Cytochrome P450 3A4 (CYP3A4) is a key enzyme involved in the metabolism of nearly half of all clinically used drugs, including widely prescribed statins and antidiabetic agents. Dietary constituents can modulate CYP3A4 expression and activity through various mechanisms, thereby altering drug pharmacokinetics and potentially leading to therapeutic failure or toxicity. This narrative review compiles current evidence on dietary modulation of CYP3A4, with a particular focus on pharmacological and clinical implications for lipid-lowering and glucose-lowering drugs. Literature was identified through a comprehensive search in PubMed, Scopus, and Web of Science, including preclinical and clinical studies addressing food–drug interactions involving CYP3A4 substrates. Numerous dietary compounds, such as citrus furanocoumarins, polyphenols, herbal extracts, and vitamins, act as CYP3A4 inhibitors or inducers through competitive, mechanism-based, or nuclear receptor-mediated pathways. Specific examples include simvastatin, atorvastatin, repaglinide, and saxagliptin, whose systemic exposure can be significantly altered by dietary factors. Moreover, interindividual variability in CYP3A4 activity may be shaped by genetic polymorphisms, microbiota-derived metabolites, and epigenetic regulation, further influencing drug response. Understanding these interactions is crucial, especially in polymedicated patients or those receiving drugs with a narrow therapeutic index. Clinicians should remain aware of potential CYP3A4-related food–drug interactions and consider dietary habits and supplement use in therapeutic decision-making. Future research should aim to integrate pharmacogenomics, gut microbiome profiling, and personalized nutrition in order to improve the prediction and prevention of clinically significant interactions.

Novel cooperative intelligent transportation systems (ITS) serve as the basis for the provision of a number of services for drivers, occupants, and third parties. The vast amount of information to be collected, especially in vehicle-to-infrastructure (V2I) communication services, requires new algorithms and hardware platforms to cope with real-time requirements; however, this combination is not properly addressed in the literature. In this paper, we introduce a high-throughput hardware-software infrastructure to gather information from vehicles and efficiently process it to provide novel ITS services. We propose a parallelization approach of a fuzzy clustering technique on heterogeneous servers based on CPU and several GPUs, tailored to classification problems in V2I. The infrastructure is empirically tested to offer a geo-located pollution information service through the periodical collection of both vehicle's position and status data. We offer a real service that correctly identifies highly polluting traffic areas and drivers. The results indicate a good performance of the system under high loads, and our scalability analysis reveals a good operation in real-ambitious deployments thanks to the use of the both CPU and multiple GPUs, showing that our proposal can efficiently host cooperative services involving high processing in the ITS context.

The numerical value discretization is a process that is performed in the data preprocessing phase of intelligent data analysis. Preprocessing phase is very relevant because the quality of the models obtained in data mining step depends on this phase.

Background: Autism spectrum disorder (ASD) is consistently associated with a high prevalence of sleep disturbances across the lifespan, with reported rates ranging from 60% to 86% depending on age and clinical characteristics. Although this issue has been widely described in the international literature, Spain currently lacks large-scale data to estimate the prevalence of sleep disturbances or to examine their relationship with factors such as age, intellectual disability, and co-occurring conditions. This study aims to estimate the prevalence and severity of sleep disturbances in individuals with autism spectrum disorder in Spain and to examine their associations with developmental stage, intellectual disability, affective symptoms, and contextual factors. Methods: This is a cross-sectional observational survey with nationwide dissemination approved by the Ethics Committee of the Universidad Católica San Antonio de Murcia. Data will be collected through an online survey (SurveyMonkey) including validated instruments: the Children’s Sleep Habits Questionnaire–Autism (CSHQ-Autism) and the Sleep Disturbance Scale for Children (SDSC) for pediatric participants; the Pittsburgh Sleep Quality Index (PSQI) for adolescents and adults without intellectual disability; and the Diagnostic Assessment for the Severely Handicapped–II (DASH-II) for adults with intellectual disability. Anxiety and depressive symptoms will be assessed using the Child Behavior Checklist (CBCL) in children and adolescents and the Hospital Anxiety and Depression Scale (HADS) and DASH-II. Statistical analyses will be conducted using SPSS v22 by applying parametric or non-parametric tests according to data distribution. Conclusions: This study represents one of the first survey protocols with nationwide dissemination designed to assess sleep disturbances in individuals with ASD in Spain. The resulting findings are expected to help identify vulnerability profiles, inform public health strategies, and support the development of multidisciplinary interventions aimed at improving sleep and, consequently, the quality of life of individuals with autism and their families.