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Abstract Introduction Artificial intelligence (AI) has gained significant attention in dentistry due to its potential to revolutionize practice and improve patient outcomes. However, dentists’ views and attitudes toward technology can affect the application of AI. This perception and attitude can be affected by the personality traits of individuals. This study aims to evaluate the perceptions and attitudes of dentistry students toward AI. Methods This cross-sectional study was conducted on dental students at Ordu University Faculty of Dentistry, involving a sample of 83 students. The study utilized the Big Five 50 Test to evaluate personality traits and a 5-point Likert scale to gather data on 20 statements regarding AI in dentistry. Data were analyzed using IBM SPSS Statistics software, and a chi-square test was employed to assess the relationship between the personality traits of dental students and their attitudes towards artificial intelligence, as well as the relationship between the gender of dental students and their attitudes towards artificial intelligence. Statistical significance was set at P

IntroductionWastewater treatment plants (WWTPs) and drinking-water treatment plants (DWTPs) are critical for public health due to the potential risks posed by microorganisms that may persist after treatment. The aim of this study was to detect the microbiome profiles of waters from both DWTPs and WWTPs under the Istanbul Water and Sewerage Administration (ISKI), identify the antimicrobial resistance profiles in all these facilities, and observe the differences in the microbiome between the inlet and outlet of different WWTPs.MethodsA total of 52 samples were examined, comprising 18 samples from DWTPs and 34 samples from WWTPs. All water samples underwent pre-isolation filtration. DNA isolation was conducted using filter material, followed by sequencing on a NovaSeq 6000 instrument. Kraken2 tools and R scripts were used for statistical analysis and data visualization.ResultsThe microbial metagenomic analysis identified 71 phyla, 113 classes, 217 orders, 480 families, and 1,282 genera across all samples. There were unclassified microbes (53.14% vs. 58.75%), Eukaryota (3.64% vs. 3.5%), Archaea (0.08% vs. 0.03%), bacteria (42% vs. 36.25%), and viruses (0.02% vs. 0.04%) in the raw water and ozonation unit outlet of DWTPs. The inlet and outlet of WWTPs showed unclassified microbes (52.68% vs. 59.62%), Eukaryota (0.6% vs. 1.72%), Archaea (0.26% vs. 0.15%), bacteria (46.43% vs. 38.43%), and viruses (0.05% vs. 0.04%). No statistically significant results were found in the analysis of raw waters collected from DWTPs and samples taken from the ozonation unit outlet—from the phylum level to the genus level (p > 0.05). The inlet and outlet points of WWTPs showed no statistically significant results from the phylum to species levels (p > 0.05). The most detected genera were Desulfobacter (4.82%) in preliminary WWTPs, Thauera (1.93%) in biological WWTPs, Pseudomonas (1.44%) in advanced biological WWTPs, Acidovorax (1.85%) in biological package WWTPs, and Pseudomonas (11.55%) in plant-based WWTPs. No antimicrobial resistance gene markers were detected in water samples from raw water inlets and ozonation unit outlets from DWTPs, membrane wastewater recovery plants, or ultraviolet (UV) recycling facilities. The ANT(3″), Erm, and Sul resistance gene markers were detected in all raw WWTPs samples.DiscussionThere were no significant microbial risk differentiation between biological WWTPs and advanced biological WWTPs. The data could serve as preliminary information for future research. More extensive studies are needed, with multiple sample tracking in these facilities and their feeding basins.

Introduction: Artificial intelligence (AI) has gained significant attention in dentistry due to its potential to revolutionize practice and improve patient outcomes. However, dentists' views and attitudes toward technology can affect the application of AI. This perception and attitude can be affected by the personality traits of individuals. This study aims to evaluate the perceptions and attitudes of dentistry students toward AI. Methods: This cross-sectional study was conducted on dental students at Ordu University Faculty of Dentistry, involving a sample of 83 students. The study utilized the Big Five 50 Test to evaluate personality traits and a 5-point Likert scale to gather data on 20 statements regarding AI in dentistry. Data were analyzed using IBM SPSS Statistics software, and a chi-square test was employed to assess the relationship between the personality traits of dental students and their attitudes towards artificial intelligence, as well as the relationship between the gender of dental students and their attitudes towards artificial intelligence. Statistical significance was set at P < 0.05. Results: The study involved 83 participants, with 29 male and 54 female participants. The most common personality traits were Openness and Agreeableness, whereas the least common was Extraversion. Participants found AI useful and believed it could help dentists evaluate radiographs. However, the least agreed statement was that they would trust AI more than a dentist in evaluating radiograph results. A statistically significant difference was found between personal traits of dental students and in expressions comparing dentists and AI. Males were more familiar with AI than females. Conclusion: This study found that attitudes towards AI in dentistry vary based on personality traits. Developing educational strategies tailored to these traits can help foster more positive attitudes and improve AI integration into dental practice. [ABSTRACT FROM AUTHOR]

Conventional hydrophobic membranes used in membrane distillation (MD) face significant challenges, such as severe membrane fouling and wetting, when treating surfactant-containing oily wastewater. Current strategies to modify surfaces for anti-fouling and anti-wetting purposes are often complex and time consuming, potentially compromising the flux in MD. This study investigates the characteristics and performance of novel fabricated Janus membranes for the treatment of oily hypersaline wastewater in membrane distillation applications. Janus membranes, which have a dense polyamide layer containing cellulose nanocrystal nanoparticles, were synthesized using the reverse interfacial polymerization (R–IP) method on a polyvinylidene fluoride (PVDF) substrate. Characterization using scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared spectrometry (FTIR), X-ray photoelectron spectroscopy (XPS), water contact angle (WCA) revealed that the modified Janus membranes exhibited an enhanced hydrophilicity and structural integrity due to the incorporation of cellulose nanocrystal nanoparticles. The fabricated membranes showed a dense surface layer without visible pores or cracks, with micron-scale patterned structures and widely distributed wrinkles, particularly at higher concentrations of cellulose nanocrystal nanoparticles. The desalination performance was evaluated in an air gap membrane distillation setup, where the modified Janus membranes demonstrated higher water vapor fluxes and stable salt rejection, even in the presence of surfactants. The wettability and fouling resistance of the Janus membranes were evaluated, with the PVDF-RIP0.5C membrane showing the highest hydrophilicity and underwater oleophobicity, thereby preventing oil adhesion and membrane fouling. These results underline the potential of Janus membranes with incorporated cellulose nanocrystal nanoparticles for efficient desalination and treatment of oily saline wastewater in the MD process. [Display omitted] • Formation of a dense polyamide layer containing CNC nanoparticles with improved hydrophilicity and structural integrity. • Exhibited higher water vapor flux and stable salt rejection in membrane distillation, even with surfactants. • PVDF-RIP0.5C membrane demonstrated superior resistance to oil fouling and oil adhesion. • CNC-modified Janus membranes proved effective for desalinating and treating oily saline wastewater. [ABSTRACT FROM AUTHOR]