Search Results (82)

Objective: This cross-sectional content analysis aimed to investigate how oil pulling is portrayed on YouTube Shorts, focusing on the types of speakers, claims made, and alignment with scientific evidence. The study further explored how the content may influence viewer perception, health behaviors, and the potential spread of misinformation. Methods: On 28 January 2025, a systematic search of YouTube Shorts was performed using the term “oil pulling” in incognito mode to reduce algorithmic bias. English language videos with at least 1000 views were included through purposive sampling. A total of 47 Shorts met the inclusion criteria. Data were extracted using a structured coding framework that recorded speaker type (e.g., dentist, hygienist, influencer), engagement metrics, stated benefits, oil type and regimen, the use of disclaimers or citations, and stance toward oil pulling rated on a 5-point Likert scale. Speaker background and nationality were determined through publicly available channel descriptions or linked websites, with user identities anonymized and ethical approval deemed unnecessary due to the use of publicly available content. In total, 47 videos met the inclusion criteria. Results: Of the 47 YouTube Shorts that met the inclusion criteria, most were posted by influencers rather than dental professionals. These videos predominantly encouraged oil pulling, often recommending coconut oil for 10–15 min daily and citing benefits such as reduced halitosis and improved gum health. However, a smaller subset advanced more extreme claims, including reversing cavities and remineralizing enamel. Notably, US-licensed dentists and dental hygienists tended to discourage or express skepticism toward oil pulling, assigning lower Likert scores (1 or 2) to influencers and alternative health practitioners (often 4 or 5). Conclusions: YouTube Shorts largely promote oil pulling through anecdotal and testimonial-driven content, often diverging from evidence-based dental recommendations. The findings reveal a disconnect between professional dental guidance and popular social media narratives. While some benefits like halitosis reduction may have limited support, exaggerated or misleading claims may result in improper oral hygiene practices. Greater engagement from dental professionals and improved health communication strategies are needed to counteract misinformation and reinforce oil pulling’s role, if any, as an adjunct—not a replacement—for standard oral care. Future studies should explore viewer interpretation, behavioral influence, and cross-platform content patterns to better understand the impact of short-form health videos. Full article

Drug-induced nephrotoxicity is a common and serious problem in clinical practice. We conducted a systematic review of studies reporting nephrotoxicity events associated with antibiotics approved since 2018. The agents assessed included aztreonam/avibactam, cefepime/enmetazobactam, cefiderocol, ceftobiprole, contezolid, gepotidacin, imipenem/cilastatin/relebactam, lascufloxacin, lefamulin, levonadifloxacin, plazomicin, and sulbactam/durlobactam. Literature searches were conducted in PubMed, Scopus, Web of Science, and major pharmacovigilance databases (Vigibase, FAERS, EudraVigilance, EMA, FDA, NMPA, PMDA, and CDSCO) in May 2025, along with reference citation tracking. Studies were included if they reported safety or adverse event data. The risk of bias was assessed using validated tools in accordance with the study design. Out of 2105 potentially relevant records, 74 studies met inclusion criteria, comprising 52 clinical trials, 17 observational studies, 1 registry-based study, 3 case series, and 1 case report. Nephrotoxicity was rarely reported for any of the newly approved antibiotics. No renal adverse events were found in the available studies for aztreonam/avibactam, levonadifloxacin, and contezolid. Most studies were of moderate to high quality; two were classified as low quality. However, nephrotoxicity was inconsistently assessed, with variable definitions and methodologies used. Although current data suggest a low frequency of nephrotoxicity, limitations in study design and reporting preclude firm conclusions. There is a need for post-marketing studies to better characterize renal safety. Clinicians should remain vigilant and continue to monitor for and report renal-related adverse events. Full article

