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Background: Whether nanoengineered titanium surfaces confer superior implant stability beyond modern microrough controls remains uncertain. Methods: This systematic review followed PRISMA 2020 guidance: comprehensive multi-database searching with de-duplication; dual independent screening, full-text assessment, and standardized data extraction for predefined outcomes (implant stability quotient [ISQ], mechanical anchorage by removal/push-out/pull-out torque, and histologic bone-to-implant contact). Risk of bias was appraised with RoB 2 for randomized trials, ROBINS-I for non-randomized clinical studies, and CAMARADES (animal experimentation). The certainty of clinical evidence was summarized using GRADE. Results: Across animal models, nanoengineered surfaces consistently improved early osseointegration indices (higher removal torque and bone-to-implant contact at initial healing). In clinical comparative studies, nanoengineered implants showed modest, time-limited gains in early stability (ISQ) versus microrough titanium. By 3–6 months, between-group differences typically diminished, and no consistent advantages were demonstrated for survival or marginal bone outcomes at later follow-up. Methodologic heterogeneity (surface chemistries, timepoints, outcome definitions) and small clinical samples limited quantitative synthesis. Overall, risk-of-bias concerns ranged from some concerns to high in non-randomized studies; the certainty of clinical evidence was low. Conclusions: Nanofeatured titanium surfaces improve early osseointegration but do not demonstrate a consistent long-term advantage over modern microrough implants. Current evidence supports an early osseointegration benefit without clear long-term clinical advantage over contemporary microrough implants. Adequately powered, head-to-head trials with standardized stability endpoints and ≥12-month follow-up are needed to determine whether early gains translate into patient-important outcomes.

Objective: This study aims to evaluate cell proliferation capacity and micronuclei incidence in the presence of nickel–chromium (Ni-Cr)-based dental alloys, with and without the addition of beryllium (Be). The use of these alloys in dental prosthetics is widespread; however, the potential risks associated with their genotoxicity and cytotoxicity require further investigation. The study seeks to provide insight into the safety of these materials and their long-term impact on the health of both patients and dental professionals. Methods: The study was conducted through a comparative analysis of genotoxicity and cytotoxicity using human lymphocyte cultures exposed to two types of Ni-Cr-based dental alloys, one containing beryllium and the other without beryllium. The evaluations were performed according to the OECD Test No. 487 guideline, employing the micronucleus assay and cell proliferation assay. Lymphocytes were exposed to three different alloy concentrations (5 mg/mL, 10 mg/mL, and 20 mg/mL), and the effects on genetic material were analyzed microscopically. Descriptive statistics (mean, standard deviation, and variance) were calculated, and one-way ANOVA was used to assess statistical significance between groups, with a significance threshold of p < 0.05. Results: A significant increase in cytotoxicity and micronuclei incidence was observed in the samples containing beryllium compared to those without beryllium. Statistical analysis revealed significant differences (p < 0.001) between the test and control groups and between different concentrations. Additionally, a direct proportional relationship was noted between alloy concentration and the intensity of genotoxic effects. Microscopic analysis confirmed genetic material damage, indicating a potentially increased risk associated with the use of this type of dental material. Conclusions: The data obtained suggest that Ni-Cr-based dental alloys containing beryllium may present a significant risk of genotoxicity and cytotoxicity. Therefore, the selection of materials used in dental prosthetics should be based on solid scientific evidence, and the use of these alloys should be approached with caution. The study highlights the need for further research to better understand the long-term impact of these materials on human health.

The CAD-CAM technology was initiated in the early 1980s, and in the last decade it has developed significantly. Currently, the functionality and aesthetics offered by CAD-CAM systems meet the expectations of patients and thus, in this material, several extremely interesting aspects are presented, regarding the technological process of designing and manufacturing structures made by CAD-CAM technology both from zirconium oxide, but also from other specific modern materials.

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit.

Objective: This systematic review and meta-analysis aimed to evaluate the effects of moisture control strategies (including wet-bonding techniques, universal adhesives, and etching type) on dentin bonding performance in restorative dentistry. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Google Scholar, following PRISMA guidelines. Only in vitro and ex vivo studies comparing wet- and dry-bonding protocols, using human dentin substrates, and reporting microtensile bond strength (μTBS) were included. The data were synthesized using a random-effects meta-analysis and the methodological quality was assessed using the MINORS tool. Certainty of evidence was evaluated using the GRADE framework. Results: Nine studies met the inclusion criteria, eight of which were included in this meta-analysis. The moisture control strategies significantly influenced the bonding outcomes, with ethanol and acetone wet bonding yielding higher μTBS and enhanced hybrid layer morphology. The universal adhesives performed effectively under both moist and dry conditions, although their performance varied by the adhesive composition and solvent system. The meta-analysis revealed a statistically significant advantage for hydrated dentin (SMD = +1.20; 95% CI: 0.52 to 1.86; p < 0.001), with the moist and ethanol-treated substrates outperforming the dry and over-wet surfaces. The long-term durability was better preserved with ethanol and acetone pretreatments and the adjunctive use of chlorhexidine. Conclusions: Moisture conditions influence dentin bond strength, but modern universal adhesives show consistent bonding performance across different moisture conditions. Solvent-wet-bonding protocols, particularly with ethanol or acetone, enhance the immediate and long-term performance. While the current evidence is limited by the in vitro designs and heterogeneity, the findings demonstrate protocol flexibility and highlight strategies to optimize adhesion in clinical practice. Future clinical trials are necessary to validate these approaches under real-world conditions.

