Search Results (6,355)

In recent years, increasing attention has been given to adjunctive therapies aimed at improving clinical outcomes in periodontal treatment. Among these, antimicrobial photodynamic therapy (aPDT) using the photosensitizer indocyanine green (ICG) has shown great promise. Objective: This narrative review seeks to summarize the existing evidence from randomized controlled trials, systematic reviews, and in vitro and in vivo studies on the use of antimicrobial photodynamic therapy with indocyanine green (ICG) as a photosensitizer, as well as the emerging approach of double-light aPDT with ICG, in the treatment of periodontitis. Materials and Methods: PubMed, Web of Science, and Cochrane Library databases were searched to find relevant articles regarding the topic. The articles were published in English between the years 2015 and 2025. The search used keywords such as (“indocyanine green” AND “antimicrobial photodynamic therapy” AND (“efficiency” OR “efficacy” OR “effect”) AND (“periodont*” OR “gingivitis” OR “gingival” OR “gum”). The articles chosen were required to evaluate the treatment outcomes of periodontitis with ICG-aPDT. Conclusions: ICG-aPDT represents an effective adjunct treatment in periodontal therapy. It can non-invasively target biofilms and minimize systemic action. It makes this technique an attractive adjunct in modern periodontology practice. This narrative review shows that ICG-aPDT can be integrated into comprehensive periodontal care as an adjunct measure promoting tissue healing. However, more high-quality clinical trials are needed to develop standardized protocols and demonstrate long-lasting benefits. Full article

Deep neural networks (DNNs) are highly effective across many domains but are sensitive to noisy or corrupted training data. Existing noise mitigation strategies often rely on strong assumptions about noise distributions or require costly retraining, limiting their scalability. Inspired by machine unlearning, we propose a novel framework that integrates attribution-guided data partitioning, neuron pruning, and targeted fine-tuning to enhance robustness. Our method uses gradient-based attribution to probabilistically identify clean samples without assuming specific noise characteristics. It then applies sensitivity-based neuron pruning to remove components most susceptible to noise, followed by fine-tuning on the retained high-quality subset. This approach jointly addresses data and model-level noise, offering a practical alternative to full retraining or explicit noise modeling. We evaluate our method on CIFAR-10 image classification and keyword spotting tasks under varying levels of label corruption. On CIFAR-10, our framework improves accuracy by up to 10% (F-FT vs. retrain) and reduces retraining time by 47% (L-FT vs. retrain), highlighting both accuracy and efficiency gains. These results highlight its effectiveness and efficiency in noisy settings, making it a scalable solution for robust generalization. Full article

The volume of scientific publications has increased exponentially over the past decades across virtually all academic disciplines. In this landscape of information overload, objective criteria are needed to identify high-impact research. Citation counts have traditionally served as a primary indicator of scientific relevance; however, questions remain as to whether they truly reflect the intrinsic quality of a publication. This study investigates the relationship between citation frequency and a wide range of editorial, authorship, and contextual variables. A dataset of 339,609 articles indexed in Scopus was analyzed, retrieved using the search query TITLE-ABS-KEY (management) AND LIMIT-TO (subarea, “Busi”). The research employed a descriptive analysis followed by two predictive modeling approaches: a Random Forest algorithm to assess variable importance, and a binary logistic regression to estimate the probability of a paper being cited. Results indicate that factors such as journal quartile, country of affiliation, number of authors, open access availability, and keyword usage significantly influence citation outcomes. The Random Forest model explained 94.9% of the variance, while the logistic model achieved an AUC of 0.669, allowing the formulation of a predictive citation equation. Findings suggest that multiple determinants beyond content quality drive citation behavior, and that citation probability can be predicted with reasonable accuracy, though inherent model limitations must be acknowledged. Full article

The increasing prevalence of nanoplastics (NPs) in food and their potential implications for human health have become a growing concern in scientific and public health discourse. Using the Scopus database, this bibliometric analysis provides a comprehensive overview of global research trends on NPs in food from 2015 to 2024. Results show a significant increase in publications and citations post-2019. China is the top-ranked country in terms of the number of publications, citations, collaborations, affiliations, and funding sponsors. The most impactful documents were review articles, indicating that this research field is currently in a synthesis stage. The most productive source was Science of the Total Environment, with 21 articles, while 9 of the top 10 most productive journals were published by Elsevier, highlighting the field’s concentration in high-impact outlets. The most prolific authors were Wang, J., and Li, Y; Li, Y. was also the author with the most citation influence, with a h-index of 9. Keyword co-occurrence analysis showed seven thematic clusters formed from 50 individual keywords; the dominant terms were microplastics, NPs, and human health. These findings illustrate an evolving and interdisciplinary research field centered on evaluating the risks and detection of NPs in food and their implications for public health. Full article