This study presents a systematic bibliometric review of digital innovations in renewable energy-oriented power systems, with a focus on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. The objective is to evaluate the research landscape, trends, and integration potential of these technologies within sustainable energy infrastructures. Peer-reviewed journal articles published between 2020 and 2025 were retrieved from Scopus using a structured search strategy. A total of 23,074 records were initially identified and filtered according to inclusion criteria based on relevance, peer-review status, and citation impact. No risk of bias assessment was applicable due to the nature of the study. The analysis employed bibliometric and keyword clustering techniques using VOSviewer and MATLAB to identify publication trends, citation patterns, and technology-specific application areas. AI emerged as the most studied domain, peaking with 1209 papers and 15,667 citations in 2024. IoT and Data Analytics followed in relevance, contributing to real-time system optimization and monitoring. Blockchain, while less frequent, is gaining traction in secure decentralized energy markets. Limitations include possible indexing delays affecting 2025 trends and the exclusion of gray literature. This study offers actionable insights for researchers and policymakers by identifying converging research fronts and recommending areas for regulatory, infrastructural, and collaborative focus. This review was not pre-registered. Funding was provided by the Universidad Politécnica Salesiana under project code 005-01-2025-02-07. Full article

(1) Burn injuries demand multidisciplinary, evidence-based care, yet the extensive literature complicates timely decision making. Retrieval-augmented generation (RAG) synthesizes research while addressing inaccuracies in pretrained models. However, citation bias in sourcing for RAG often prioritizes highly cited studies, overlooking less-cited but valuable research. This study examines RAG’s performance in burn management, comparing citation levels to enhance evidence synthesis, reduce selection bias, and guide decisions. (2) Two burn management datasets were assembled: 30 highly cited (mean: 303) and 30 less-cited (mean: 21). The Gemini-1.0-Pro-002 RAG model addressed 30 questions, ranging from foundational principles to advanced surgical approaches. Responses were evaluated for accuracy (5-point scale), readability (Flesch–Kincaid metrics), and response time with Wilcoxon rank sum tests (p < 0.05). (3) RAG achieved comparable accuracy (4.6 vs. 4.2, p = 0.49), readability (Flesch Reading Ease: 42.8 vs. 46.5, p = 0.26; Grade Level: 9.9 vs. 9.5, p = 0.29), and response time (2.8 vs. 2.5 s, p = 0.39) for the highly and less-cited datasets. (4) Less-cited research performed similarly to highly cited sources. This equivalence broadens clinicians’ access to novel, diverse insights without sacrificing quality. As plastic surgery evolves, RAG’s inclusive approach fosters innovation, improves patient care, and reduces cognitive burden by integrating underutilized studies. Embracing RAG could propel the field toward dynamic, forward-thinking care. Full article

Background/Objectives: The World Health Organization (WHO) declared a global pandemic of COVID-19 caused by SARS-CoV-2 in March 2020. May 2023 was the month that ended the global pandemic. Pregnant females with COVID-19 are less likely to be symptomatic than non-pregnant patients, with nearly three-quarters being without symptoms. According to previous studies, even if somebody develops symptoms, they are usually mild, most commonly coughing (41%), fever (40%), and dyspnea (21%). Our study aims to search the literature systematically, especially case series and case reports published in Europe, and to summarize results about the kind of COVID-19 therapy in pregnant women and about outcomes in mothers and newborns. Methods: Our systematic review was registered at the International Prospective Register of Systematic Reviews (PROSPERO) with CRD42024566838. We searched PubMed/MEDLINE, Google Scholar, Web of Science, Scopus, and Serbian Citation Index (SCIndeks). In this study, case reports or case series with open, complete text that included full clinical records of the individuals identified with infection in pregnancy, thought to be caused by COVID-19, were used. Case series or case reports were eliminated if they (1) did not contain a full clinical report for every patient, or (2) included an individual who suffered from another viral infection other than COVID-19, so the clinical course and the outcome could not be precisely defined. We evaluated reporting bias and attrition bias. Results: Our study included 32 published studies (eight case series and 24 case reports) that included 56 individual cases. The oldest patient was 50 years old, and the youngest was 19 years old. The most common symptom initially was dry cough (n = 23; 41%), followed by fever (n = 21; 37%) and dyspnea (n = 10; 17%). In three patients, a lower level of thrombocytes was reported, with the lowest level of 86 × 10⁹. The most frequently used drugs in pregnant women with COVID-19 infection were azithromycin, lopinavir/ritonavir, hydroxychloroquine, as well as corticosteroids. Twenty-two patients were on mechanical ventilation. After all this reported therapy, ten women died, as well as seven newborns. Conclusions: From our results, we can conclude that mechanical ventilation correlates with cesarean section performed more frequently, as well as with a higher mortality rate of neonates. There are no significant data related to transplacental transmission of the virus. Generally, mortality in our group of patients (mothers) was 17%, which is similar to the general population death from COVID-19 infection. Full article

