DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network

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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.

Artículos y libros

Julia García-García mail , Ana Rodriguez-Larrad mail , Maren Martinez de Rituerto Zeberio mail , Jenifer Gómez Mediavilla mail , Borja López-San Vicente mail , Nuria Torrego Artola mail , Izaskun Zeberio Etxetxipia mail , Irati Garmendia mail , Ainhoa Alberro mail , David Otaegui mail , Francisco Borrego Rabasco mail , María M. Caffarel mail , Kalliopi Vrotsou mail , Jon Irazusta mail , Haritz Arrieta mail , Mireia Peláez mail mireia.pelaez@uneatlantico.es, Jon Belloso mail , Laura Basterretxea mail ,

García-García

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A novel approach for disease and pests detection in potato production system based on deep learning

Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.

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Ahmed Abbas mail , Saif Ur Rehman mail , Khalid Mahmood mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Aseel Smerat mail , Imran Ashraf mail ,

Abbas

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Dietary EGCG reshapes metabolic-epigenetic interplay to induce transgenerational host defense

Introduction Parental diet is a key determinant of offspring health and immune function, in part through epigenetic regulation. Metabolic and epigenetic networks integrate nutrient sensing with chromatin dynamics to maintain cellular and organismal homeostasis. However, the mechanism by which specific dietary bioactive compounds reshape metabolic-epigenetic networks to drive transgenerational adaptive responses remains poorly understood. Objectives Here, we investigate whether and how epigallocatechin-3-gallate (EGCG), a well-characterized dietary bioactive compound, modulates heritable host defense through metabolic-epigenetic crosstalk. Methods To address both physiological relevance and mechanistic insight, we employed mouse and Drosophila melanogaster models. Parental animals were administered EGCG, and offspring were subsequently assessed for immune function upon infection with Escherichia coli, Pseudomonas aeruginosa, or Staphylococcus aureus. By integrating transcriptomics, metabolite analysis, and isotopic tracing, we analyzed metabolism-related pathways and constructed a dynamic network linking metabolic changes to epigenetic modifications in Drosophila. Results In mice, EGCG administration led to a decrease in Escherichia coli burden across multiple tissues in paternal and male offspring in a sex-specific manner, accompanied by metabolic and pro-inflammatory factor changes. In Drosophila melanogaster, early-life EGCG exposure increased survival upon Pseudomonas aeruginosa or Staphylococcus aureus infection and persisted for two subsequent generations. Mechanistically, EGCG reduced intestinal amino acids, thereby moderately inducing activation of activating transcription factor 4 (ATF4), which in turn enhanced maternal glycolysis and immune adaptation. Tyrosine supplementation abolished the enhanced host defense and metabolic changes. Furthermore, ATF4-induced activation of glycolysis promoted ovarian lactate production, serving as a substrate for increased global H3K27 acetylation in the offspring. Conclusion Together, these findings suggest that dietary bioactive compounds modulate metabolic and gene regulatory processes, with functional evidence supporting a role for amino acid metabolism and lactate in linking metabolic remodeling to enhanced resistance to infection in the offspring. This work provides mechanistic insight into how diet can shape heritable immune function through metabolic-epigenetic interplay.

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Wenqi Huang mail , Shiye Lin mail , Xuanyu Zheng mail , Mohamed A. Farag mail , Thomas Efferth mail , Jesus Simal-Gandara mail , Zimiao Chen mail , Jianbo Xiao mail , Hui Cao mail ,

Huang

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Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

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Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

Butt

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Securing internet of things devices using a hybrid approach

With increased Internet of Things (IoT) devices, complexity and protection are more challenging. Lightweight cryptographic algorithms are secure and suitable for limited-resource environments; however, their hash functions provide encrypted data but not integrity. Strong security features are available, but setup is difficult and expensive. Network security mechanisms increase power consumption and latency. As IoT networks grow, managing cryptographic keys and securely authenticating large numbers of devices become complex tasks. Efficient key management strategies are required to ensure the scalability required. Existing state-of-the-art solutions lack standardization, scalability, complex and costly. Thus, this research proposes a secure solution for IoT resource-constrained devices, combining strong data integrity and lightweight encryption, and is thus named a hybrid. This hybrid approach integrates SHA-512 and the present cipher in our proposed approach and thus ensuring higher security than state-of-the-art models. This intelligent combination not only enhances the algorithm’s resistance against cryptographic attacks but also improves its processing speed. The proposed approach is used to reduce the processing time for encryption in the IoT platform and to preserve the trade-off between security and efficiency. In terms of memory use, execution time, and precision, the proposed approach is compared with recent state-of-the-art research. The experimental results indicate that our approach is efficient using the avalanche, authentication success rate, collision events, and execution time. The efficiency is 53% to 65%, and the avalanche effect indicates sensitivity to input variations, suggesting moderate-to-considerable reactivity to small data changes. The experimental tests conducted across 10,000 and 80,000 runs reveal no collisions and found that the proposed approach is resilient in managing unique IDs. Moreover, our approach performs consistently, with an average execution time of 0.088246 s, ranging from 0.075954 to 0.094583 s. Finally, our approach provides a practical and scalable solution for securing IoT devices in resource-constrained environments, addressing practical problems for IoT devices.

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R. Sherine Jenny mail , N. Sugirtham mail , B. Thiyaneswaran mail , S. Kumarganesh mail , Martin Sagayam K. mail , Syed Immamul Ansarullah mail , Farhan Amin mail , Isabel de la Torre Díez mail , Carlos Manuel Osorio García mail carlos.osorio@uneatlantico.es, Alina Eugenia Pascual Barrera mail alina.pascual@unini.edu.mx,

Jenny