Hospital design plays a pivotal role in improving patient outcomes, enhancing clinical efficiency, and strengthening infection control. Since the outbreak of COVID-19, research in this field has expanded significantly, showing a marked trend toward interdisciplinary integration. In this study, bibliometric analysis was conducted using CiteSpace (version 6.2.R3) as the primary tool, with Excel and Tableau (version 2024.3) as supplementary software. A total of 877 documents on hospital design published between 1932 and 2025 were retrieved from the Web of Science Core Collection and analyzed from multiple perspectives. The analysis examined publication trends, collaborative networks, co-citation structures, disciplinary evolution, and keyword dynamics. The results indicate that the field has entered a phase of rapid development since 2019. Global collaboration networks are becoming increasingly multipolar; yet, institutional and author-level connections remain decentralized, with relatively low overall density. Evidence-based design (EBD) continues to serve as the theoretical foundation of the field, while emerging themes such as healing environments, biophilic design, and patient-centered spatial strategies have become major research hotspots. Increasingly, the field reflects deeper integration across disciplines, including architecture, medicine, nursing, and environmental science. This study provides a clearer picture of the developmental trajectory, knowledge base, and future directions of hospital design research, offering systematic insights and theoretical guidance for both scholars and practitioners. Full article

Background/Objectives: Tooth autotransplantation is a natural tooth replacement method that preserves the periodontal ligament, supporting root development and alveolar bone remodeling. Unlike dental implants, autotransplanted teeth maintain sensory function and adapt better to the mouth. Although once overlooked, new surgical, imaging, and regenerative advances have revived interest in this technique. This narrative review explores the renewed interest in tooth autotransplantation by assessing its benefits, success rates, technological advancements, and role in modern dentistry while evaluating its advantages, limitations, and potential impact on dental care. Methods: A narrative approach was used to provide a comprehensive and descriptive overview of current knowledge on tooth autotransplantation. A literature search was conducted in PubMed, Scopus, and Google Scholar using keywords such as “tooth autotransplantation”, “biological tooth replacement”, “periodontal ligament”, and “dental implants alternative”. English-language articles published between 2000 and 2025 were included, covering clinical trials, reviews, and relevant case reports. Selection focused on studies discussing biological mechanisms, clinical techniques, technological advances, and treatment outcomes. Results: Success rates range from 80% to 95%, with better predictability in younger patients with immature donor teeth. Long-term viability depends on preserving the PDL and performing atraumatic extractions. However, challenges such as root resorption, ankylosis, and appropriate case selection remain significant considerations. Technological advancements, including CBCT, 3D-printed surgical guides, and biomimetic storage media, have improved surgical precision and clinical outcomes. Conclusions: Tooth autotransplantation is an effective and cost-effective alternative to dental implants, particularly for growing patients or when implants are not suitable. While success depends on surgical skill and proper case selection, improvements in imaging and regenerative techniques have made outcomes more predictable. Future advances in bioengineering, AI-based planning, and regenerative therapies are likely to expand their use in modern dentistry. Full article

Background/Objectives: Traumatic dislocation of the lumbosacral facet joints without associated fractures is exceedingly rare in the pediatric population. Due to the unique anatomical and biomechanical features of the pediatric spine, such injuries present diagnostic and therapeutic challenges. This study aims to describe a rare case of bilateral L5–S1 jumped facets without fracture in a 13-year-old boy and to review the existing literature on pediatric traumatic facet dislocations. Methods: We performed a systematic review according to PRISMA guidelines, searching PubMed, Embase, Scopus, and the Cochrane Library up to 16 January 2025. Keywords included “pediatric traumatic spondylolisthesis” and “pediatric traumatic facet joint”. Eligible studies reported traumatic lumbosacral or thoracolumbar facet dislocations in patients aged <18 years. In addition, we report the clinical course, surgical management, and outcome of a representative case from our institution. Results: The systematic review identified 14 pediatric cases across 11 studies. Most patients were male (71.4%), with high-energy trauma as the primary mechanism. The L5–S1 level was most frequently involved (57.1%). Neurological impairment was present in 57.1% of cases. All patients underwent surgical treatment, with posterior fixation being the most common approach. Our case involved bilateral L5–S1 jumped facets without fracture, successfully treated with open reduction and posterior fusion. Postoperative recovery was favorable, with neurological improvement. Conclusions: Traumatic bilateral facet dislocation without fracture is an extremely rare but serious condition in pediatric patients. Early recognition and surgical stabilization are essential to prevent permanent neurological damage. This study reinforces the importance of advanced imaging and prompt multidisciplinary management in optimizing outcomes. Full article

