Conectar
by Viet Anh Nguyen, Ngo The Minh Pham, Minh Ngoc Tran, Thi Bich Ngoc Ha, Thi Quynh Trang Vuong
IntroductionBonding fixed appliances to zirconia restorations is challenging, yet adult orthodontics increasingly involves ceramic crowns and patient-driven esthetic choices such as lingual appliances. Customized lingual brackets may improve fit and reduce adhesive thickness, but evidence on their bonding to zirconia is limited.
Materials and methodsThis in vitro study evaluated the shear bond strength of customized lingual brackets bonded to glazed zirconia after airborne-particle abrasion. Bracket manufacturing was either three-dimensionally (3D) printed cobalt-chromium or cast nickel-chromium. Primers were a universal adhesive (Single Bond Universal, 3M) or a primer containing 10-methacryloyloxydecyl dihydrogen phosphate Z-Prime Plus (Bisco), and adhesives were a light-cure orthodontic composite or a dual-cure resin cement. One hundred twenty-eight specimens (n = 16 per group) were tested. Shear bond strength was analyzed with three-way ANOVA, followed by post-hoc Tukey tests. Adhesive Remnant Index (ARI) scores were evaluated with ordinal regression. Significance was set at α = 0.05.
ResultsManufacturing modality significantly affected bond strength, with additively manufactured cobalt-chromium exceeding cast nickel-chromium (P = 0.049). The primer category and polymerization mode showed no significant main effects (P > 0.20) and no significant interactions. Group means clustered 9–10 MPa, and all combinations met the clinically accepted threshold. Additively manufactured brackets exhibited lower ARI scores than cast brackets (P Conclusions
On glazed, sandblasted zirconia, shear bond strength of customized lingual brackets showed a borderline main effect of fabrication method, whereas primer type and adhesive polymerization mode were not statistically significant. Failures were predominantly located at or near the zirconia–adhesive interface. Within this in vitro model, base manufacturing may warrant attention, whereas primer and curing mode may be selected for handling and workflow considerations, with clinical relevance yet to be established.
by Laura Maniscalco, Marco Enea, Peter de Winter, Neeltje de Vries, Anke Boone, Olivia Lavreysen, Kamil Baranski, Walter Mazzucco, Adriano Filadelfio Cracò, Malgorzata Kowalska, Szymon Szemik, Lode Godderis, Domenica Matranga
According to the World Health Organization (WHO), in 2022 there was a shortfall of approximately 1.2 million doctors, impacting healthcare system and patient care. Understanding turnover intentions is crucial for managing the healthcare workforce and ensuring continuous, and high-quality patient care. This study investigates the prevalence of physicians planning to leave their hospital or the profession, and risk factors such as job demand, resources, satisfaction, and burnout across four European countries. A cross-sectional multicenter study was conducted in eight hospitals across Belgium, the Netherlands, Poland and Italy, including both academic and non-academic institutions. Data from Poland were excluded due to a low response rate, to preserve respondent anonymity. Multivariable logistic regression analyses were performed, adjusted for country, demographics, and work context, using significant variables from the univariable analysis. The overall intention to leave the hospital was 16.5%, with the highest rates in Belgium (19.6%) and Italy (19%), and the lowest in the Netherlands (9.8%). The intention to leave the profession was 9.1%, with the highest rate in the Netherlands (16.1%), followed by Belgium (6.3%) and Italy (5.7%). Physicians at higher risk of leaving the hospital were younger (adjOR = 0.90, 95%CI = 0.86–0.93), lacked colleague support (adjOR = 3.18, 95%CI = 1.06–9.36), and were dissatisfied with job prospects (adjOR = 2.38, 95%CI = 1.02–5.54) and overall work (adjOR = 2.71, 95%CI = 1.09–6.69). Those more likely to leave the profession were from the Netherlands (adjOR = 4.14, 95%CI = 1.62–11.4), surgeons (adjOR = 2.90, 95%CI = 1.22–6.78), working in non-academic hospitals (adjOR = 2.43, 95%CI = 1.01–5.97), lacked development opportunities (adjOR = 5.97, 95%CI = 1.01–36.2), or were dissatisfied with career prospects (adjOR = 2.77, 95%CI = 1.04–7.27). Health system managers and relevant stakeholders involved in the planning, implementation, or evaluation of health policies and reforms aimed at improving healthcare job retention should take into account the key determinants of the intention to leave identified in this study.by Tu Ngoc Tran, Nguyen Phan Thu Hang