Pollution remains the leading environmental cause of premature death and the problem that affects us the most. This study employed a bibliometric analysis using the Web of Science Core Collection to identify emerging trends in pollution prevention and their social implications. A Boolean query retrieved 23 open access, English-language articles published between 2021 and 2025, which were further analyzed with Vos Viewer through co-authorship, country collaboration, and keyword co-occurrence maps. Results show that England, the USA, and China lead research production, forming strong international networks, while smaller nations such as Estonia and Portugal contribute to the diversity of perspectives. Co-authorship analysis revealed dense interdisciplinary collaboration, while keyword mapping identified four thematic clusters: hazardous material management, digital and AI-driven solutions, sustainability and social responsibility, and traditional environmental issues such as water quality. These findings indicate a growing integration of technological innovation and social justice concerns in pollution prevention research, underscoring the need for equitable, community-driven strategies to address the persistent disproportionate impact of pollution on marginalized groups.

: Following myocardial infarction (MI), cardiac fibroblasts (CFs) adopt distinct phenotypes to ensure scar formation and healing. Although leukocytes are a critical driver of post-MI healing, the role of neutrophils in modulating CF phenotype remains insufficiently explored. We therefore investigated the impact of soluble mediators released by neutrophil subtypes found post-MI-pro-inflammatory (N1) and anti-inflammatory (N2)-on shaping CFs phenotype. : In vitro, human 3D grown CFs were indirectly co-cultured with N1 or N2 neutrophil-like cells using a two-chamber Transwell system. After 24 h, expression of inflammatory, remodeling, and pro-fibrotic markers was evaluated in fibroblasts and conditioned media. In vivo, soluble mediators derived from polarized mouse neutrophils (SN1 or SN2) were injected into the infarcted myocardium of C57BL/6J after MI surgery. The effects on the healing process were investigated at 1 and 7 days post-MI. : In vitro, CFs were found to exhibit a pro-inflammatory and matrix-degrading phenotype following indirect co-culture with N1 cells, characterized by overexpression of IL-1β, IL-6, MCP-1, and metalloproteases MMP-3/MMP-9. In vivo, both SN1 and SN2 treatments significantly reduced pro-inflammatory markers IL-1β and IL-6 gene expression at day 1 post-MI (inflammatory phase). At day 7 post-MI (resolution phase), SN1/SN2 treatments continued to limit local inflammation, while mitigating fibrotic remodeling by reducing CCN2, α-SMA, and key extracellular matrix proteins. : Together, these findings suggest that while N1-derived mediators promote a pro-inflammatory fibroblast phenotype in vitro, factors secreted by both N1 and N2 support a more balanced reparative response in vivo, by limiting local inflammation and potentially mitigating adverse remodeling post-MI.

IntroductionCongenital anomalies are the most encountered cause of fetal death, infant mortality and morbidity. 7.9 million infants are born with congenital anomalies yearly. Early detection of congenital anomalies facilitates life-saving treatments and stops the progression of disabilities. Congenital anomalies can be diagnosed prenatally through morphology scans. A correct interpretation of the morphology scan allows a detailed discussion with the parents regarding the prognosis. The central feature of this project is the development of a specialised intelligent system that uses two-dimensional ultrasound movies obtained during the standard second trimester morphology scan to identify congenital anomalies in fetuses.Methods and analysisThe project focuses on three pillars: committee of deep learning and statistical learning algorithms, statistical analysis, and operational research through learning curves. The cross-sectional study is divided into a training phase where the system learns to detect congenital anomalies using fetal morphology ultrasound scan, and then it is tested on previously unseen scans. In the training phase, the intelligent system will learn to answer the following specific objectives: (a) the system will learn to guide the sonographer’s probe for better acquisition; (b) the fetal planes will be automatically detected, measured and stored and (c) unusual findings will be signalled. During the testing phase, the system will automatically perform the above tasks on previously unseen videos.Pregnant patients in their second trimester admitted for their routine scan will be consecutively included in a 32-month study (4 May 2022–31 December 2024). The number of patients is 4000, enrolled by 10 doctors/sonographers. We will develop an intelligent system that uses multiple artificial intelligence algorithms that interact between themselves, in bulk or individual. For each anatomical part, there will be an algorithm in charge of detecting it, followed by another algorithm that will detect whether anomalies are present or not. The sonographers will validate the findings at each intermediate step.Ethics and disseminationAll protocols and the informed consent form comply with the Health Ministry and professional society ethics guidelines. The University of Craiova Ethics Committee has approved this study protocol as well as the Romanian Ministry of Research Innovation and Digitization that funded this research. The study will be implemented and reported in line with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement.Trial registration numberThe study is registered under the name ‘Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning’, project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057. Trial registration: ClinicalTrials.gov, unique identifying number NCT05738954, date of registration: 2 November 2023.

Background: Recent studies have shown that gut microbiota have important roles in different human diseases. There has been an ever-increasing application of high-throughput technologies for the characterization of microbial ecosystems. This led to an explosion of various molecular profiling data, and the analysis of such data has shown that machine-learning algorithms have been useful in identifying key molecular signatures. Results: In this review, we first analyze how dysbiosis of the intestinal microbiota relates to human disease and how possible modulation of the gut microbial ecosystem may be used for disease intervention. Further, we introduce categories and the workflows of different machine-learning approaches and how they perform integrative analysis of multi-omics data. Last, we review advances of machine learning in gut microbiome applications and discuss challenges it faces. Conclusions: We conclude that machine learning is indeed well suited for analyzing gut microbiome and that these approaches are beneficial for developing gut microbe-targeted therapies, helping in achieving personalized and precision medicine.