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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.
Assessment Of Thermal Asymmetries In Professional Soccer Players According To Field Position
PURPOSE: Infrared Thermography is a valid and non-invasive tool to evaluate skin temperature. Analyzing changes in temperature in response to physiological processes is important to detect fatigue and risk factors for injury. The main aim of this study was to assess thermal asymmetries (TA) in the rectus femoris (RF) and biceps femoris (BF) in professional soccer players according to position.
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.
Dietary microRNAs from dairy products and their potential relevance to breast cancer: A systematic review
Background The potential role of dairy products in breast cancer development remains controversial, with studies reporting both protective and detrimental effects. MicroRNAs, small non-coding RNA molecules involved in post-transcriptional gene regulation, are increasingly recognized as important mediators in cancer biology, including breast cancer. Milk and other dairy products contain extracellular vescicle-associated microRNAs, some of which are conserved, bioactive, and capable of surviving digestion. Additionally, dairy intake may modulate the expression of endogenous microRNAs in humans. Scope and approach This review explores the landscape of dairy-related microRNAs—both exogenous microRNAs naturally present in dairy products and endogenous microRNAs influenced by dairy consumption—and examines their potential overlap with those implicated in breast cancer. A structured PubMed search identified studies addressing: (1) the most relevant microRNAs detected in dairy products, (2) human-based studies investigating microRNA modulation following dairy intake, and (3) reported associations with breast cancer across clinical and molecular contexts. Key findings and conclusions We identified a group of dairy-associated microRNAs—particularly, miR-148a-3p-, miR-21-5p, miR-30a-5p-5p, miR-125b-5p, miR-26a-5p, miR-155-5p, and miR-146a-5p—that have been linked to key processes involved in breast cancer development, including cell proliferation, apoptosis, epithelial-to-mesenchymal transition, and chemoresistance. Some of these microRNAs are also differentially expressed in mastitic milk or modulated by farming systems and stress-related conditions, highlighting the need for interdisciplinary approaches bridging food systems and human health. While causal inference is beyond the scope of this review, the widespread and sustained consumption of dairy products supports the need for further research to clarify how dairy intake may influence microRNA-mediated regulatory mechanisms and breast cancer risk in humans. The available evidence also suggests that dairy subtype, origin, and processing may be relevant when considering potential dietary implications.
Cost-sensitive random forest with adaptive threshold optimization for imbalanced financial fraud detection
Background: Financial transaction fraud detection is a challenging binary classification problem characterized by severe class imbalance, rare fraudulent cases, and asymmetric misclassification costs. In real-world banking systems, failing to detect fraudulent transactions can lead to substantial financial losses, whereas false alarms mainly increase operational review costs. Therefore, fraud detection models should not be optimized only for overall accuracy, as accuracy-oriented classifiers may favor the majority non-fraud class and fail to identify minority fraud cases effectively. To address this issue, this study proposes a cost-sensitive ML framework for financial transaction fraud detection under class imbalance and asymmetric financial risk.Methods: In this study, a cost-sensitive RF framework with adaptive probability threshold optimization is proposed for binary fraud classification. Misclassification costs are incorporated during model training through class-weighted learning, allowing the classifier to assign greater importance to minority fraud cases. In addition, instead of using the default probability threshold of 0.5, the decision threshold is optimized using validation data to minimize the expected misclassification cost and improve minority-class detection. The proposed framework was evaluated on two publicly available financial fraud datasets with different transaction characteristics: the Credit Card Fraud Detection dataset and the Financial Fraud Prediction dataset. Its performance was compared with conventional ML baselines using fraud-oriented evaluation metrics, including precision, recall, F1-score, false-negative rate, false-positive rate, and expected misclassification cost.Results: The proposed framework achieved strong and consistent performance across both datasets. On the Credit Card Fraud Detection dataset, it achieved a precision of 94.4%, recall of 85.7%, and F1-score of 89.8%. On the Financial Fraud Prediction dataset, it achieved a precision of 93.1%, a recall of 83.8%, and an F1-score of 88.2%. Compared with conventional fixed-threshold ML models, the adaptive threshold strategy reduced false-negative predictions while maintaining high precision. These results indicate that the proposed cost-sensitive framework provides a more balanced decision strategy for fraud detection under highly imbalanced transaction distributions.Conclusion: The findings demonstrate that combining class-weighted learning with adaptive threshold optimization can improve fraud detection performance under asymmetric misclassification costs. The proposed framework offers a practical and interpretable decision-support approach for banking fraud detection systems, particularly in settings where reducing undetected fraudulent transactions is more important than maximizing overall accuracy alone.