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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.
SPE-ECG: An Ontology-Guided Knowledge Representation Framework for Enhancing LLM-Based Code Generation
Large Language Models (LLMs) can translate natural-language instructions into code, but when the input is unstructured clinical protocol text, the generated code may omit doses, monitoring intervals, or safety thresholds that the text states. Such omissions matter because they can propagate into software that supports clinical monitoring. We propose SPE-ECG (Structured Prompt Engineering for Enhanced Code Generation), a transparent, rule-based pipeline that converts unstructured domain text into a JSON prompt using text cleaning, part-of-speech tagging, bigram matching, and hand-built domain dictionaries, without fine-tuning any model. In a case study of 43 rheumatoid arthritis DMARD monitoring protocols, we compared raw and structured prompts on three LLMs (GPT-3.5, LLaMA 2, and Gemma), scoring the generated Python code using version 3.15.1 with BLEU against the raw-prompt code of the other two models. Mean BLEU rose for all three models (relative gains of 55.1%, 32.5%, and 19.9%); the paired differences were significant for GPT-3.5 and LLaMA 2 but not, after correction for multiple comparisons, for Gemma. These results suggest that lightweight input structuring can change how closely different models’ outputs agree, and that it could serve as a low-cost, auditable preparation step for protocol-to-code tools built on free or open models. BLEU does not measure correctness, no ablation or state-of-the-art baseline was run, and clinical validation is required before any use.
Hybrid deep learning and CNN-Isotonic Regression framework for accurate global mean sea level rise prediction using multi-source atmospheric and oceanic data
Keeping track of Global Mean Sea Level (GMSL) over long periods is important for assessing coastal risk. However, deep learning models may produce predictions that exhibit short-term increases and decreases, potentially conflicting with expected long-term physical behavior. In this study, we propose a combined Convolutional Neural Network and Isotonic Regression framework, termed CNN-ISO, for modeling and forecasting annual GMSL changes over the period 1880–2024. The framework uses 11 predictors representing atmospheric, oceanic, and cryospheric conditions. To provide longer temporal sequences, the original 144 annual observations are linearly interpolated into 1728 monthly observations. Model evaluation follows a strict chronological split, with training data from 1880–1979, validation data from 1980–1999, and test data from 2000–2024. All preprocessing transformations and the isotonic mapping are fitted exclusively on the training set to prevent information leakage from later periods. On the unseen test period, CNN-ISO achieves R 2 = 0.997 with a 95% confidence interval of [ 0.995,0.999 ] , together with an MAE of 0.56 mm and an RMSE of 0.72 mm. For comparison, linear regression achieves R 2 = 0.982 . Although the predictive improvement over linear regression is relatively modest, CNN-ISO provides an additional constraint by producing monotonic out-of-sample projections, which is consistent with the assumed long-term behavior of GMSL. Under statistical trend extrapolation to 2050, CNN-ISO estimates a GMSL increase of 184.10 mm relative to the baseline. However, estimates vary substantially across models, with linear regression and LSTM producing 116.60 mm and 299.19 mm, respectively. These results should therefore be interpreted as trend-based statistical extrapolations rather than comprehensive climate projections, as the framework does not explicitly model ice-sheet dynamics, changes in ocean circulation, or future emission pathways. Nevertheless, the proposed approach provides a transparent framework for examining model uncertainty and may support early-stage coastal adaptation planning.
Development and validation of the driving self-esteem questionnaire
Global self-esteem has proved to be a strong predictor of several outcomes, including anger and aggressive behaviour on and off the road. However, different theoretical approaches suggest the relevance of identifying and assessing context-specific forms of self-esteem. The present study aimed to develop and validate a self-report measure of driving self-esteem (DSEQ) in a sample of Spanish drivers (n = 530, Mage = 30.55, 67.4% women). Exploratory factor analyses using polychoric correlations and parallel analysis supported a unidimensional structure, with initial multidimensional patterns attributable to item wording effects. The final model showed adequate fit, strong unidimensionality indices (UniCo, ECV, MIREAL), and high reliability. Measurement invariance across gender was supported at configural, metric, and scalar levels. Evidence of validity based on relationships with external variables was observed, with driving self-esteem positively associated with global self-esteem and negatively related to several dimensions of driving anger. Hierarchical regression analyses indicated modest and domain-specific incremental validity, with small increases in explained variance for discourtesy and slow driving, but not for other dimensions. These findings support the DSEQ as a reliable and valid instrument and highlight the relevance of assessing self-esteem at a context-specific level in driving research.
Fine-Grained Human Activity Recognition in Domestic Settings Using Ultra-Wideband Radar: A Comparative Study of Deep Learning Models
Human activity recognition with ultra-wideband (UWB) radar offers contactless, privacy-preserving monitoring of daily living, but distinguishing fine-grained activities performed in the same posture remains difficult. We recorded nine daily activities from 30 participants with a single PulsON 410 radar (3.1–5.3 GHz); seven are performed seated in the same chair and differ only by subtle hand and arm motions. Each 15 s recording is a 120×1440 channel–impulse–response matrix, preprocessed by background subtraction, two-dimensional wavelet denoising and standardization. Six deep learning models (Transformer, CNN-LSTM, 1D-CNN, LSTM, GRU and MLP) and a soft-voting ensemble were compared with participant-independent splits (22 training, 3 validation and 5 test participants) and five-fold cross-validation. The Transformer reached the highest cross-validation accuracy (97.74±0.30% ). On the held-out participants, the Transformer (97.16%), 1D-CNN (96.93%) and GRU (96.45%) performed similarly, ahead of the CNN-LSTM, LSTM and MLP (87.47%), and a three-model ensemble reached 98.35%. Two additional participant splits and a retrained re-run confirmed that the MLP is clearly the weakest model, but the order of the other models changed by several points between runs; in the re-run, an ensemble of all six models did not exceed the Transformer (97.74%), and a range–Doppler baseline reached only 50.63%. Eating versus drinking tea and mobile phone use versus reading or sitting idle were the most confused pairs. Given the small test cohort and the controlled setting, the results are indicative rather than definitive.