Apollonia University

Neuromarketing has emerged as a rapidly expanding field aimed at understanding the neural mechanisms that shape consumer behaviour. Yet despite its empirical success, the field lacks a unifying computational theory. In contrast, cognitive neuroscience increasingly converges on the Bayesian Brain and predictive-coding frameworks, which conceptualise perception, learning, and decision-making as hierarchical predictive processes driven by minimisation of precision-weighted prediction errors. This paper introduces Predictive Neuromarketing, a hybrid neuroscience-marketing paradigm that integrates predictive coding with consumer neuroscience findings. We develop a mathematical framework that formalises consumer expectations, brand priors, price cues, and prediction errors, providing a computational explanation for phenomena such as price placebo effects, brand-identity modulation, electroencephalography (EEG)-based preference prediction, and neuroforecasting of advertising succesa. We then reinterpret the empirical neuromarketing literature through this lens and propose experimental paradigms to test predictive-coding principles in consumer contexts. By embedding neuromarketing within a rigorous predictive framework, we offer a mechanistic account of how marketing stimuli shape consumer beliefs, valuation, and behaviour. The paper concludes with ethical considerations and a research agenda for advancing Predictive Neuromarketing. The contribution of this worki s the formal integration of consumer neuroscience findings into a cohesive Bayesian and predictive-coding generative model, producing clear, testable computational predictions without introducing additional empirical data.

Advances in the understanding of exosome biology have led to their recognition as the heart of intercellular communications. Additionally, the new insights into the role of exosomes in neuroinflammation and spreading of characteristic Alzheimer's Disease (AD) pathologies, open the scope for their use as a key target for diagnostic and therapeutic innovation. The immuno-engineering platforms are sensitive for detecting exosomal biomarkers including amyloid-B species, phosphorylated tau, and regulatory microRNAs, in peripheral biofluids with minimal invasion. Hence, these are potential platforms that can facilitate early diagnosis of AD. Although most supporting evidence is presently derived based on preclinical models and limited observational cohorts, these findings support minimally invasive opportunities for early disease monitoring and diagnosis, possibly even before the manifestation of clinical symptoms of AD. Alongside, another advantage of engineered exosomes is their flexible framework for targeted drug delivery. Since engineered exosomes are derived from neuronal or mesenchymal stem cells, they can cross the blood-brain barrier and deliver neuroprotective or immunomodulatory agents with high specificity. Presently, the target precision and off-target biodistribution of engineered exosomes are one of the most active areas of investigation. More encouragingly, incorporating nanotechnology for surface modification and cargo loading strategies can further enhance exosomal signaling, delivery efficiency, and cell-specific uptake by neuronal and glial cells. These applications, however, are presently challenging in terms of scalability and reproducibility. Also, a number of advancements are needed in areas of standardization of exosome isolation, scalable manufacturing, regulatory frameworks, and biological heterogeneity. Overcoming these challenges, however, is feasible with the integration of Artificial Intelligence and multi-omics profiling, and optimisation of exosome-based interventions.

Background: Predicting the number of euploid embryos is critical for optimising IVF outcomes and managing patient expectations. While maternal age and anti-M & uuml;llerian hormone (AMH) are established markers of ovarian reserve, their combined predictive power regarding chromosomal normality remains a subject of clinical debate. Artificial intelligence is increasingly being explored in assisted reproduction as a non-invasive, data-driven approach to estimate embryo ploidy. By leveraging advanced models such as convolutional neural networks (CNNs) and machine learning algorithms to evaluate morphological and morphokinetic characteristics from time-lapse sequences, Al contributes to improving the accuracy and objectivity of embryo selection. Objective: This study evaluated the statistical association between maternal age. AMH levels, and fertilisation methods (IVF, ICSI, IMSI) and euploid embryo yield. A secondary objective was to translate these clinical findings into a visual decision-support system (DSS) grounded in an Explainable Al (XAI) framework. Methods: A retrospective observational study was conducted on 31 patients undergoing IVF with PGT-A. Statistical significance was assessed using one-way ANOVA and multiple linear regression. Building on these data, a specialised decision-support system was developed using React 19 and TypeScript, employing a binomial probability model to translate clinical biomarkers into intuitive success simulations. Results: Patients younger than 35 years exhibited significantly higher AMH levels (p = 0.033) and a higher mean number of euploid embryos (p = 0.032) compared to those greater than 35. The fertilisation method did not significantly influence euploidy outcomes (p = 0.99) The regression model was statistically significant (p = 0.03) explaining 22.1% of the variance. However, none of the individual predictors reached statistical significance, suggesting that the observed effect may be driven by the combined contribution of the variables rather than by independent effects. The resulting DSS operationalises these findings in a preliminary manner through real-time attrition modelling and "Opportunity Cost" visualisations. Conclusion: Maternal age may represent an important factor in embryo euploidy, while AMH provides a quantitative baseline for embryo yield. By synergising retrospective data with explainable Al, the developed framework offers a transparent, data-driven approach to fertility counselling. This study indicates that integrating statistical analysis with a visual decision-support system effectively bridges the gap between raw clinical data and patient-centred practice, facilitating more objective decision-making in in vitro fertilisation. The development of a conceptual decision support system based on these findings derived a secondary objective of the paper by exploring it.