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI’s potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts. Full article

Children’s engagement with toys and play materials can contribute to the foundational cognitive processes that drive learning. Loose parts are interactive, open-ended materials originally not designed as toys but can be incorporated into children’s play (e.g., acorns, cardboard, and fabric). Practitioners and researchers widely endorse loose parts for fostering creativity, divergent thinking, and problem-solving skills. Despite these recommendations, research on their specific role in young children’s cognitive development remains limited. This systematic review examines how indoor loose parts play has been studied in relation to young children’s (0–6 years) cognitive development. Following PRISMA guidelines, searches in bibliographic databases and forward and backward citation tracking identified 5721 studies published until December 2024. We identified 25 studies and evaluated the quality and risk of bias. Studies focused on children’s general cognitive outcomes, language development, and specific cognitive subdomains, with many reporting positive associations between children’s play materials and cognitive development. However, five studies found no such associations, and another seven did not address the relationship between play materials and outcomes. Despite methodological variation across studies, our systematic review identified a relationship between play materials similar to loose parts and children’s problem-solving, creativity, academic skills (reading and math), and both convergent and divergent thinking. Notably, only one study explicitly used the term “loose parts.”Our review identified empirical and methodological gaps regarding the relationship between play materials and cognitive development, which can inform future research. Full article

Background/Objectives: Furocoumarins, chemical compounds found in many plant species, have a photosensitizing effect on the skin when applied topically and, by interacting with ultraviolet radiation (UVR), stimulate melanoma cells to proliferate. Whether dietary intake of furocoumarins acts as a melanoma risk factor has been investigated in several epidemiological studies, which are synthesized in our systematic review. Methods: The study protocol was registered with PROSPERO (registration number: CRD42023428596). We conducted an in-depth literature search in three databases coupled with forward and backward citation tracking and expert consultations to identify all epidemiological studies, irrespective of their design, addressing the association between a furocoumarin-containing diet and melanoma risk. We extracted information on the study details and results in a standardized manner and evaluated the risk of bias of the results using the Joanna Briggs Institute’s critical appraisal tools. Results: We identified 20 publications based on 19 different studies providing information on the association between dietary furocoumarin intake and melanoma risk. We refrained from a meta-analytical synthesis of the results because of the large heterogeneity in exposure assessment, operationalization of furocoumarin intake in the analyses, and analytical methods of the studies. In a qualitative synthesis, we found moderate evidence supporting the notion that dietary furocoumarin intake at higher levels acts as a risk factor for cutaneous melanoma. Conclusions: Our systematic review provides an overview of the current epidemiological evidence, but it could not clearly answer whether and to what extent dietary furocoumarin intake increases melanoma risk. Future epidemiological analyses focusing on this topic require more comprehensive dietary and UVR exposure data to better characterize the individual total furocoumarin intake and its interplay with UVR exposure patterns. Full article

Background: A significant concern for patients with Inflammatory Bowel Disease (IBD) is predicting and managing disease flares. While healthcare providers rely on biomarkers, providing conclusive patient advice remains challenging. This review explores the role of lifestyle, psychological health, and environmental exposures in the prediction and management of IBD flares. Methods: This review followed PRISMA guidelines (2020). A structured search was conducted in PubMed for articles published between 2012 and 2024, using free and Medical Subject Heading (MeSH) terms for predicting factors in IBD. Inclusion criteria included studies reporting primary data on modifiable clinical or environmental predictors of IBD relapse, excluding studies on post-operative investigations, treatment cessation, and pediatric or pregnant populations. The Mixed Method Appraisal Tool (MMAT) was used to assess the quality of the studies. Results: Out of 2287 identified citations, 58 articles were included. Several modifiable factors influencing disease flares were identified, including psychological stress, sleep disturbances, smoking, and nutrition. Poor sleep quality and mental health were linked to increased flare risks, while smoking was associated with higher relapse rates in Crohn’s disease. Environmental exposures, such as heat waves and high-altitude regions, also contributed. Predictive models integrating clinical, lifestyle, and psychological factors showed promising accuracy but require further refinement. Limitations of this review include the potential for publication bias, variability in flare definitions, and limited sample sizes Conclusions: Key predictors of IBD flares include dietary factors, psychological stress, poor sleep quality, and pharmacological influences. Personalized approaches integrating these predictors can optimize disease control and improve patient outcomes. Full article

