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25 pages, 659 KiB  
Systematic Review
Mechanical and Physical Properties of Durable Prosthetic Restorations Printed Using 3D Technology in Comparison with Hybrid Ceramics and Milled Restorations—A Systematic Review
by Bettanapalya. V. Swapna, B. Shivamurthy, Vinu Thomas George, Kavishma Sulaya and Vaishnavi M Nayak
Prosthesis 2025, 7(4), 90; https://doi.org/10.3390/prosthesis7040090 (registering DOI) - 1 Aug 2025
Abstract
Background/Objectives: Additive manufacturing (AM) technology has emerged as an innovative approach in dentistry. Recently, manufacturers have developed permanent resins engineered explicitly for the fabrication of definitive prostheses using AM techniques. This systematic review evaluated the mechanical and physical properties of 3D-printed permanent resins [...] Read more.
Background/Objectives: Additive manufacturing (AM) technology has emerged as an innovative approach in dentistry. Recently, manufacturers have developed permanent resins engineered explicitly for the fabrication of definitive prostheses using AM techniques. This systematic review evaluated the mechanical and physical properties of 3D-printed permanent resins in comparison to milled resins and hybrid ceramics for the fabrication of indirect dental restorations. Methods: Three electronic databases—Scopus, Web of Science, and PubMed—were searched for English-language articles. Two independent researchers conducted study selection, data extraction, quality assessment, and the evaluation of the certainty of evidence. In vitro studies assessing the mechanical and physical properties of the permanent resins were included in this review. Results: A total of 1779 articles were identified through electronic databases. Following full-text screening and eligibility assessment, 13 studies published between 2023 and 2024 were included in this qualitative review. The investigated outcomes included physical properties (surface roughness, color changes, water sorption/solubility) and mechanical properties (flexural strength, elastic modulus, microhardness). Conclusions: Three-dimensionally printed permanent resins show promising potential for fabricating indirect dental restorations. However, the current evidence regarding their mechanical and physical properties remain limited and inconsistent, mainly due to variability in study methodologies. Full article
(This article belongs to the Section Prosthodontics)
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9 pages, 299 KiB  
Article
Assessing the Accuracy and Readability of Large Language Model Guidance for Patients on Breast Cancer Surgery Preparation and Recovery
by Elena Palmarin, Stefania Lando, Alberto Marchet, Tania Saibene, Silvia Michieletto, Matteo Cagol, Francesco Milardi, Dario Gregori and Giulia Lorenzoni
J. Clin. Med. 2025, 14(15), 5411; https://doi.org/10.3390/jcm14155411 (registering DOI) - 1 Aug 2025
Abstract
Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly [...] Read more.
Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. Full article
(This article belongs to the Section Oncology)
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11 pages, 598 KiB  
Systematic Review
Clinical Assessment of Flexible and Non-Metal Clasp Dentures: A Systematic Review
by Plinio Mendes Senna, Carlos Fernando Mourão, Carlos Roberto Teixeira Rodrigues, Laila Zarranz, Mônica Zacharias Jorge, Tea Romasco and Wayne José Batista Cordeiro
Prosthesis 2025, 7(4), 91; https://doi.org/10.3390/prosthesis7040091 (registering DOI) - 1 Aug 2025
Abstract
Background/Objectives: The present study aimed to evaluate the oral health and patient satisfaction of flexible and non-metal clasp dentures (NMCD) compared to removable partial dentures (RPD) using a systematic review. Methods: The PICOS framework of this review was as follows: Do rehabilitations involving [...] Read more.
