Behavioral addiction in digital environments is an increasingly relevant neurobehavioral phenomenon characterized by persistent engagement with high-frequency, algorithmically optimized reward stimuli. Although neural correlates of addictive behaviors have been widely studied, current models only partly explain how modern reinforcement environments reorganize behavior at the systems level. This review introduces Reward Instability Theory, a conceptual dynamical systems framework proposing that behavioral addiction may emerge as an attractor-like state within distorted reward landscapes shaped by high-density and high-variance reinforcement signals. The model shifts focus from static behavioral descriptions toward a systems account of motivation involving reinforcement learning, salience attribution, executive control, and environmental reward structure. We propose that digital environments may increase reinforcement density and reward variance, promoting dominant reward peaks and reducing behavioral diversity. To formalize these dynamics, we outline the Behavioral Reward Instability Index (BRII) as a heuristic systems construct integrating individual reward sensitivity, environmental reinforcement structure, and behavioral variability. The framework also situates established addiction models—including incentive sensitization, habit formation, and allostatic regulation—within a shared dynamical architecture. In addition, digital phenotyping is discussed as a potential empirical strategy for testing reward instability, while acknowledging limitations related to signal noise, ecological validity, bias, and privacy. This model is intended to explain problematic patterns characterized by reduced behavioral flexibility, persistence despite negative consequences, and functional impairment, rather than all forms of frequent digital use. Attractor-like terminology is used throughout as a conceptual heuristic to describe behavioral persistence and reduced flexibility, rather than as evidence of formally verified mathematical attractors.

Behavioral addiction in digital environments is an increasingly relevant neurobehavioral phenomenon characterized by persistent engagement with high-frequency, algorithmically optimized reward stimuli. Although neural correlates of addictive behaviors have been widely studied, current models only partly explain how modern reinforcement environments reorganize behavior at the systems level. This review introduces Reward Instability Theory, a conceptual dynamical systems framework proposing that behavioral addiction may emerge as an attractor-like state within distorted reward landscapes shaped by high-density and high-variance reinforcement signals. The model shifts focus from static behavioral descriptions toward a systems account of motivation involving reinforcement learning, salience attribution, executive control, and environmental reward structure. We propose that digital environments may increase reinforcement density and reward variance, promoting dominant reward peaks and reducing behavioral diversity. To formalize these dynamics, we outline the Behavioral Reward Instability Index (BRII) as a heuristic systems construct integrating individual reward sensitivity, environmental reinforcement structure, and behavioral variability. The framework also situates established addiction models—including incentive sensitization, habit formation, and allostatic regulation—within a shared dynamical architecture. In addition, digital phenotyping is discussed as a potential empirical strategy for testing reward instability, while acknowledging limitations related to signal noise, ecological validity, bias, and privacy.

Minimally invasive aortic valve replacement through a right anterior thoracotomy is limited by prolonged operative times. We assessed whether a rapid-deployment prosthesis reduces those times and improves early outcomes when surgical access, team and era are held constant. Single-centre retrospective cohort study of 204 consecutive isolated aortic valve replacements performed through a right anterior thoracotomy by one surgical team between 2023 and 2025. Patients receiving a mechanical prosthesis (n = 5) or a 29 mm sutured valve (n = 7, a size not manufactured for the study device) were excluded, leaving 192 patients: 70 received the Edwards Intuity rapid-deployment valve and 122 a conventional sutured bioprosthesis. Prosthesis choice was at the surgeon’s discretion. Primary endpoints were aortic cross-clamp and cardiopulmonary bypass time. Groups were compared by multivariable regression adjusted for age, sex, EuroSCORE II, NYHA class, ejection fraction and implanted valve size, with robust standard errors for continuous outcomes and Firth-penalised logistic regression for binary outcomes. Aortic cross-clamp time was shorter with the Intuity (median 61.0 [54.0–75.0] versus 91.0 [76.0–108.5] min; adjusted difference −26.9 min, 95% CI −33.7 to −20.1; p < 0.001), as was cardiopulmonary bypass time (103.5 [84.2–115.0] versus 131.0 [115.0–153.0] min; adjusted difference −29.4 min, −38.7 to −20.2; p < 0.001). Mean and peak transvalvular gradients were lower (adjusted differences −5.52 mmHg, −6.80 to −4.25, and −11.50 mmHg, −13.90 to −9.10; both p < 0.001) and hospital stay was shorter by 1.4 days (−2.4 to −0.4; p = 0.009). In-hospital mortality was 1/70 versus 1/122 (p = 1.000). Permanent pacemaker implantation occurred in 6/70 (8.6%) versus 2/122 (1.6%) (unadjusted p = 0.053) and paravalvular leak of any grade in 7/69 (10.1%) versus 8/121 (6.6%); with eight and fifteen events respectively, neither endpoint could be estimated with useful precision; these non-significant comparisons should not be read as evidence of comparable safety. Within a single access, team and era, the rapid-deployment prosthesis was associated with substantially shorter operative times, lower gradients and shorter hospital stay, with similar observed in-hospital mortality (1/70 versus 1/122); with only two deaths overall this comparison cannot establish equivalence. These findings describe operative efficiency rather than demonstrated clinical superiority, and the safety comparisons remain underpowered.