This paper aims to explore the effect of User-Generated Content (UGC) on the purchasing behavior of environmentally friendly products at Hospitality and Food Service Industry in Vietnam, particularly in Ho Chi Minh City. A conceptual model has been developed based on literature reviews and empirical studies. Furthermore, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method was employed to investigate the impact of UGC on the purchasing behavior of environmentally friendly products at Hospitality and Food Service Industry in Vietnam. The results confirm that environmental concerns, attitudes, and the intention to purchase green products are all positively and significantly influenced by UGC. Especially, the results validate that brand reputation plays a moderating role in the connections between UGC and environmental concern, UGC and environmental attitude, as well as the relationship between the intention to purchase environmentally friendly products and actual purchasing behavior.by Hoc Tran, Olaf Berke, Nicole Ricker, Zvonimir Poljak
BackgroundH3 influenza A viruses (IAV) have been shown to frequently cross the species barrier which can be an important factor in sustained transmission and spread. Machine learning methods have been widely explored for host prediction of IAV using genomic data; however, this is often done using data from only one of the eight IAV segments or by using all available IAV data to predict broad categories of hosts.
ObjectiveThe objective of this study was to combine machine learning algorithms with H3 IAV sequence data from all eight segments to train predictive machine learning models for distinct host prediction and validate model performance.
MethodsModels were trained on both k-mers and amino acid properties alongside machine learning algorithms that included random forest and XGBoost for each of the eight IAV genome segments. Models were then validated on a test dataset through analytics of model class predicted probabilities and subsequently used to investigate between-species transmission patterns within case studies including canine H3N8, swine H3N2 2010.2, and duck H3 sequences.
ResultsModels demonstrated strong performance in host prediction across all eight segments on the test dataset, with overall accuracies and κ (kappa) values ranging from 0.995–0.997, 0.984–0.990, respectively. Misclassified test dataset sequences with high predicted probabilities (> 90%) were validated using available literature and were identified to be frequently associated with between-species transmission events. Between-species transmission patterns within case study model class predicted probabilities were also identified to be consistent with the literature in cases of both correct and incorrect classification.
ConclusionsThese models allow for rapid and accurate host prediction of H3 IAV datasets from any of the eight IAV segments and provide a solid framework that allows for identification of variants with higher than typical between-species transmission potential. However, results obtained on selected case studies suggest further improvements of the training and validation processes should be considered.
by Ibrahim Aqtam, Ahmad Ayed, Ahmad Batran, Moath Abu Ejheisheh, Riham H. Melhem, Mustafa Shouli
IntroductionWork engagement, defined as a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption, is crucial for nurse retention and quality of care in high-stress environments. Neonatal Intensive Care Units (NICUs) present unique emotional and psychological challenges for nurses, necessitating skills like emotional intelligence (EI) to enhance work engagement. This study investigates the association between EI, demographic factors, and work engagement among Palestinian NICU nurses.
MethodsA cross-sectional, descriptive correlational design was employed during February-April 2025. Of 230 nurses invited, 207 completed the survey (response rate = 90.2%) across 12 Palestinian NICUs using convenience sampling. Data analysis was conducted using descriptive statistics, Pearson’s correlation, and multiple linear regression via SPSS v26. Validated tools, the Schutte Self-Report Emotional Intelligence Test (SSEIT) and Utrecht Work Engagement Scale (UWES), were used.
ResultsEmotional intelligence (EI) demonstrated a strong positive correlation with work engagement (r = 0.693, p B = 0.463, β = 0.535, p = 0.002), female gender (B = −2.250, β = −0.115, p = 0.017), and rotating shifts (B = 1.579, β = 0.105, p = 0.028) were significant predictors. EI was the strongest predictor (B = 0.358, β = 0.593, p M = 47.3 ± 5.8).
DiscussionThe findings demonstrate strong associations between EI and engagement in high-stress NICU environments. Based on these findings, we propose implementing comprehensive EI training programs in nursing curricula, establishing mentorship programs to address age-related disparities, and developing gender-sensitive workplace policies to optimize work engagement and improve patient care quality.
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