Scholars are showing a growing interest in smart tourism, a promising trend in destination development. The current research studies have established a strong theoretical foundation on the functions of technology and the impacts of smart tourism on travelers. Nevertheless, little is known about the comprehensive and systemic effects on the growth of smart tourism in a particular destination. This study employs bibliometric analysis to examine the scientific literature of smart tourism research, based on 563 relevant publications retrieved from the leading database Web of Science Core Collection between 2000 and 2024 and analyzed using VOSviewer software 1.6.20 packages. The results show that the total number of relevant publications has gradually increased in recent years. Key journals include Tourism Management, Sustainability-Basel, and Annals of Tourism Research. The results also show that authors from the People’s Republic of China have the most publications and international co-authorships, while the most influential institution is the Hong Kong Polytechnic University. Moreover, research keywords have been identified, including smart tourism, smart cities, Internet of Things and big data. The research findings of this study provide valuable insights to further improve smart tourism research. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Background: The potential impact of maternal SARS-CoV-2 infection during pregnancy on the neurodevelopment of offspring has raised considerable concern. Emerging studies have evaluated various developmental domains in exposed infants, yet findings remain inconsistent. Objective: To synthesize current evidence regarding neurodevelopmental outcomes in infants born to mothers with confirmed SARS-CoV-2 infection during pregnancy. Methods: We conducted a narrative review following PRISMA guidelines. A literature search was performed in PubMed, Cochrane, and ScienceDirect using keywords including “COVID-19”, “pregnancy”, “neurodevelopment”, and “SARS-CoV-2”. Nineteen studies were included. Data were extracted regarding study design, sample size, timing of exposure, age at assessment, developmental tools used, and key findings. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Among 19 included studies, 12 reported at least some neurodevelopmental delays, particularly in motor and language domains. However, these delays were generally mild, domain-specific, and often not statistically significant. Seven studies, most of which were high-quality and low-risk, reported no significant differences between exposed and unexposed groups. Assessment tools and follow-up durations varied widely, limiting comparability. Conclusions: Current evidence does not support a consistent association between in utero SARS-CoV-2 exposure and an unfavorable neurodevelopmental outcome up to 24 months. However, heterogeneity in methods and short-term follow-up warrant further high-quality longitudinal research. Full article

In Italy, several changes to academic and professional standards and rules in kinesiology and sport have recently occurred. On the university side, no data collection has started regarding these changes and effects on specific scholars. The aim of this study was to evaluate the opinions of Italian university scholars in Exercise and Sport Sciences regarding recent disciplinary reclassifications, the emergence of the kinesiologist as a formal profession, and related curricular updates. Specifically, this study aimed to measure scholars’ views on the usefulness of unification, hybridization with other fields of knowledge, interdisciplinarity with pedagogy, the distinctiveness of undergraduate education in light of the new kinesiologist profile, and the inclusion of Technical and Laboratory Activities (TLA) credited through the European Credit Transfer System (ECTS). These aspects were explored through an eight-question survey offering three multiple-choice answers. An exploratory survey was distributed to a defined population of 261 Italian scholars (48 full professors, 137 associate professors, and 76 researchers). A total of 83 responses were collected: 14 full professors, 45 associate professors, and 24 researchers (response rate: 31.8%). Descriptive statistics and inferential analyses (Chi-Square tests, Cramér’s V, and Pearson/Spearman correlations) were conducted. Results indicated that 72.3% perceived overlap between pedagogical and medical disciplinary groups, and 85.5% considered practical/laboratory activities essential to the kinesiologist’s role. Significant differences in keyword-sharing perceptions across academic ranks emerged (p = 0.012; V = 0.3), and a near-significant trend was found regarding the importance of discipline-aligned research (p = 0.058; V = 0.3). Full agreement was found on the use of updated scientific evidence in lectures (100%), and 81.9% supported standardized education for the kinesiologist profession (Q6). Positive correlations were observed between support for keyword sharing and belief in its usefulness for promoting interdisciplinarity among full professors (r = 0.58, p = 0.02), associate professors (r = 0.68, p < 0.01), and researchers (r = 0.83, p < 0.01). Conversely, negative correlations emerged between the importance placed on practical activities and support for interdisciplinarity among associate professors and researchers, with values ranging from r = −0.31 to −0.46. The results are significant and tended toward autonomy from pedagogy, training aligned with the bachelor’s and master’s degree kinesiologist, and interdisciplinarity inherent in typical Exercise and Sport Sciences (ESS) keywords. This study should be replicated to increase the sample and to expand the ad hoc questionnaire to other issues. These findings highlight the need for greater alignment between academic training, disciplinary definitions, and professional practice through shared epistemological frameworks and updated descriptors that reflect scientific and labor market developments. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article

