Producción científica y docente
Producción científica reciente
Effects of a 12-week multicomponent exercise programme on physical function in older adults with cancer: Study protocol for the ONKO-FRAIL randomised controlled trial
Introduction Cancer in older adults is often associated with functional limitations, geriatric syndromes, poor self-rated health, vulnerability, and frailty, and these conditions might worsen treatment-related side effects. Recent guidelines for patients with cancer during and after treatment have documented the beneficial effects of exercise to counteract certain side effects; however, little is known about the role of exercise during cancer treatment in older adults. Materials and Methods This is a multicentre randomised controlled trial in which 200 participants will be allocated to a control group or an intervention group (the sample size has been calculated to detect a clinical difference of 1 point in Short Physical Performance Battery (SPPB) score, assuming an α error of 0.05, a β error of 0.20, and a 10 % loss rate). Patients aged ≥70 years, diagnosed with any type of solid cancer and candidates for systemic treatment are eligible. Subjects in the intervention group are invited to participate in a 12-week supervised multicomponent exercise programme in addition to receiving usual care. Study assessments are conducted at baseline and three months. The primary outcome measure is physical function as assessed by the SPPB. Secondary outcome measures include comprehensive geriatric assessment scores (including social situation, basic and instrumental activities of daily living, cognitive function, depression, nutritional status, polypharmacy, geriatric syndromes, pain, and emotional distress), anthropometric characteristics, frailty status, physical fitness, physical activity, cognitive function, quality of life, fatigue, and nutritional status. Study assessments also include analysis of inflammatory, endocrine, and nutritional mediators in serum and plasma as potential frailty biomarkers at mRNA and protein levels and multiparametric flow cytometric analysis to measure immunosenescence markers on T and NK cells. Discussion This study seeks to extend our knowledge on exercise interventions during systemic anticancer treatment in patients over 70 years of age. Results from this research will guide the management of older adults during systemic treatment in hospitals seeking to enhance the standard of care.
Thermal degradation mechanism of myricetin in palmitic acid
Dietary polyphenols are susceptible to degradation during high-temperature cooking, yet their transformation behavior in lipid-rich frying systems remains poorly understood. The thermal stability and degradation of myricetin in a simplified palmitic acid model under frying-relevant conditions was investigated. Myricetin degraded rapidly at 180 °C but remained relatively stable at 120 °C. UPLC–QTOF–MS/MS, supported by density functional theory calculations, suggested that myricetin transformation is temperature-dependent but primarily governed by lipid oxidation–derived radical and carbonyl chemistry. Vitamin C markedly delayed myricetin degradation by suppressing radical initiation and limiting o-quinone–centered downstream reactions. These findings highlight lipid oxidation as a key determinant of polyphenol stability during frying and clarify the protective role of antioxidants in lipid-rich thermal processing systems.
Micro-recovery dosing in elite sport: an initiative-taking, organization-wide recovery framework
Recovery in elite professional sport is now widely understood as a multidimensional process spanning physiological, psychological, cognitive, and social domains, yet recovery models applied in professional clubs and sporting organisations have been slow to reflect this. Most remain reactive and athlete-centred, leaving the full complexity of competitive fatigue inadequately addressed and the equivalent recovery demands on coaching and performance staff entirely unrecognised. No structural framework currently places initiative-taking, calendar-embedded multidimensional recovery as a deliberate component of seasonal planning. Main body. We introduce micro-recovery dosing (MRD): proactively planned, pre-scheduled 36-hour multidimensional recovery periods embedded within the competitive season as a proposed starting point at five to eight instances per year, extending to all members of the high-performance organisation. Drawing on neuromuscular recovery kinetics, autonomic science, sleep architecture research, occupational recovery psychology, and complex systems theory, we construct a convergent theoretical rationale for the proposed parameters and distinguish MRD from related constructs. We present a role-differentiated implementation framework with supporting evidence and a discussion of emerging AI-driven monitoring tools. Conclusions. Taken together, the four-domain convergent rationale and the complex systems perspective provide a theoretically coherent basis for MRD’s proposed structure. We argue for a specific organisational transformation: the shift from recovery as an incidental gap between performance demands to an intentionally designed recovery architecture that encompasses athletes and staff alike. The article identifies the active ingredient problem as the central scientific challenge and sets out a structured empirical agenda.
Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice
Cognitive decline and neurodegenerative diseases are progressive, multifactorial conditions that may begin years before overt clinical diagnosis and reflect interactions among biological vulnerability, modifiable exposures, environmental determinants, and reduced brain resilience. This structured narrative review examines how artificial intelligence (AI) and machine learning (ML) can integrate clinical, biological, behavioral, and digital data to support earlier, personalized, and clinically actionable strategies for preserving cognitive health. Drawing on evidence from aging neuroscience, biomarker research, digital medicine, lifestyle prevention, natural-product pharmacology, and translational AI, we propose an AI-enabled framework for precision prevention and early management of cognitive decline. Within this framework, AI may support multimodal data integration, individualized risk prediction, digital phenotyping, biomarker-based stratification, intervention selection, natural-compound prioritization, and longitudinal monitoring. Lifestyle interventions and natural medicines are considered complementary components of personalized care whose value depends on biological plausibility, standardization, target engagement, and measurable cognitive or biomarker effects. However, translation from benchmark datasets to clinical practice remains limited by insufficient prospective and external validation, poor interpretability, dataset bias, limited generalizability, inadequate calibration and clinical-utility assessment, and incomplete integration into real-world workflows. Overall, AI may provide the integrative architecture needed to combine multimodal biomarkers, modifiable risk profiles, lifestyle interventions, and natural-product pharmacology within dynamic, person-centered precision-prevention pathways. Its clinical value will depend on transparent reporting, representative datasets, prospective evaluation, and demonstrable improvement in clinical decisions and patient outcomes.
Framework to Detect Adulterated Honey Using Hyperspectral Imaging and Machine Learning
Honey is a natural product renowned for its nutritional and medicinal properties. However, the increasing market demand in Pakistan has led to frequent instances of adulterated and fraudulent honey. Conventional detection methods are widely used but are often ineffective in accurately identifying adulteration. This study proposes a novel approach to detecting adulteration in honey, specifically the mixing of sugar, using machine learning (ML) techniques in conjunction with hyperspectral imaging (HSI). By leveraging HSI, we capture distinct spectral features of both pure and adulterated honey. These spectral features are processed and analyzed using ML algorithms trained on a dataset comprising pure and mixed honey samples. Experiments involve binary, as well as, multiclass (5 classes) honey samples with different levels of adulteration. In addition, data balancing is also carried out using synthetic minority oversampling technique (SMOTE). The results prove the proposed approach to be efficient in detecting adulteration across various honey varieties, with linear regression achieved 99.9% while support vector machine and multilayer perceptron obtaining a 98.7% accuracy for binary class. For the multiple classes of honey, the multilayer perceptron shows a 96.67% accuracy outperforming the remaining ML and deep learning models. Among the deep learning models, the convolutional neural network (CNN) achieved the best performance with an accuracy of 94.67% after applying the SMOTE × 3 (three times of over samples of original samples of each class) data balancing strategy, whereas recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models showed comparatively lower performance.