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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.
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.
Parental Decision-Related Factors Are Associated with Discretionary Ultra-Processed Food Consumption Among Children and Adolescents Living in the Mediterranean Area
Background/Objectives: Nutrition during childhood and adolescence is a key determinant of long-term health, influencing metabolic homeostasis, neurocognitive development, and immune system maturation. Globalization and technological advances have reshaped food production and consumption, increasing the availability of ultra-processed foods (UPF) of low nutritional quality. This study aimed to investigate the relationship between parental factors, namely food literacy, perceived barriers and enablers, dietary attitudes, and healthy eating behaviors, and the consumption of discretionary UPF among children and adolescents living in 5 Mediterranean countries. Methods: This cross-sectional study was based on a survey completed by 2011 parents of children and adolescents aged 6–17 years from 5 Mediterranean countries, who reported on their children’s dietary and lifestyle habits. Adherence to the Mediterranean diet was assessed through the KIDMED index. Parental food literacy was measured using the Short Food Literacy Questionnaire (SFLQ). Perceived barriers and enablers were assessed based on the Theory of Planned Behavior, and parents’ attitudes toward their child’s diet were evaluated using the Healthy-Eating Attitudes Questionnaire (HEAQ). Finally, the Theory of Internet Use Related to Health (TIUH) questionnaire was used to assess parents’ tendencies related to health information use online. Results: Higher perceived barriers and enablers were significantly associated with lower discretionary UPF consumption across all models. Parental food literacy (SFLQ) showed a positive association with discretionary UPF consumption, remaining significant in the fully adjusted model, although with reduced magnitude. Healthy-eating attitudes (HEAQ) were initially positively associated with discretionary UPF intake but lost statistical significance after full adjustment. Regarding health-related internet use (TIUH), the Health Information dimension showed a strong positive association with discretionary UPF consumption, while other dimensions (Consciousness and Beliefs) showed inconsistent and non-significant associations in the fully adjusted model. Conclusions: Children’s consumption of discretionary UPF is shaped by several interrelated factors, such as family environment, eating patterns, and parents’ perceptions, rather than solely by knowledge or attitudes.
Cryptocurrency Market Trends: A Machine Learning-Driven Time Series Forecasting with Twitter Sentiment Integration
Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE, MAE, MAPE, R 2 , and Directional Accuracy (DA) across five random seeds, with paired Wilcoxon significance tests. XGBoost-LSTM achieves the best performance (RMSE = 81.547, R 2 = 0.9254, DA = 80.0%), outperforming all nine literature baselines. Removing Twitter sentiment degrades DA by 14.3 percentage points ( p < 0.01 ), confirming that social media signals carry independent predictive information. Hybrid architectures consistently outperform single-model baselines; XGBoost-LSTM offers the best accuracy-to-compute ratio. VADER-enriched Twitter sentiment is a significant predictor beyond price history. Limitations include reliance on a single sentiment platform and a training window that predates several structural market events
Valorization of Purple Prickly Pear Peel By‐Products: Antiproliferative and Pro‐Apoptotic Effects on Human Colorectal Cancer Cells HCT116
Opuntia ficus-indica peel is known to possess antioxidant, anti-inflammatory, and anticancer activities and currently is discarded or used for animal feeding. Within this context, the aim of this work was to evaluate the antiproliferative and pro-apoptotic effect of purple prickly pear peel extract (PPE) on the human colon adenocarcinoma cancer cell line (HTC116). The methanolic extract of PPE was characterized in terms of betalain and polyphenols as well as total antioxidant capacity. Cell viability, apoptosis induction, cell cycle arrest, and reactive oxygen species (ROS) production assays were performed. Important proteins and genes related to proliferation and apoptosis were determined. PPE represents a good source of bioactive compounds with a high antioxidant capacity. Cell viability was reduced gradually by PPE treatments, with lower effects in nontumorigenic cells. Compared to the control group, a significant induction of apoptosis as well as cell cycle arrest in the sub-G1 phase and ROS production was observed in PPE-treated cells. Furthermore, the treatment induced the overexpression of p53 at protein levels and upregulated the mRNA expression of pro-apoptotic BAX, CASP9, BID, and CYCS, along with the significant decrease of anti-apoptotic BCL2 gene expression. Simultaneously, cyclin D1 and CDK4 gene expression were significantly decreased, while p21 increased considerably. The treatment also induced the downregulation of Her2 and PI3K at protein levels and caused the suppression of PI3KCA and mTOR expression at gene levels. Overall, these findings suggested that PPE has potential anticancer effects against human colon adenocarcinoma progression.