With the increasing attention to China’s ecological environment protection and the prominence of lake water environment problems, the impact of soil erosion on lake ecosystems has become an important research topic for regional sustainable development. Based on the CiteSpace bibliometric method, this study systematically analyzed 225 research articles on the impact of soil erosion on the water environment of lakes in China in the core collection of Web of Science from 1998 to 2025, aiming to reveal the research hotspots, evolution trends and regional differences in this field. The results show that China occupies a dominant position in this field (209 papers), and the Chinese Academy of Sciences is the core research institution (93 papers). The research hotspots show obvious policy-driven characteristics, which are divided into slow start periods (1998–2007), accelerated growth periods (2008–2015), explosive growth periods (2016–2020) and stable development periods (2021–2025). A keyword cluster analysis identified nine main research directions, including sedimentation effect (#0 cluster), soil loss (#2 cluster) and nitrogen and phosphorus migration (#11 cluster) in the Three Gorges Reservoir area. The study found that the synergistic effects of climate change and human activities (such as land use change) are becoming a new research paradigm, and the Yangtze River Basin, the Loess Plateau and the Yunnan–Guizhou Plateau constitute the three core research areas (accounting for 72.3% of the total literature). Future research should focus on a multi-scale coupling mechanism, a climate resilience assessment and an ecological engineering effectiveness verification to support the precise implementation of lake protection policies in China. This study provides a scientific basis for the comprehensive management of the soil erosion–lake water environment system, and also contributes a Chinese perspective to the sustainable development goals (SDG6 and SDG15) of similar regions in the world. Full article

Background/Objectives: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. Methods: This systematic review followed PRISMA guidelines to ensure a robust methodology. A comprehensive search was conducted in June 2025 across Embase, Medline, Web of Science, medRxiv, Google Scholar, and IEEE databases, using MeSH terms, relevant keywords, and Boolean logic. Eligible studies were original research articles published in English between 2024 and 2025, focusing on AI applications in robotic cancer surgery using real patient data. Studies were excluded if they were non-peer-reviewed, used synthetic/preclinical data, addressed non-oncologic indications, or explored non-robotic AI applications. This approach ensured the selection of studies with practical clinical relevance. Results: The search identified 989 articles, with 17 duplicates removed. After screening, 921 were excluded, and 37 others were eliminated for reasons such as misalignment with inclusion criteria or lack of full text. Ultimately, 14 articles were included, with 8 using a retrospective design and 6 based on prospective data. These included articles that varied significantly in terms of the number of participants, ranging from several dozen to several thousand. These studies explored the application of AI across various stages of robotic oncologic surgery, including preoperative planning, intraoperative support, and postoperative predictions. The quality of 11 included studies was very good and good. Conclusions: AI significantly supports robotic oncologic surgery at various stages. In preoperative planning, it helps estimate the risk of conversion from minimally invasive to open colectomy in colon cancer. During surgery, AI enables precise tumor and vascular structure localization, enhancing resection accuracy, preserving healthy tissue, and reducing warm ischemia time. Postoperatively, AI’s flexibility in predicting functional and oncological outcomes through context-specific models demonstrates its value in improving patient care. Due to the relatively small number of cases analyzed, further analysis of the issues presented in this review is necessary. Full article

Traditional encryption prioritises confidentiality but complicates search operations, requiring decryption before searches can be conducted. The public key encryption with keyword search (PEKS) scheme addresses this limitation by enabling authorised users to search for specific keywords within encrypted data without compromising the underlying encryption. This facilitates efficient and secure data retrieval without the need to decrypt the entire dataset. However, PEKS is susceptible to the keyword guessing attack (KGA), exploiting the deterministic nature of the PEKS trapdoor so that the adversary can correctly guess the keyword encrypted in a trapdoor. To enhance PEKS security to counter a KGA, various schemes have been proposed. A notable one is public key authenticated encryption with keyword search (PAEKS). PAEKS combines authentication and encryption with keyword-based search functionalities, ensuring data source authentication, encrypted information security, and keyword-based searches. However, many existing PAEKS schemes rely on computationally exhaustive bilinear pairing. In this paper, we propose a PAEKS scheme based on k-resilient identity-based encryption without bilinear pairing. By using the provable security approach, we show that our proposed PAEKS scheme satisfies both ciphertext privacy and trapdoor privacy. We present a comparison of the computation cost of our proposed PAEKS scheme with the existing PAEKS schemes and highlight its efficiency, particularly in the $\mathsf{Test}$ algorithm, where it achieves the fastest execution time. By performing experiments using the real-world Enron Email dataset, we show that the proposed scheme is efficient. Full article