Background: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in women of reproductive age, characterized by a broad spectrum of clinical, metabolic, and ultrasound findings. Over time, ultrasound has evolved into a cornerstone for diagnosing polycystic ovarian morphology (PCOM), thanks to advances in probe technology, 3D imaging, and novel stromal markers. The recent incorporation of artificial intelligence (AI) further enhances diagnostic precision by reducing operator-related variability. Methods: We conducted a narrative review of English-language articles in PubMed and Embase using the keywords “PCOS”, “polycystic ovary syndrome”, “ultrasound”, “3D ultrasound”, and “ovarian stroma”. Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. Results: Conventional 2D ultrasound remains essential in clinical practice, with follicle number per ovary (FNPO) and ovarian volume (OV) functioning as primary diagnostic criteria. However, sensitivity and specificity values vary significantly depending on probe frequency, cut-off thresholds (≥12, ≥20, or ≥25 follicles), and patient characteristics (e.g., adolescence, obesity). Three-dimensional (3D) ultrasound and Doppler techniques refine PCOS diagnosis by enabling automated follicle measurements, stromal/ovarian area ratio assessments, and evaluation of vascular indices correlating strongly with hyperandrogenism. Meanwhile, AI-driven ultrasound analysis has emerged as a promising tool for minimizing observer bias and validating advanced metrics (e.g., SA/OA ratio) that may overcome traditional limitations of stroma-based criteria. Conclusions: The continual evolution of ultrasound, encompassing higher probe frequencies, 3D enhancements, and now AI-assisted algorithms, has expanded our ability to characterize PCOM accurately. Nevertheless, challenges such as operator dependency and inter-observer variability persist despite standardized protocols; the integration of AI holds promise in further enhancing diagnostic accuracy. Future directions should focus on robust AI training datasets, multicenter validation, and age-/BMI-specific cut-offs to optimize the balance between sensitivity and specificity, ultimately facilitating earlier and more precise PCOS diagnoses. Full article

This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems’ trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human–computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject. Full article

Background/Objectives: The rapid development of Large Language Models (LLMs) presents promising applications in healthcare, including patient education. In anesthesia, where patient anxiety is common due to misunderstandings and fears, LLMs could alleviate perioperative anxiety by providing accessible and accurate information. This study explores the potential of LLMs to enhance patient education on anesthetic and perioperative care, addressing time constraints faced by anesthetists. Methods: Three language models—ChatGPT-4, Claude 3, and Gemini—were evaluated using three common patient prompts. To minimize bias, incognito mode was used. Readability was assessed with the Flesch–Kincaid, Flesch Reading Ease, and Coleman–Liau indices. Response quality was rated for clarity, comprehension, and informativeness using the DISCERN score and Likert Scale. Results: Claude 3 required the highest reading level, delivering detailed responses but lacking citations. ChatGPT-4o offered accessible and concise answers but missed key details. Gemini provided reliable and comprehensive information and emphasized professional guidance but lacked citations. According to DISCERN and Likert scores, Gemini had the highest rank for reliability and patient friendliness. Conclusions: This study found that Gemini provided the most reliable information, followed by Claude 3, although no significant differences were observed. All models showed limitations in bias and lacked sufficient citations. While ChatGPT-4o was the most comprehensible, it lacked clinical depth. Further research is needed to balance simplicity with clinical accuracy, explore Artificial Intelligence (AI)–physician collaboration, and assess AI’s impact on patient safety and medical education. Full article

The American Society of Clinical Oncology (ASCO) has released the principles for the responsible use of artificial intelligence (AI) in oncology emphasizing fairness, accountability, oversight, equity, and transparency. However, the extent to which these principles are followed is unknown. The goal of this study was to assess the presence of biases and the quality of studies on AI models according to the ASCO principles and examine their potential impact through citation analysis and subsequent research applications. A review of original research articles centered on the evaluation of predictive models for cancer diagnosis published in the ASCO journal dedicated to informatics and data science in clinical oncology was conducted. Seventeen potential bias criteria were used to evaluate the sources of bias in the studies, aligned with the ASCO’s principles for responsible AI use in oncology. The CREMLS checklist was applied to assess the study quality, focusing on the reporting standards, and the performance metrics along with citation counts of the included studies were analyzed. Nine studies were included. The most common biases were environmental and life-course bias, contextual bias, provider expertise bias, and implicit bias. Among the ASCO principles, the least adhered to were transparency, oversight and privacy, and human-centered AI application. Only 22% of the studies provided access to their data. The CREMLS checklist revealed the deficiencies in methodology and evaluation reporting. Most studies reported performance metrics within moderate to high ranges. Additionally, two studies were replicated in the subsequent research. In conclusion, most studies exhibited various types of bias, reporting deficiencies, and failure to adhere to the principles for responsible AI use in oncology, limiting their applicability and reproducibility. Greater transparency, data accessibility, and compliance with international guidelines are recommended to improve the reliability of AI-based research in oncology. Full article