Background/Objectives: The present study aimed to evaluate the oral health and patient satisfaction of flexible and non-metal clasp dentures (NMCD) compared to removable partial dentures (RPD) using a systematic review. Methods: The PICOS framework of this review was as follows: Do rehabilitations involving flexible dentures or NMCD have a similar success rate to those using RPD? Thus, the PICOS approach involves the following topics: (P) Population/Problem: partial edentulous adult patients; (I) Intervention: patients rehabilitated with flexible dentures or NMCD; (C) Comparison: patients rehabilitated with standard RPD; (O) Outcome: clinical parameters such as oral health, masticatory function, and patient satisfaction; and (S) Study Type: clinical trials and observational studies (cohort, case–control, and cross-sectional). No language restrictions were applied to the studies. The search strategy consisted of the following keywords in different databases: ((flexible) OR (nonmetal) OR (non-metal) OR (thermoplastic)) AND (denture). Only clinical trials and observational studies (cohort, case–control, and cross-sectional studies) from the last 15 years were included, and no language restrictions were applied. Studies that did not describe the denture material were excluded. Results: Of the 2197 potentially relevant records, 14 studies were included in the present review. Two studies reported retrospective results, while twelve reported a prospective evaluation. Considering the thermoplastic materials, five studies evaluated polyester, five polyamides, three polyacetals, and only one study evaluated polyetheretherketone (PEEK). Flexible dentures and NMCD demonstrated similar periodontal status and bone levels on abutment teeth to RPD after up to 12 months. Flexible dentures exhibited a higher degree of redness of the mucosa after 12 months. One study showed a lower maximum bite force for flexible dentures compared to RPD. No study has performed a clinical evaluation of mastication and chewing ability. Conclusions: Despite increased short-term patient satisfaction for flexible dentures and NMCD, there is weak evidence to support a similar clinical performance of flexible dentures and NMCD to RPD. Full article
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23 pages, 1192 KiB  
Article
Multi-Model Dialectical Evaluation of LLM Reasoning Chains: A Structured Framework with Dual Scoring Agents
by Catalin Anghel, Andreea Alexandra Anghel, Emilia Pecheanu, Ioan Susnea, Adina Cocu and Adrian Istrate
Informatics 2025, 12(3), 76; https://doi.org/10.3390/informatics12030076 (registering DOI) - 1 Aug 2025
Abstract
(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed [...] Read more.
(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed modular framework designed to evaluate reasoning through a structured three-stage process: opinion, counterargument, and synthesis. The framework enables transparent and comparative analysis of how different LLMs handle dialectical reasoning. (2) Methods: Each stage is executed by a single model, and final syntheses are scored via two independent LLM evaluators (LLaMA 3.1 and GPT-4o) based on a rubric with four dimensions: clarity, coherence, originality, and dialecticality. In parallel, a rule-based semantic analyzer detects rhetorical anomalies and ethical values. All outputs and metadata are stored in a Neo4j graph database for structured exploration. (3) Results: The system was applied to four open-weight models (Gemma 7B, Mistral 7B, Dolphin-Mistral, Zephyr 7B) across ten open-ended prompts on ethical, political, and technological topics. The results show consistent stylistic and semantic variation across models, with moderate inter-rater agreement. Semantic diagnostics revealed differences in value expression and rhetorical flaws not captured by rubric scores. (4) Originality: The framework is, to our knowledge, the first to integrate multi-stage reasoning, rubric-based and semantic evaluation, and graph-based storage into a single system. It enables replicable, interpretable, and multidimensional assessment of generative reasoning—supporting researchers, developers, and educators working with LLMs in high-stakes contexts. Full article
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12 pages, 1346 KiB  
Article
A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology
by Yi Luo, Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Xiaojian Chen, Rui Zhang, Quan Chen, Wil Ngwa and Kai Ding
Bioengineering 2025, 12(8), 835; https://doi.org/10.3390/bioengineering12080835 (registering DOI) - 31 Jul 2025
Abstract
Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), [...] Read more.
Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article
(This article belongs to the Special Issue Novel Imaging Techniques in Radiotherapy)
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21 pages, 1750 KiB  
Article
Predictive Analytics Leveraging a Machine Learning Approach to Identify Students’ Reasons for Dropping out of University
by Asmaa El Mahmoudi, Nour El Houda Chaoui and Habiba Chaoui
Appl. Sci. 2025, 15(15), 8496; https://doi.org/10.3390/app15158496 (registering DOI) - 31 Jul 2025
Abstract
In today’s fast-changing world, the higher education system must evolve to enhance the quality of learning and teaching. Fulfilling the role of a university is a major challenge. Universities must implement strategies that place the student at the center of their concerns; so, [...] Read more.
