Recent evidence links residential mobility, moving away from one's childhood region, to higher Dark Triad scores. We ask the mirror-image question. In a U.S. adult sample with known childhood and current state (N = 3958), respondents still living in their childhood state (stayers) scored higher on psychopathy than those who had moved away (movers). The difference was small and survived comparisons within origin state and birth cohort with the current state held fixed. Machiavellianism and narcissism showed no statistically significant adjusted difference. The stayer difference did not depend on sex or on how far movers had gone, and a state-level education proxy gave no sign of socioeconomic selection. Among movers, higher scorers on all three traits went to denser destinations, and higher psychopathy scorers to states with more homicide and inequality. Lifetime immobility and recent mobility thus relate to psychopathy in opposite directions, which is consistent with interstate moves being pulled by opportunities that reach high scorers less often. Individual socioeconomic status and reasons for moving were unmeasured.
The COVID-19 pandemic triggered an unprecedented economic shock, disrupting business operations and demand across sectors. This study examines the impact of the COVID-19 pandemic on the capital structure of small and medium-sized enterprises (SMEs) in the Portuguese hotel sector, a core segment of the hospitality industry and one of the most severely affected by the crisis. Using a panel dataset of private hotel firms in Portugal, we find a significant reduction in total debt during the pandemic. Decomposing debt by maturity reveals that firms reduced short-term borrowing while expanding long-term financing, indicating a recomposition of the debt structure during the pandemic. Furthermore, when firms are segmented by size, the reduction in total leverage was concentrated among smaller, financially constrained firms, while the expansion in long-term financing was observed among larger, financially unconstrained firms. A further decomposition of total liabilities into financial and operating components reveals that financial and operating liabilities responded in opposite directions during the pandemic: operating liabilities contracted, while financial liabilities, notably long-term financial debt, increased. This study contributes to the literature on capital structure, corporate finance, and crisis management by providing one of the first empirical assessments of COVID-19's effects on SME financing in a tourism-dependent, bank-reliant economy, a context that remains understudied. It also extends prior research by focusing on private rather than public firms. By examining a highly vulnerable sector, the findings offer insights into financial resilience during crises and provide policy implications for improving liquidity access and strengthening financial stability among tourism-dependent hotel SMEs.
This study aimed to evaluate the clinical outcomes of gingival recession Type 1 (RT1) treatment using a coronally advanced flap (CAF) alone and in combination with L-PRF at 6 months. A total of 70 RT1 from 19 patients were included. Participants were randomly assigned to the test group (TG, CAF + L-PRF, n = 42) and the control group (CG) (CAF alone, n = 28). Clinical parameters were assessed at baseline and at 6 months: the primary outcomes were percentage of root coverage (
This study assessed the prevalence and risk factors associated with erosive tooth wear (ETW) among the adult population. Clinical examinations were conducted using the Basic Erosive Wear Examination index, which assesses the severity of ETW in six sextants of the oral cavity. The risk factors for ETW included: (1) socio-demographic characteristics, (2) general health conditions, (3) vitamin C consumption, (4) beverage consumption, (5) acidic foods and drinks, (6) dairy products, (7) use of fluoridated mouthwashes and toothpastes, and (8) type of toothbrush. A one-way ANOVA test and an independent sample t-test were used; a p-value of 0.05 was considered the threshold for statistical significance. A total of 312 participants were included; 174 (55.8
Peri-implant diseases remain a major cause of late implant failure, and current risk assessment tools show limited capacity to integrate prosthetic factors, salivary biomarkers and artificial intelligence-based prediction. This study aimed to develop the Implant Success Prediction Tool (ISPT), a multifactorial peri-implant risk stratification system structurally designed for modular integration with artificial neural networks and salivary omics data. ISPT development followed three main pillars: (1) incorporation of Implant Disease Risk Assessment (IDRA)-validated clinical vectors, including bleeding on probing percentage, number of sites with probing depth ≥ 5 mm, bone loss in relation to age, periodontitis susceptibility, supportive periodontal therapy and hygiene/compliance parameters; (2) qualitative usability testing of IDRA by implantologists, who identified elements to maintain, clarify or expand; and (3) alignment with a precision medicine framework, establishing collaboration with a salivary diagnostics laboratory SalivaTec ( https://ciis.ucp.pt/salivatec ) to enable systematic saliva collection and future deep phenotyping. The final ISPT structure comprises ten standardized risk vectors displayed in a colour-coded radial traffic-light diagram, integrating six adapted IDRA-derived vectors and four novel vectors: abutment height/angulation; saliva collection/deep phenotyping vector (“salivaomics”); foreign bodies, titanium particles and tribocorrosion; and other for occlusal loading and functional risk. The tool is conceptually prepared to function as a structured input matrix for artificial neural networks, supporting longitudinal training with combined clinical and salivary data to predict implant outcomes (peri-implant health, mucositis, peri-implantitis) over a minimum 5-year monitoring period. ISPT represents the first peri-implant risk assessment tool explicitly designed for modular integration of artificial intelligence and salivary omics data within a precision dentistry framework. Its standardised vectors, traffic-light visualisation and longitudinal validation methodology provide a scalable structure for future externally validated predictive models of implant success and failure.
