Search Results (487)

Dental implant components are typically fabricated using subtractive manufacturing, often involving metal materials that can be costly, inefficient, and time-consuming. This study explores the use of additive manufacturing (AM) with zirconia for dental implant overdenture bars, focusing on mechanical performance, stress distribution, and fit. Solid and lattice-structured bars were designed in Fusion 360 and produced using LithaCon 210 3Y-TZP zirconia (Lithoz GmbH, Vienna, Austria) on a CeraFab 8500 printer. Post-processing included cleaning, debinding, and sintering. A 3D-printed denture was also fabricated to evaluate fit. Thermography and optical imaging were used to assess adaptation. Custom fixtures were developed for flexural testing, and fracture loads were recorded to calculate stress distribution using finite element analysis (ANSYS R2025). The FEA model assumed isotropic, homogeneous, linear-elastic material behavior. Bars were torqued to 15 Ncm on implant analogs. The average fracture loads were 1.2240 kN (solid, n = 12) and 1.1132 kN (lattice, n = 5), with corresponding stress values of 147 MPa and 143 MPa, respectively. No statistically significant difference was observed (p = 0.578; α = 0.05). The fracture occurred near high-stress regions at fixture support points. All bars demonstrated a clinically acceptable fit on the model; however, further validation and clinical evaluation are still needed. Additively manufactured zirconia bars, including lattice structures, show promise as alternatives to conventional superstructures, potentially offering reduced material use and faster production without compromising mechanical performance. Full article

Background/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial polyps. Methods: A multicenter dataset (n = 3) comprising 65 hysteroscopies was used, yielding 33,239 frames and 37,512 annotated objects. Still frames were extracted from full-length videos and annotated for the presence of histologically confirmed polyps. A YOLOv1-based object detection model was used with a 70–20–10 split for training, validation, and testing. Primary performance metrics included recall, precision, and mean average precision at an intersection over union (IoU) ≥ 0.50 (mAP50). Frame-level classification metrics were also computed to evaluate clinical applicability. Results: The model achieved a recall of 0.96 and precision of 0.95 for polyp detection, with a mAP50 of 0.98. At the frame level, mean recall was 0.75, precision 0.98, and F1 score 0.82, confirming high detection and classification performance. Conclusions: This study presents a CNN trained on multicenter, real-world data that detects and classifies polyps simultaneously with high diagnostic and localization performance, supported by explainable AI features that enhance its clinical integration and technological readiness. Although currently limited to binary classification, this study demonstrates the feasibility and potential of AI to reduce diagnostic subjectivity and inter-observer variability in hysteroscopy. Future work will focus on expanding the model’s capabilities to classify a broader range of endometrial pathologies, enhance generalizability, and validate performance in real-time clinical settings. Full article

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

Background: Androgen deprivation therapy (ADT) is a primary treatment for prostate cancer (PCa) that effectively reduces androgen levels to suppress tumor progression. However, growing evidence suggests potential cognitive side effects, raising concerns about the long-term neurological consequences of this treatment. Objective: This scoping review aims to synthesize the existing evidence linking ADT to cognitive changes in men with PCa, identifying the key cognitive domains affected and outlining gaps in the existing literature. Methods: A systematic literature search was conducted according to the PRISMA-ScR guidelines in CINAHL, PubMed, Scopus, and Web of Science. Studies investigating cognitive function in ADT-treated PCa patients were included, covering randomized controlled trials (RCTs) and cohort, case–control, and cross-sectional studies. The extracted data included the study design, evaluated cognitive characteristics, measurement tools, and overall findings. Results: A total of 22 studies met the inclusion and exclusion criteria. Cognitive assessments varied across studies. While some studies reported cognitive impairments in ADT-treated patients—particularly in working, verbal, and visual memory and executive function—others found no significant effects. The variability in prostate cancer staging, epidemiological study designs, and treatment regimens; the exclusion of comorbid conditions; and the differences in assessment tools, sample sizes, and study durations hinder definitive conclusions about the cognitive effects of ADT. Conclusions: This scoping review highlights the heterogeneous and often contradictory evidence regarding ADT-associated cognitive dysfunction. While certain cognitive domains may be affected, methodological inconsistencies limit robust conclusions. Standardized cognitive assessments and longer longitudinal studies are required to clarify ADT’s role in cognitive decline. As the PCa survival rate increases with extended ADT use, integrating routine cognitive monitoring into clinical practice should be considered for PCa patients. Full article