In today’s fast-changing world, the higher education system must evolve to enhance the quality of learning and teaching. Fulfilling the role of a university is a major challenge. Universities must implement strategies that place the student at the center of their concerns; so, these strategies must be designed for and by the student. However, the high university dropout rate is one of the current problems faced by many universities. This suggests that there are some issues that hinder the learning process. Several studies have highlighted the advantage of artificial intelligence (AI) technologies in providing explorative and predictive analyses that explain why students are dropping out, with the aim of improving the quality of teaching and providing an integrated learning environment. This paper proposes a framework that predicts student dropout rates using machine learning techniques, based on data collected from various sources. Data collection was carried out between 2022 and 2024. We used a quantitative analysis method employed through a questionnaire distributed to 120 students (aged 18–26) from open access faculties of a Moroccan public university to identify the factors leading to an increase in university dropout rates. We discuss the impact of selected variables, and the findings show that several factors are related to university dropout rates, such as social background, psychological and health problems, insufficient motivation of professors, limited perspective on educational programs, changes in language and teaching methodologies, absenteeism, student attitude, and a lack of interaction between professors and students. Full article
(This article belongs to the Special Issue ICT in Education, 2nd Edition)
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16 pages, 628 KiB  
Article
Beyond the Bot: A Dual-Phase Framework for Evaluating AI Chatbot Simulations in Nursing Education
by Phillip Olla, Nadine Wodwaski and Taylor Long
Nurs. Rep. 2025, 15(8), 280; https://doi.org/10.3390/nursrep15080280 (registering DOI) - 31 Jul 2025
Abstract
Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase [...] Read more.
Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article
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17 pages, 2283 KiB  
Article
Recognition of Japanese Finger-Spelled Characters Based on Finger Angle Features and Their Continuous Motion Analysis
by Tamon Kondo, Ryota Murai, Zixun He, Duk Shin and Yousun Kang
Electronics 2025, 14(15), 3052; https://doi.org/10.3390/electronics14153052 - 30 Jul 2025
Abstract
To improve the accuracy of Japanese finger-spelled character recognition using an RGB camera, we focused on feature design and refinement of the recognition method. By leveraging angular features extracted via MediaPipe, we proposed a method that effectively captures subtle motion differences while minimizing [...] Read more.
To improve the accuracy of Japanese finger-spelled character recognition using an RGB camera, we focused on feature design and refinement of the recognition method. By leveraging angular features extracted via MediaPipe, we proposed a method that effectively captures subtle motion differences while minimizing the influence of background and surrounding individuals. We constructed a large-scale dataset that includes not only the basic 50 Japanese syllables but also those with diacritical marks, such as voiced sounds (e.g., “ga”, “za”, “da”) and semi-voiced sounds (e.g., “pa”, “pi”, “pu”), to enhance the model’s ability to recognize a wide variety of characters. In addition, the application of a change-point detection algorithm enabled accurate segmentation of sign language motion boundaries, improving word-level recognition performance. These efforts laid the foundation for a highly practical recognition system. However, several challenges remain, including the limited size and diversity of the dataset and the need for further improvements in segmentation accuracy. Future work will focus on enhancing the model’s generalizability by collecting more diverse data from a broader range of participants and incorporating segmentation methods that consider contextual information. Ultimately, the outcomes of this research should contribute to the development of educational support tools and sign language interpretation systems aimed at real-world applications. Full article
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14 pages, 3600 KiB  
Article
Performance of Large Language Models in Recognizing Brain MRI Sequences: A Comparative Analysis of ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro
by Ali Salbas and Rasit Eren Buyuktoka
Diagnostics 2025, 15(15), 1919; https://doi.org/10.3390/diagnostics15151919 - 30 Jul 2025
Abstract
Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate [...] Read more.
Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate and compare the performance of three advanced multimodal LLMs (ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) in classifying brain MRI sequences. Methods: A total of 130 brain MRI images from adult patients without pathological findings were used, representing 13 standard MRI series. Models were tested using zero-shot prompts for identifying modality, anatomical region, imaging plane, contrast-enhancement status, and MRI sequence. Accuracy was calculated, and differences among models were analyzed using Cochran’s Q test and McNemar test with Bonferroni correction. Results: ChatGPT-4o and Gemini 2.5 Pro achieved 100% accuracy in identifying the imaging plane and 98.46% in identifying contrast-enhancement status. MRI sequence classification accuracy was 97.7% for ChatGPT-4o, 93.1% for Gemini 2.5 Pro, and 73.1% for Claude 4 Opus (p < 0.001). The most frequent misclassifications involved fluid-attenuated inversion recovery (FLAIR) sequences, often misclassified as T1-weighted or diffusion-weighted sequences. Claude 4 Opus showed lower accuracy in susceptibility-weighted imaging (SWI) and apparent diffusion coefficient (ADC) sequences. Gemini 2.5 Pro exhibited occasional hallucinations, including irrelevant clinical details such as “hypoglycemia” and “Susac syndrome.” Conclusions: Multimodal LLMs demonstrate high accuracy in basic MRI recognition tasks but vary significantly in specific sequence classification tasks. Hallucinations emphasize caution in clinical use, underlining the need for validation, transparency, and expert oversight. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
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18 pages, 919 KiB  
Article
Timing of Intervals Between Utterances in Typically Developing Infants and Infants Later Diagnosed with Autism Spectrum Disorder
by Zahra Poursoroush, Gordon Ramsay, Ching-Chi Yang, Eugene H. Buder, Edina R. Bene, Pumpki Lei Su, Hyunjoo Yoo, Helen L. Long, Cheryl Klaiman, Moira L. Pileggi, Natalie Brane and D. Kimbrough Oller
Brain Sci. 2025, 15(8), 819; https://doi.org/10.3390/brainsci15080819 (registering DOI) - 30 Jul 2025
Abstract
Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: [...] Read more.
Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article
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15 pages, 288 KiB  
Systematic Review
Interventions to Improve Vaccination Uptake Among Adults: A Systematic Review and Meta-Analysis
by Anelisa Jaca, Lindi Mathebula, Thobile Malinga, Kimona Rampersadh, Masibulele Zulu, Ameer Steven-Jorg Hohlfeld, Charles Shey Wiysonge, Julie C. Jacobson Vann and Duduzile Ndwandwe
Vaccines 2025, 13(8), 811; https://doi.org/10.3390/vaccines13080811 (registering DOI) - 30 Jul 2025
Abstract
Background: Immunization is a highly effective intervention for controlling over 20 life-threatening infectious diseases, significantly reducing both morbidity and mortality rates. One notable achievement in vaccination efforts was the global eradication of smallpox, which the World Health Assembly declared on 8 May 1980. [...] Read more.