Background: Impulsivity is a key transdiagnostic trait linked to dysregulated reward processing in both behavioral and substance-related addictions, as recognized in DSM-5. Dopaminergic genes, including DRD2, may modulate individual differences in impulsivity, particularly under condi-tions of in-creased addiction vulnerability. This study examined whether the DRD2 Exon 8 rs6276 poly-morphism predicts impulsivity levels in men with mixed addictive behaviors compared with healthy controls. Methods: 328 males (128 with mixed behavioral and substance addictions and 200 controls) were assessed. Diagnoses were confirmed using the MINI interview, and impulsivity was measured with the Barratt Impulsiveness Scale (BIS-11). Genotyping of rs6276 was performed using real-time PCR. Statistical analysis included non-parametric tests and two-way ANOVA. Results: There were no significant differences in rs6276 genotype or allele frequencies between groups. However, participants with mixed addictions scored significantly higher on BIS-11 total, motor, and attentional impulsivity scales. A significant interaction between genotype and clinical status was found for motor impulsivity and total scores, with the G allele linked to elevated impulsivity in the clinical group. Conclusions: The DRD2 rs6276 polymorphism may act as a trait-modifying factor, enhancing impulsivity in individuals with mixed addictions. These findings support a gene–environment interaction model and underscore the value of dimensional phenotyping in addiction research.

Background: The perimenopausal period, typically occurring between the ages of 40 and 55 years, represents a critical transitional stage in women’s physiology characterized by profound hormonal, metabolic, and systemic adaptations. While the endocrine aspects of this transition have been extensively investigated, increasing attention has recently been directed toward the role of micronutrient homeostasis in maintaining metabolic health and physical performance during this period. Therefore, the aim of this narrative review is to summarize current knowledge regarding microelement deficiencies in perimenopausal women and their potential impact on metabolic health and physical performance. Additionally, the review discusses the potential applications of VIS–NIR tissue spectrophotometry, including diagnostic platforms, such as SO/CHECK, as innovative tools for functional metabolic assessment. Materials and methods: This narrative review is based on literature concerning perimenopause, micronutrient deficiencies, mitochondrial bioenergetics, oxidative stress, intestinal barrier function, physical performance, and visible and near-infrared (VIS–NIR) tissue spectrophotometry. The reviewed literature includes experimental, clinical, and review studies addressing essential microelements and functional diagnostic approaches, including platforms such as SO/CHECK. Results: Essential microelements such as magnesium, zinc, selenium, iodine, and phosphorus participate in numerous biochemical processes including mitochondrial energy production, enzymatic regulation, antioxidant defense, and hormonal metabolism. Deficiencies in these elements may contribute to impaired mitochondrial function, increased oxidative stress, disturbances in intestinal barrier integrity, and reduced exercise tolerance. Functional metabolic disturbances may develop before measurable changes in blood parameters. VIS–NIR tissue spectrophotometry represents a promising non-invasive approach for assessing metabolic status at the tissue level. Conclusions: Micronutrient status plays a key role in metabolic health during perimenopause. Integration of functional diagnostic approaches, including VIS–NIR-based systems, such as the SO/CHECK platform, may support early detection of metabolic disturbances and personalized interventions.