Euphorbia hirta L. is traditionally used to treat tumors and has demonstrated anticancer effects. This study evaluated the phytochemical composition, toxicity, and antitumor activity of saline extract (SE) from E. hirta leaves in mice. Phytochemical analysis included thin layer chromatography, high-performance liquid chromatography, and quantification of phenols, flavonoids, and proteins. Acute toxicity (2000 mg/kg) assessed mortality, hematological, biochemical, histological parameters, water/feed intake, and body weight. Genotoxicity was evaluated via comet and micronucleus assays. Antitumor activity was tested in vitro and in vivo on sarcoma 180. SE contained 107.3 mg GAE/g phenolics and 22.9 mg QE/g flavonoids; the presence of gallic and ellagic acids was detected. Protein concentration was 12.16 mg/mL with lectin activity present. No mortality, organ damage, or genotoxic effects occurred in toxicity tests. SE demonstrated in vitro cytotoxicity against sarcoma cells (IC₅₀: 10 µg/mL). In vivo, SE (50–200 mg/kg) reduced tumor weight by 70.2–72.3%. SE modulated IL-2, IL-4, IL-6, IL-17, IFN-γ, and TNF-α in tumor environment. Tumors showed inflammatory infiltrate, necrosis, and fibrosis after treatment. These findings position the extract as a promising candidate for further development as a safe, plant-based antitumor agent. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

Milk powder is a key nutritional alternative to breastfeeding, but its thermal properties, which vary with temperature, can affect its quality and shelf life. However, there is little information about the physical and chemical properties of powdered milk in several countries. This dataset contains the result of an analysis of the aflatoxins, macroelement and microelement concentrations, oxidative stability, and fatty acid profile of infant formula milk powder. The concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Pb, Se, V, and Zn in digested powdered milk samples were quantified through inductively coupled plasma optical emission spectrometry (ICP OES). Thermogravimetry (TG) and differential scanning calorimetry (DSC) were used to estimate the oxidative stability of infant formula milk powder, while the methyl esters of the fatty acids were analyzed by gas chromatography. Most milk samples showed significant concentrations of As (0.5583–1.3101 mg/kg) and Pb (0.2588–0.0847 mg/kg). The concentrations of aflatoxins G2 and B2 are below the limits established by Brazilian regulatory agencies. The thermal degradation behavior of the samples is not the same due to their fatty acid compositions. The data presented may be useful in identifying compounds present in infant milk powder used as a substitute for breast milk and understanding the mechanism of thermal stability and degradation, ensuring food safety for those who consume them. Full article

This study aimed to evaluate the validity of panoramic radiography as an auxiliary method for identifying mandibular bone features consistent with a diagnosis of osteopenia or osteoporosis. Ninety panoramic radiographs were analyzed to assess the quality of the mandibular cortical layer below the mental foramen on both sides of the mandible. Scores C1 (normal), C2 (osteopenia), and C3 (osteoporosis) were attributed according to the cortical morphology. The sample consisted of 78 (86%) women aged 45 years or older and 12 (14%) men older than 60 years old. In 39 (43%) cases, the C1 score was evidenced as the lower mandibular cortical layer was normal on the image. The C2 score was identified in 47 (52%) cases, in which the cortical layer showed semilunar defects. Four (5%) cases presented a C3 score, with the cortical layer showing a clearly porous, thinner bone cortex. The presence of risk behaviors (e.g., smoking and alcoholism) as well as some comorbidities (e.g., systemic arterial hypertension, diabetes mellitus and thyroid disorders) was also observed. Mandibular bone changes were observed in association with a set of risk factors using panoramic radiography. Full article

