Search Results (949)

Background: Clinical Learning Environments (CLEs) are essential to nursing education as a platform for students to develop professional identity; consolidate knowledge with clinical practice; and to gain cognitive, communication, and psychomotor skills. Experience in CLEs significantly impacts nursing students’ satisfaction with education and graduate career preferences. The Clinical Learning Environment, Supervision and Nurse Teacher scale (CLES+T) is widely used to measure the quality of professional experience placements (PEPs), but it has limited evidence of psychometric performance in rural and regional Australian contexts. Aim: To assess the psychometric properties of the CLES+T scale in the Australian context of rural and regional undergraduate nursing PEPs. Methods: A cross-sectional observational study of a convenience sample of 165 undergraduate nursing students from regional Victoria, Australia, who undertook PEPs between January and June 2020. Participants completed the CLES+T scale post-PEP. Statistical analyses included a test of survey tool reliability using Cronbach’s alpha and exploratory factor analysis to investigate instrument dimensionality and validity. Results: The CLES+T scale displayed adequate validity and reliability levels and demonstrated internal consistency similar to previous studies. The most important factor in the CLE was revealed as “pedagogy atmosphere and the content of supervisory relationship” followed by “role of the nurse educator”. Conclusions: The CLES+T shows adequate psychometric properties as a valid tool for use with undergraduate nursing students undertaking PEPs in Australian regional, rural, and remote settings. Full article

Background and Objectives: There is a worldwide increase in the demand for gender-affirming surgical treatments among transgender and gender-diverse (TGD) adults and adolescents. In Poland, transgender people generally lack trust in healthcare providers, which makes it more difficult for them to begin their transition process. This patient population is not well understood by many of the specialists who may potentially be involved in their care, in some way, reinforcing their concerns. The aim of this study is to present the sociodemographic characteristics of a group of female-to-male transgender patients who were admitted to a privately based plastic surgery center to undergo chest wall reconstruction. Materials and Methods: This study comprises a statistical analysis of data retrospectively obtained from the medical records of 100 patients from across the country undergoing female-to-male transition, who were operated on between 2021 and 2025 at a specialized private clinic in Poland. All individuals had already started gender-affirming medical treatment with testosterone at the time of first consultation. Results: The results show a trend toward a decreasing age at the time of the decision to undergo gender-affirming surgery. In the study group, 100% of patients were already undergoing hormone therapy. In our group of transgender individuals, we did not observe a correlation between cultural or social background, religion, and gender dysphoria. It is encouraging that more than half of the patients reported no longer needing psychiatric support, and that those who were still under specialist supervision stated that they experienced a significant improvement in their overall well-being. Conclusions: The rising demand for transgender healthcare highlights the need for studying and analyzing this group of patients in order to provide the best patient-centered care throughout the gender transition process by all specialists involved. Gender-affirming mastectomy, when combined with testosterone therapy, has a positive mental health impact on transgender individuals. Full article

Accurate classification of focal liver lesions (FLLs) is crucial for reliable clinical decision-making. Inspired by contrastive vision-language models such as CLIP and MedCLIP, we propose Liver-VLM for FLLs classification, trained on a dedicated multi-phase 2D CT dataset. Liver-VLM aligns multi-phase CT image representations with class-specific textual descriptions by calculating their similarity under a cross-entropy loss. Furthermore, we design tailored, enriched textual prompts to stabilize optimization and enable robust classification even with limited labeled data. Additionally, self-supervised pretraining and data augmentation strategies are incorporated to further improve classification performance. Experimental results on an in-house MPCT-FLLs dataset demonstrate that Liver-VLM consistently outperforms existing VLMs, achieving an accuracy of 85.63 ± 3.18% and an AUC of 0.94 ± 0.01. Our findings highlight the efficacy of self-supervised learning and task-specific augmentation in overcoming data scarcity and distributional biases in medical image analysis. Full article

