Peruvian Union University

IntroductionSubstance use disorder (SUD) is a complex neurobiological disorder characterized by the consolidation of maladaptive neuroplasticity affecting dopaminergic, glutamatergic, and neurotrophic systems, as well as cortical and subcortical networks critical for executive control, emotional regulation, and associative learning.MethodsThis systematic review was conducted in accordance with PRISMA 2020 guidelines and integrated 57 studies published between 2020 and 2025 to analyze neuroplastic mechanisms involved in vulnerability to substance use disorder and brain recovery following chronic substance exposure.ResultsThe findings revealed consistent alterations in synaptic density, BDNF/TrkB signaling, glutamatergic homeostasis, and epigenetic regulation, along with structural and functional neuroimaging changes in regions such as the prefrontal cortex (PFC), nucleus accumbens (NAc), and amygdala. Four core therapeutic domains for neuroplastic restoration were identified: neuromodulation approaches (including repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation), compounds that promote neuroplasticity via neurotrophic signaling, epigenetic and anti-inflammatory interventions, and psychological therapies based on memory reconsolidation processes. These strategies demonstrated the capacity to normalize prefrontal activity, modulate reward networks, strengthen emotional regulation, and reduce craving.ConclusionDespite significant advances, important gaps remain, including methodological heterogeneity, scarcity of longitudinal studies, and limited clinical generalizability. Overall, the evidence suggests that recovery from substance use disorder requires multimodal interventions simultaneously targeting molecular, synaptic, and circuit-level plasticity, with growing emphasis on personalized approaches guided by neurobiological biomarkers.

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min-max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training-testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics-accuracy, precision, recall, F1-score, specificity, and AUC-alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care.

To culturally adapt the Patient Health Questionnaire (PHQ-9) for Aymara-speaking populations in Peru and Bolivia and to evaluate its psychometric properties. A study was conducted with a non-probabilistic sample of 1,607 Aymara speakers from Peru (n = 969) and Bolivia (n = 638), aged 18 to 74 years, with both sexes represented. In the first phase, forward and backward translations were carried out, followed by expert review to ensure cultural contextualization, and focus group with Aymara speakers. In the second phase, internal structure validity, external criterion validity, group-based invariance, and reliability were assessed. The Aymara PHQ-9 demonstrated a unidimensional factor structure with satisfactory fit indices (CFI TLI > 0.95 and RMSEA SRMR < 0.08) across Aymara variants in Peru and Bolivia. Measurement invariance was established across age, sex, residential area, education level, and marital status. External validity revealed significant relationships with the GAD-7 and WHO-5. Both country-specific versions showed high reliability coefficients (Peru α = 0.847 to 0.849; Bolivia ω = 0.943 to 0.942). The Aymara PHQ-9 for Peru and Bolivia demonstrated robust internal structure validity, external validity, measurement invariance, and reliability. This version is therefore recommended for depression screening among Aymara populations.

Introduction: The process of reading encompasses not only cognitive dimensions but also motivational factors, such as self-efficacy, which are crucial for learning among adolescents. These factors can be influenced by adverse physiological and emotional conditions. Consequently, the objective of this study was to ascertain whether depression, anxiety, and stress are negatively associated with reading self-efficacy in Peruvian adolescents. Method: This study employs a quantitative, non-experimental cross-sectional design, utilizing surveys to gather data from 463 adolescent students, aged 12 to 17 years, attending one public and two private schools in Chanchamayo, Jun & iacute;n, Peru. The research instruments include the Abbreviated Scales of Depression, Anxiety, and Stress (DASS-21) and the Self-Efficacy Scale for Reading. Results: The findings indicate that women exhibit significantly higher levels of depression (p < 0.05) and anxiety (p < 0.01) compared to men. A structural equation modeling (SEM) analysis was performed, demonstrating an excellent fit: chi(2) scaled = 1.18, df = 2, p = 0.553, CFI = 1.000, TLI = 1.006, RMSEA = 0.000 CI 95% [0.000, 0.069], and SRMR = 0.004. This analysis confirms that depression (beta = -0.20, p < 0.05) and anxiety (beta = -0.23, p < 0.01) are negatively associated with reading self-efficacy, whereas stress (beta = 0.03, p = 0.636) is not associated with reading self-efficacy among Peruvian adolescents. The model accounts for 14.5% of the variance in reading self-efficacy. Conclusion: Depression and anxiety are negatively associated with reading self-efficacy among Peruvian adolescents. Conversely, there is no empirical evidence to indicate that stress is linked with reading self-efficacy.

OBJECTIVE:To provide a state-of-the-art, otolaryngology-focused review of 22q11.2 deletion syndrome (22q11.2DS), emphasizing recent developments in the understanding of airway, hearing, velopharyngeal, sleep, and perioperative manifestations that directly influence contemporary ENT practice. DATA SOURCES:PubMed, Scopus, and Cochrane Library were searched for English-language publications from January 2018 to September 2025. Additional data were identified through reference lists of key guidelines and landmark cohort studies. REVIEW METHODS:A narrative review approach was used to synthesize recent literature addressing the epidemiology, anatomy, diagnostic advances, and management of otologic, palatal, airway, sleep, swallowing, vascular, immune, and perioperative features of 22q11.2DS. Priority was given to studies published within the past 5 years, multicenter analyses, updated clinical practice guidelines, and investigations describing evolving surgical and diagnostic paradigms. Articles focusing solely on cardiac, endocrine, or psychiatric aspects were excluded unless directly relevant to otolaryngologic care. CONCLUSIONS:22q11.2DS presents a uniquely high burden of ENT disease across the lifespan, including chronic otitis media, elevated cholesteatoma risk, persistent velopharyngeal dysfunction, multilevel airway anomalies, sleep-disordered breathing, dysphagia, and perioperative vulnerability due to vascular, immunologic, and hematologic anomalies. Emerging imaging techniques, updated pediatric and adult guidelines, and expanded genotype-phenotype data have reshaped diagnostic and surgical strategies. IMPLICATIONS FOR PRACTICE:Otolaryngologists play a central role in lifelong care of individuals with 22q11.2DS. Structured surveillance, genotype-informed imaging, proactive sleep and airway assessment, and multidisciplinary perioperative planning are essential for improving outcomes. Standardized pathways and longitudinal, ENT-specific research are urgently needed to guide evidence-based management.