Tooth wear is a growing oral health concern with implications for function, esthetics, and psychological well-being, ultimately affecting oral health-related quality of life (OHRQoL). While clinical indices assess tooth wear severity, they fail to capture patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs). This narrative review aims to identify and synthesize the use of PROs and PROMs used in adults with tooth wear and to map their assessed domains against the Wilson and Cleary model of health outcomes, highlighting gaps and guiding the development of condition-specific instruments. A comprehensive search of the literature was conducted across PubMed, MEDLINE, and Embase. Studies involving PROMs in adults with tooth wear were included. Extracted data encompassed psychometric properties and domains assessed. PROMs such as the OHIP, OES, OIDP, and QMFQ have been frequently used, focusing on functional limitation, esthetic perception, and psychological distress. However, no single instrument comprehensively addresses all relevant domains of the Wilson and Cleary model. Moreover, variation in tools and constructs limits comparability across studies and clinical settings. Existing PROMs capture only partial aspects of the patient experience related to tooth wear. When mapping these instruments to a validated theoretical model, significant gaps become evident, especially in terms of general health perceptions and overall quality of life metrics. To improve the evaluation and management of tooth wear in clinical settings, it is essential to create a condition-specific PROM based on a solid conceptual framework. Full article

This study presents the synthesis, physicochemical characterization, and biological evaluation of a novel ternary nickel(II) complex with isoleucine and 1,10-phenanthroline ligands, [Ni(Phen)(Ile)₂]∙6H₂O, designed as a potential antitumor agent. Single-crystal X-ray diffraction revealed a monoclinic structure (C2-space group) with an octahedral Ni(II) coordination involving Phen and Ile ligands. A Hirshfeld surface analysis highlighted intermolecular interactions stabilizing the crystal lattice, with hydrogen bonds (H···H and O···H/H···O) dominating (99.1% of contacts). Density functional theory (DFT) calculations, including solvation effects (in water and methanol), demonstrated strong agreement with the experimental geometric parameters and revealed higher affinity to the water solvent. The electronic properties of the complex, such as HOMO−LUMO gaps (3.20–4.26 eV) and electrophilicity (4.54–5.88 eV), indicated a charge-transfer potential suitable for biological applications through interactions with biomolecules. Raman and infrared spectroscopic studies showed vibrational modes associated with Ni–N/O bonds and ligand-specific deformations, with solvation-induced shifts observed. A study using ultraviolet–visible–near-infrared absorption spectroscopy demonstrated that the complex remains stable in solution. In vitro cytotoxicity assays against MCF-7 (breast adenocarcinoma) and HCT-116 (colorectal carcinoma) cells showed dose-dependent activity, achieving 47.6% and 65.3% viability reduction at 100 μM (48 h), respectively, with lower toxicity to non-tumor lung fibroblasts (GM07492A, 39.8%). Supporting the experimental data, we performed computational modeling to examine the pharmacokinetic profile, with particular focus on the absorption, distribution, metabolism, and excretion properties and drug-likeness potential. Full article

Background: Stereolithography is a rapid prototyping and 3D printing technique that creates solid three-dimensional models. An accurate and functional 3D model using stereolithography is invaluable in scientific research, particularly in studies involving edentulous patients. Additive manufacture and CAD systems help achieve accurate measurements and procedures and be easily replicated by lowering human error mistakes. The main objective of this study was to develop an in vitro simulation model with a reduced alveolar ridge with the same characteristics as mandibular edentulous patients using stereolithography. Methods: A mandibular model with a resorbed mandibular crest was scanned, and the STL model was aligned to the XYZ reference system. A reduction in the alveolar ridge corresponding to the mandibular mucosa of an edentulous patient was achieved. A negative model also derived from the original model was made to ensure the space for oral simulation material. A dimensional stability test was performed to validate the model. Results: The maximal mean displacement of the model was 0.015 mm, and the minimal mean displacement was 0.004 mm. The oral mucosa had a displacement of approximately 1.6 mm. Conclusions: An in vitro 3D simulation model of a complete edentulous patient mucosa was achieved. Full article