The use of clinically important antibiotics in U.S. poultry production has decreased drastically over the past decade. They can only be used to treat diseases under the supervision of a veterinarian. Reducing antibiotic use, even for disease treatment, can improve the long-term sustainability of the industry. In the current study, we examined the effect of supplementation of a low dose of trans-cinnamaldehyde (TC; 0.03%), a GRAS-status plant-derived compound, with or without oxytetracycline (OTC; 16 μg/mL), an anti-30S ribosomal subunit targeting antibiotic, on the multidrug-resistant (MDR) S. Heidelberg (SH) in turkey poults. Two independent experiments were conducted (N = 96). In each experiment, 48, straight-run, day-old, commercial Hybrid Converter turkey poults were randomly assigned to 6 treatments of 8 birds each: Negative Control [NC; −SH, −TC, −OTC, −0.06% Miglyol (MIG, emulsifier for TC in water)], Positive Control (PC; +SH, −TC, −OTC, −MIG), MIG Control (MIG; +SH, −TC, −OTC, +MIG), TC Group (TC; +SH, +TC, −OTC, +MIG), OTC group (OTC; +SH, −TC, +OTC, −MIG), and TC+OTC group (TC+OTC; +SH, +TC, +OTC, +MIG). OTC was supplemented from day 1 through drinking water throughout the experiment. The birds in the TC and TC+OTC groups were supplemented with TC in their drinking water for 7 days post-challenge. All birds were challenged on day 7 with 6 log₁₀ CFU of SH/bird via crop gavage. On day 14, all birds were euthanized to collect the cecum, liver, and spleen for pathogen recovery. TC at 0.03% emulsified in MIG was highly effective in reducing MDR SH colonization in turkey poults (p < 0.05) compared to the SH control (>4.5 log₁₀ CFU/g reduction) on day 14. The OTC group reduced the pathogen load by 2.5 log₁₀ CFU/g by day 14. TC enhanced the effect of OTC, reducing pathogen load by ~3.9 log₁₀ CFU/g compared to the SH control after 7 days. TC significantly reduced SH invasion into the liver and spleen compared with the SH control on day 14. The results of the study indicate that TC at 0.03% can augment OTC at 16 μg/mL for the treatment of MDR SH infection in poults and could be an industry-sustainable strategy. Full article

The Timed Up and Go (TUG) test is a widely used clinical tool for assessing mobility and fall risk in older adults and individuals with neurological or musculoskeletal conditions. While it provides a quick measure of functional independence, traditional stopwatch-based timing offers only a single completion time and fails to reveal which movement phases contribute to impairment. This study presents a smartphone-based system that automatically segments the TUG test into distinct phases, delivering objective and low-cost biomarkers of lower-limb performance. This approach enables clinicians to identify phase-specific impairments in populations such as individuals with Parkinson’s disease, and older adults, supporting precise diagnosis, personalized rehabilitation, and continuous monitoring of mobility decline and neuroplastic recovery. Our method combines adaptive preprocessing of accelerometer and gyroscope signals with supervised learning models (Random Forest, Support Vector Machine (SVM), and XGBoost) using statistical features to achieve continuous phase detection and maintain robustness against slow or irregular gait, accommodating individual variability. A threshold-based turn detection strategy captures both sharp and gradual rotations. Validation against video ground truth using group K-fold cross-validation demonstrated strong and consistent performance: start and end points were detected in 100% of trials. The mean absolute error for total time was 0.42 s (95% CI: 0.36–0.48 s). The average error across phases (stand, walk, turn) was less than 0.35 s, and macro F₁ scores exceeded 0.85 for all models, with the SVM achieving the highest score of 0.882. Combining accelerometer and gyroscope features improved macro F1 by up to 12%. Statistical tests (McNemar, Bowker) confirmed significant differences between models, and calibration metrics indicated reliable probabilistic outputs (ROC-AUC > 0.96, Brier score < 0.08). These findings show that a single smartphone can deliver accurate, interpretable, and phase-aware TUG analysis without complex multi-sensor setups, enabling practical and scalable mobility assessment for clinical use. Full article

Surgical Phase Recognition (SPR) enables real-time, context-aware assistance during surgery, but its use remains limited by the cost and effort of dense video annotation. This study presents a Semi-Supervised Deep Learning framework for SPR in endoscopic pituitary surgery, aiming to reduce annotation requirements while maintaining performance. A Timestamp Supervision strategy is employed, where only one or two representative frames per phase are labeled. These labels are then propagated, creating pseudo-labels for unlabeled frames using an Uncertainty-Aware Temporal Diffusion (UATD) approach, based on confidence and temporal consistency. Multiple spatial and temporal architectures are evaluated on the PituPhase–SurgeryAI dataset, the largest publicly available collection of endoscopic pituitary surgeries to date, which includes an outside-the-body phase. Despite using less than 3% of the annotated data, the proposed method achieves an F1-score of 0.60 [0.55–0.65], demonstrating competitive performance against previous Supervised approaches in the same context. Removing the recurrent outside-the-body phase reduces misclassification and improves temporal consistency. These results demonstrate that uncertainty-guided Semi-Supervision is a scalable and clinically viable alternative to fully Supervised Learning for surgical workflow analysis. Full article