Background: Obesity affects both physical and mental health, and bariatric patients often show high levels of psychological distress. Cardiac adipose tissue, which includes epicardial and pericardial fat, is an active fat depot linked to inflammation and cardiovascular risk. Its relationship with anxiety has not been well studied, especially in bariatric candidates. Methods: This cross-sectional study included 29 adults undergoing presexative evaluation for bariatric surgery. All participants completed the Hamilton Anxiety Rating Scale and underwent CT imaging to measure epicardial and pericardial adipose tissue thickness. Additional adiposity measures included BMI, waist circumference, abdominal wall thickness, and adipose tissue density: Correlations and simple linear regressions were used to examine associations between anxiety severity and adiposity markers, Group differences across obesity grades were assessed with one way ANOVA. Results: Higher anxiety scores were strongly associated with greater pericardial fat thickness (r 0.621. p < 0l ) epicardial fat thickness fr = 0.667 p < 0.001 ) BMI (r=0.840, p < 0.001). waist circumference 6r = 0.748 p < 0.001 ) and abdominal wall thickness fr = 0.494 p 0.007). Both pericardial and epicardial fat thickness significantly predicted Hamilton total score in regression models. Conclusion: Anxiety severity in bariatric patients is closely related to several markers of adiposity, especially cardiac adipose tissue thickness. These findings suggest that cardiac adipose tissue may play a meaningful role in the psychological profile of individuals with severe obesity. Integrating both biological and psychological factors may improve the assessment and care of bariatric candidates.Artificial intelligence, especially deep learning techniques, is starting to play an increasingly important role in the assessment of epicardial and pericardial adipose tissue. It allows for automated segmentation and quantification based on CT images, providing high accuracy and reducing the time required for data processing. Recent artificial intelligence models, such as convolutional neural networks and U-Net architectures, have demonstrated significant agreement with manual measurements performed by specialists, which supports the possibility of their integration into routine clinical assessment. In the case of bariatric patients, these technologies can increase the accuracy of cardiac adipose tissue assessment and facilitate a broader understanding of the relationship between obesity, cardiovascular risk and the associated psychological impact.

Background: Adolescent pregnancy remains a major global public health issue, often linked to socioeconomic and educational disparities rather than biological immaturity. This study aimed to identify sociodemographic factors associated with adolescent pregnancies and to evaluate their impact on maternal and neonatal outcomes in a tertiary hospital in Northeastern Romania. Methods: A retrospective analysis was conducted at the "Cuza Vodă" Obstetrics-Gynecology Clinic Hospital, Iași, over two periods: 2013-2017 and January-October 2025. Records of 637 mothers aged <20 years were reviewed. Variables included age, education, prenatal monitoring, gestational age, delivery mode, neonatal outcomes, and obstetric complications. Statistical analyses were performed using SPSS v26, employing ANOVA, Welch ANOVA, and post hoc tests (p < 0.05). Ethical approval was obtained from the institutional ethics committee. Results: The mean maternal age was 17.26 ± 1.5 years, with 82.6% from rural areas. Most had only primary or lower secondary education. Fully monitored pregnancies were associated with significantly higher birth weights (mean = 3249 g) compared with unmonitored pregnancies (mean = 3009 g; p < 0.001). Infants of mothers with low education had the lowest mean birth weights (2963 g; p = 0.002). Preterm births represented 14.3% of cases, and cesarean deliveries accounted for 34.5%. A slight but significant increase in maternal age was observed between 2013-2017 and 2025 (p < 0.001), suggesting delayed adolescent childbearing. Conclusions: Low educational attainment and inadequate prenatal monitoring remain major determinants of adverse neonatal outcomes among adolescent mothers. Comprehensive sexual education, improved prenatal care accessibility, and social support programs are essential to reduce adolescent pregnancy rates and improve reproductive health in Romania.