notably, machine learning is identified as a key tool for intelligent process optimization in this field. Originality/value This work systematically clarifies the critical correlations between slurry formulation, rheology, printing quality and final product performance. It provides a theoretical and technical foundation for manufacturing high-performance ceramic components via DLP, underscores the technology’s significant potential in advanced fields like aerospace and electronics and highlights machine learning’s role in advancing DLP technology toward intelligent, low-cost production.

Background/Objectives: Vision language models (VLMs) show strong potential for medical image understanding, but their large scale often limits practical deployment. This study investigates whether a compact VLM can be effectively adapted for dermatology, with a focus on explaining bacterial skin disease images. Methods: We curate a dataset derived from PMC-OA using the BIOMEDICA dataset and construct PMC-derma-VQA-bacteria by pairing images with inherited figure captions and synthetically generated question-answer (QA) supervision produced by Google's Gemini model. SmolVLM is fine-tuned under three supervision settings: QA-only, caption-only, and a combined QA+caption strategy. The models are evaluated on a held-out test set for both text-generation quality and diagnostic classification performance. Results: QA-only supervision yields the best report-generation performance, while the combined QA+caption setting achieves the highest classification accuracy (70.20%). Conclusions: Synthetic QA supervision can meaningfully enhance compact VLMs for medical image understanding and diagnostic support in dermatology.

The construction sector in developing countries continues to face persistent challenges related to cost overruns, projects delays, and compromised quality performance. In Oman, these concerns have hindered the ability of construction industry to achieve sustainable project success. This study aimed to empirically assess the effect of value management (VM) implementation on overall project success (OPS) in building projects in Oman using the partial least squares structural equation modelling (PLS-SEM). The questionnaire content was validated by 30 construction management experts using the content validity index (CVI). Data from 272 professionals across the construction sector in Oman were collected using a structured questionnaire to assess VM activities and OPS. The results indicated that VM implementation has a significant positive effect on OPS, with all phases except the creativity phase showing statistically significant relationships. The analysis showed a high coefficient of determination (R2 = 0.803) and a large effect size (f2 = 0.793), indicating that VM implementation explains over 80% of the variance in project success. Cost (β = 0.945) was the most significant success measure, followed by time (β = 0.883) and quality (β = 0.843). These results highlighted that the application of VM improves the performance of construction projects through the optimization of cost efficiency, improving scheduling reliability, and ensuring quality compliance. This study provided empirical evidence on the role of VM implementation in improving the overall project success by quantitatively linking VM phases to cost, time, and quality performance in developing countries. The findings offered practical guidance for policymakers and practitioners to integrate VM early in construction projects.