Aim: The aim of this study was to evaluate and compare the efficacy of Triphala and chlorhexidine mouthwashes in reducing gingivitis and dental plaque in children. Methodology: A literature search was confined to the English language using MeSH terms conferring to PICO format in PubMed, Cochrane Library, and Ovid ^(SP), covering the period from January 1960 to August 2022. A search in Google Scholar and the grey literature and a hand search of references was performed to find additional data. Suitable studies were selected based on the predefined inclusion and exclusion criteria. Quality analysis of the selected studies was performed using the Cochrane Risk of Bias Tool for Randomized Controlled Trials. Results: Seven hundred and forty-seven articles were retrieved from three databases (PubMed, Cochrane Library, Ovid ^(SP), and other sources). Results: A total of 747 studies were retrieved from electronic databases and hand searches. After removing duplications, 519 were available; among them, 495 irrelevant citations were excluded with inclusion and exclusion criteria. Twenty-four citations were eligible for abstract screening, and fourteen citations were excluded including invitational studies, narrative reviews, animal studies, and studies that involved adults. Finally, studies for full texts were screened for eligibility for the research question, and then only five studies were available upon full-text phase analysis. The five studies involved 1740 children to evaluate the efficacy of Triphala and chlorhexidine mouthwashes in reducing gingivitis and dental plaque in children. Among them, one article showed low risk, three articles showed moderate risk, and one article showed high risk. Conclusion: While Triphala and chlorhexidine both reduce gingivitis, reports indicate that Triphala is less effective than chlorhexidine in improving plaque index scores. Further controlled studies are needed to confirm the effectiveness of Triphala mouthwash in children. Full article

Background: Para-aortic lymphadenectomy can be used for both diagnostic and therapeutic purposes as it aids in staging, provides prognostic data, and influences the patient’s options for adjuvant therapy. However, there is still contention over its potential in treating cancer. A systematic review of the literature was performed to look into the published randomized controlled studies (RCTs) that have reported the effectiveness of lymphadenectomy. Methods: Five different electronic databases, including PubMed, Cochrane Library, Clinical trials.gov, ICTRP, and Embase, were used to conduct a comprehensive search. Original RCTs reporting on the impact of lymphadenectomy on the overall survival in various cancers were included. Information related to the study population, intervention, type of cancer, primary endpoints, and key findings of the study were extracted. Quality assessment of the selected studies was conducted using the Revised Cochrane Risk of Bias Tool Rob 2 for randomized trials. Results: A total of 1693 citations, with 1511 from PubMed, 80 from the Cochrane Library, 67 from Embase, 18 from ICTRP, and 17 from Clinicaltrials.gov were retrieved. Preliminary screening was performed, and after applying selection criteria, nine articles were included in the final qualitative analysis. The total number of patients was 4231, and the sample size ranged from 70 to 1408. Among these nine studies, four studies were on genital cancers (two ovarian cancers, one endometrial cancer, and one cervical cancer); four on digestive cancers (advanced gastric cancers); and one on urinary cancer (advanced bladder cancer). These studies reported that para-aortic lymphadenectomy did not improve overall survival and disease-free survival in advanced ovarian cancers, early endometrial cancers, advanced gastric, and bladder cancers. All of the studies had a low risk of bias. Conclusions: Para-aortic lymphadenectomy is not advised in advanced ovarian cancers, early endometrial cancers with low risks, advanced gastric cancers, and bladder cancers. SNB could be an alternative to lymphadenectomy for ovarian cancer in the future. Clinicians should inform patients regarding the benefits of para-aortic lymphadenectomy in terms of survival and the potential risks associated with it. Full article