Background/Objectives: The objective of this study is to evaluate the diagnostic performance of a combined screening approach using self-reported periodontal information and radiographic periodontal bone loss (R-PBL) in identifying individuals with periodontitis. Methods: An exploratory cross-sectional study was conducted including adult participants with available panoramic radiographs and responses to a validated self-reported periodontal screening questionnaire. R-PBL was assessed on interproximal sites and classified according to established thresholds. Self-reported information followed a validated strategy based on the Center for Diseases Control tool. The performance of individual and combined indicators was analyzed against the 2018 case definition for periodontitis, calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: A total of 150 participants were included, equally divided between periodontitis cases and controls, with a mean age of 46.5 years. The R-PBL model demonstrated the best predictive performance for both periodontitis (AUC: 0.833) and severe periodontitis (AUC: 0.796), with the highest precision and net benefit across thresholds. The Either model showed similar performance, particularly in sensitivity, while SR and Both models underperformed. Decision curve analysis confirmed the superior clinical utility of ‘R-PBL’ and ‘Either’ models in guiding decision-making. Conclusions: Combining self-reported information with radiographic bone loss showed adequate screening performance for periodontitis. This dual approach may provide a feasible strategy for identifying high-risk individuals in settings where full clinical examination is not possible. Full article

This study investigated the impact of increased extraplatelet content on the tissue regenerative capacity of platelet-rich plasma (PRP)-derived fibrin scaffolds. Comparative analyses were performed between a “balanced protein-concentrate plasma” (BPCP) and a standard PRP (sPRP), focusing on platelet and fibrinogen content, scaffold microstructure, and functional performance. Growth factor (GF) release kinetics from the scaffolds were quantified via ELISA over 10 days, while scaffold biomechanics were evaluated through rheological testing, indentation, energy dissipation, adhesion, and assessments of coagulation dynamics, biodegradation, swelling, and retraction. Microstructural analysis was conducted using scanning electron microscopy (SEM), with fiber diameter and porosity measurements. The results demonstrated that BPCP scaffolds released significantly higher amounts of GFs and total protein, especially beyond 24 h (* p < 0.05). Despite a delayed coagulation process (** p < 0.01), BPCP scaffolds exhibited superior structural integrity and cushioning behavior (* p < 0.05). SEM revealed thicker fibers in BPCP scaffolds (**** p < 0.0001), while adhesion and biodegradation remained unaffected. Notably, BPCP scaffolds showed reduced retraction after 24 h and maintained their shape stability over two weeks without significant swelling. These findings indicate that enhancing the extraplatelet content in PRP formulations can optimize fibrin scaffold performance. Further preclinical and clinical studies are warranted to evaluate the therapeutic efficacy of BPCP-derived scaffolds in regenerative medicine. Full article

Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. By harnessing the power of data-driven predictive analytics, the research offers a promising pathway towards enhancing operational efficiency and minimizing unplanned downtime in industrial settings. This study not only contributes to the field of predictive maintenance but also underscores the transformative potential of advanced monitoring technologies in optimizing asset reliability and performance. Full article

In semi-arid regions, seasonally dry tropical forests are essential for regulating the surface energy balance, which can be analyzed by examining air heating processes and water availability control. The objective of this study was to evaluate the ability of the Brazilian Developments on the Regional Atmospheric Modelling System (BRAMS) model in simulating the seasonal variations of the energy balance components of the Caatinga biome. The surface measurements of meteorological variables, including air temperature and relative humidity, were also examined. To validate the model, we used data collected in situ using an eddy covariance system. In this work, we used the BRAMS model version 5.3 associated with the Joint UK Land Environment Simulator (JULES) version 3.0. The model satisfactorily represented the rainfall regime over the northeast region of Brazil (NEB) during the wet period. In the dry period, however, the coastal rainfall pattern over the NEB region was underestimated. In addition, the results showed that the surface fluxes linked to the energy balance in the Caatinga were impacted by the effects of rainfall seasonality in the region. The assessment of the BRAMS model’s performance demonstrated that it is a reliable tool for studying the dynamics of the dry forest in the region, providing valuable support for sustainable management and conservation efforts. Full article