Background: Immunization is a highly effective intervention for controlling over 20 life-threatening infectious diseases, significantly reducing both morbidity and mortality rates. One notable achievement in vaccination efforts was the global eradication of smallpox, which the World Health Assembly declared on 8 May 1980. Additionally, there has been a remarkable 99.9% reduction in wild poliovirus cases since 1988, decreasing from more than 350,000 cases that year to just 30 cases in 2022. Objectives: The objective of this review was to assess the effects of various interventions designed to increase vaccination uptake among adults. Search Methods: A thorough search was conducted in the CENTRAL, Embase Ovid, Medline Ovid, PubMed, Web of Science, and Global Index Medicus databases for primary studies. This search was conducted in August 2021 and updated in November 2024. Selection Criteria: Randomized trials were eligible for inclusion in this review, regardless of publication status or language. Data Analysis: Two authors independently screened the search outputs to select potentially eligible studies. Risk ratios (RR) with 95% confidence intervals (CI) were calculated for each randomized controlled trial (RCT). A meta-analysis was conducted using a random-effects model, and the quality of the evidence was assessed using the GRADE approach. Main Results: A total of 35 randomized controlled trials met the inclusion criteria and were included in this review, with the majority conducted in the United States. The interventions targeted adults aged 18 and older who were eligible for vaccination, involving a total of 403,709 participants. The overall pooled results for interventions aimed at increasing influenza vaccination showed a risk ratio of 1.41 (95% CI: 1.15, 1.73). Most studies focused on influenza vaccination (18 studies), while the remaining studies examined various other vaccines, including those for hepatitis A, COVID-19, hepatitis B, pneumococcal disease, tetanus, diphtheria, pertussis (Tdap), herpes zoster, and human papillomavirus (HPV). The results indicate that letter reminders were slightly effective in increasing influenza vaccination uptake compared to the control group (RR: 1.75, 95% CI: 0.97, 1.16; 6 studies; 161,495 participants; low-certainty evidence). Additionally, participants who received education interventions showed increased levels of influenza vaccination uptake compared to those in the control group (RR: 1.88, 95% CI: 0.61, 5.76; 3 studies; 1318 participants; low-certainty evidence). Furthermore, tracking and outreach interventions also led to an increase in influenza vaccination uptake (RR: 1.87, 95% CI: 0.78, 4.46; 2 studies; 33,752 participants; low-certainty evidence). Conclusions: Letter reminders and educational interventions targeted at recipients are effective in increasing vaccination uptake compared to control groups. Full article
22 pages, 1220 KiB  
Systematic Review
The Evolving Role of Stem Cells in Oral Health and Regeneration: A Systematic Review
by Gianna Dipalma, Grazia Marinelli, Arianna Fiore, Liviana Balestriere, Claudio Carone, Silvio Buongiorno, Francesco Inchingolo, Giuseppe Minervini, Andrea Palermo, Angelo Michele Inchingolo and Alessio Danilo Inchingolo
Surgeries 2025, 6(3), 65; https://doi.org/10.3390/surgeries6030065 (registering DOI) - 30 Jul 2025
Abstract
Background: Mesenchymal stem cells (MSCs), multipotent and immune-regulatory cells derived from tissues such as bone marrow, dental pulp, and periodontal ligament, emerged as promising agents in regenerative dentistry. Their clinical applications include endodontic tissue regeneration, periodontal healing, and alveolar bone repair, addressing [...] Read more.
Background: Mesenchymal stem cells (MSCs), multipotent and immune-regulatory cells derived from tissues such as bone marrow, dental pulp, and periodontal ligament, emerged as promising agents in regenerative dentistry. Their clinical applications include endodontic tissue regeneration, periodontal healing, and alveolar bone repair, addressing critical challenges in dental tissue restoration. Methods: A systematic review was conducted following PRISMA guidelines and registered in PROSPERO. We searched PubMed, Scopus, and Web of Science databases for open-access, English-language clinical trials and observational studies published from 2015 to 2025. Studies focusing on the application of MSCs in dental tissue regeneration were included based on predefined eligibility criteria. Results: Out of 2400 initial records, 13 studies met the inclusion criteria after screening and eligibility assessment. Most studies investigated MSCs derived from dental pulp and periodontal ligament for regenerating periodontal tissues and alveolar bone defects. The majority reported improved clinical outcomes; however, variations in MSC sources, delivery methods, sample sizes, and follow-up periods introduced methodological heterogeneity. Conclusions: MSCs show significant potential in enhancing bone and periodontal regeneration in dental practice. Nonetheless, the current evidence is limited by small sample sizes, short follow-up, and inconsistent methodologies. Future large-scale, standardized clinical trials are required to validate MSC-based regenerative therapies and optimize treatment protocols. Full article
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15 pages, 747 KiB  
Article
Comparative Analysis of LLMs in Dry Eye Syndrome Healthcare Information
by Gloria Wu, Hrishi Paliath-Pathiyal, Obaid Khan and Margaret C. Wang
Diagnostics 2025, 15(15), 1913; https://doi.org/10.3390/diagnostics15151913 - 30 Jul 2025
Abstract
Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims [...] Read more.
Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article
(This article belongs to the Special Issue Artificial Intelligence Application in Cornea and External Diseases)
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13 pages, 236 KiB  
Opinion
How Do We Keep Our New Graduate Nurses in Australia?
by Linda Ng, Rob Eley, Jennifer Dawson, Priya Govindaswamy and Karen Walker
Nurs. Rep. 2025, 15(8), 276; https://doi.org/10.3390/nursrep15080276 - 30 Jul 2025
Abstract
This paper aims to discuss the transition of new graduate nurses into the workforce, the preparation provided to equip them through the novice–beginner stage, and the theory–practice conundrum. Background: In Australia, new graduate transition programs have been in existence since the 1990s. [...] Read more.
This paper aims to discuss the transition of new graduate nurses into the workforce, the preparation provided to equip them through the novice–beginner stage, and the theory–practice conundrum. Background: In Australia, new graduate transition programs have been in existence since the 1990s. While there is widespread acknowledgment that this period is pivotal for new graduate nurses entering the profession, there is a lack of consensus on the definition of best practice to achieve optimal preparation for new graduate nurses transitioning into the workforce. Methods: This discussion paper integrates the nursing literature on this topic with the extensive professional experiences of the authors, who are currently working as clinicians in metropolitan hospitals and hold academic positions at universities. Their insights are informed by the literature sourced from peer-reviewed English language journals, including reviews, empirical studies, and national and international reports. Discussion: Recruiting and retaining nurses presents a multifaceted challenge that requires the development of effective tools and strategies to build a sustainable workforce. Both the literature and the authors’ experiences highlight several key factors influencing the preparedness of new graduates. These factors include workplace culture, the demands placed on new graduates, and the support, education, and training they receive. The perspectives shared in this article offer valuable discussion points that can deepen our understanding of the current issues and contribute to the development of more effective solutions. Full article
15 pages, 1152 KiB  
Article
Nurse-Led, Remote Optimisation of Guideline-Directed Medical Therapy in Patients with Heart Failure and Reduced Ejection Fraction Across Australia
by Gabrielle Freedman, Racheal Watt, Enayet Karim Chowdhury, Kate Quinlan, David Eccleston, Andrea Driscoll, James Theuerle and Leighton Kearney
J. Clin. Med. 2025, 14(15), 5371; https://doi.org/10.3390/jcm14155371 - 30 Jul 2025
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Abstract
Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal [...] Read more.
Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal 4P-HF therapy were identified from a large, multicentre Cardiology network database using a natural language processing tool, supported by manual file review. A nurse-led, remotely delivered, medication uptitration program aimed to optimise therapy in this real-world cohort. Results: The final cohort included 2004 patients with a mean age of 72.7 ± 11.6 years. Utilisation of 4P-HF increased from 11.1% at baseline to 49.8% post intervention, and each individual medication class increased significantly post intervention (all p < 0.001). The largest increase was observed with the use of sodium–glucose cotransporter 2 inhibitors, which rose from 17.3% to 73.9%, followed by mineralocorticoid receptor antagonists (51.6% to 65.7%), beta-blockers (88.4% to 97.0%), and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor blocker–neprilysin inhibitors (89.8% to 96.4%). In patients on submaximal therapy, barriers were documented in all cases. Following medication optimisation, left ventricular ejection function (LVEF) improved significantly (38.5% ± 10.8% vs. 42.5% ± 11.7, p < 0.001). Conclusions: This nurse-led, remotely delivered, medication optimisation program significantly improved the adoption of 4P-HF therapy and LVEF in patients with HFrEF. The program demonstrates a practical, scalable solution for the optimisation of HFrEF therapy across a large healthcare network. Full article
(This article belongs to the Section Cardiology)
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