Background: Asthma is a global public health challenge, and although guidelines recommend self-management programs, their implementation is limited. Digital health tools, such as mobile applications and web platforms, have emerged as promising solutions to improve self-management, adherence, and monitoring. However, the evidence on their effectiveness is heterogeneous and often presents methodological limitations. This systematic review and meta-analysis aimed to synthesize the current evidence on the efficacy of these tools in asthma management. Methods: A systematic search was conducted in six databases for randomized controlled trials (RCTs) published between 2010 and 2025. Twenty-six RCTs that evaluated digital interventions in pediatric and adult patients with asthma were included. The outcomes of interest were asthma control, pulmonary function, symptom-free days, and health-related quality of life (HRQoL). A meta-analysis was performed using a random-effects model. Results: Digital tools showed a statistically significant improvement in pulmonary function, specifically in FEV₁ (SMD: 1.53; p = 0.007) and the FEV₁/FVC ratio (SMD: 1.20; p = 0.02). No significant effects were found on asthma control, PEF, symptom-free days, or HRQoL in the overall analysis. However, subgroup analyses revealed that remote supervision significantly improved asthma control, and mobile applications improved HRQoL. Conclusions: Digital health interventions are a promising complement for asthma management, notably improving pulmonary function. Their effectiveness on other clinical outcomes appears to depend on factors such as the supervision mode and the type of tool. More standardized research is needed to confirm these findings. Full article

Classifying the severity of intensive-care-unit-acquired muscle atrophy (ICU-AW) is essential for early prognosis and individualized neurorehabilitation, improving functional outcomes in survivors of critical illness. This study evaluated and compared advanced machine learning (ML) algorithms for classifying neuromuscular atrophy in neurocritical patients. Clinical, biochemical, anthropometric, and morphometric data from 198 neuro-ICU patients were retrospectively analyzed. Six supervised ML models—Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), TPOT AutoML, AdaBoost, and Multinomial Logistic Regression—were trained using stratified cross-validation, synthetic oversampling, and hyperparameter optimization. Among the most outstanding models, SVM achieved the best performance (accuracy = 93%, ROC-AUC = 0.95), followed by MLP (accuracy = 82.8%, ROC-AUC = 0.93) and XGBoost (accuracy = 80%, ROC-AUC = 0.94). Stability analyses across random seeds confirmed the robustness of SVM and TPOT, with the highest median AUPRC (>0.90). Explainable AI methods (LIME and SHAP) identified BMI, serum albumin, and body surface area as the most influential variables, showing physiologically consistent patterns associated with a classification of muscle loss. Full article

Introduction: Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) is associated with changes in the brain’s pain processing. This is often due to problems with the body’s natural way of handling the pain management system. Exercise therapy, such as motor control and spinal stabilization, can help reduce pain and disability. However, exercise alone may not be sufficient. Approaches that consider both body mechanics and brain function are gaining popularity. Since brain changes play a role in muscle and bone problems, noninvasive brain stimulation (NIBS) is considered a helpful adjunctive treatment. Studies have shown that NIBS may help people with spinal pain and mood disorders. The aim of this study is to assess the impact of combining tDCS targeting the dorsolateral prefrontal cortex with spinal motor control exercises in patients diagnosed with PSPS-T2. This investigation is based on the hypothesis that such a combined intervention could result in a more significant reduction in disability. Methods/Materials: This randomized controlled trial (RCT) is structured as a double-blind, comparative, longitudinal design in accordance with the CONSORT guidelines. This RCT has been registered at ClinicalTrials.gov (NCT06969456). Forty-two participants diagnosed with PSPS-T2 will be randomized in a 1:1 ratio into two groups: tDCS + rehabilitation (E_tDCS) or sham tDCS + rehabilitation (E_SHAM). The intervention will use tDCS to deliver low-intensity direct current to modulate cortical excitability. The intervention will consist of 24 supervised sessions (2 per week, 60 min each) over 12 weeks. Neuromodulation and exercise protocols will be adapted to the intervention phases based on previous research. The sample size has been calculated using GPower^®, assuming an effect size of 0.81, α = 0.05, power = 0.95, and a 40% dropout rate. Data will be collected from October 2025 to January 2027. Impact Statement: This study integrates neurophysiological modulation via tDCS with targeted exercise therapy, presenting an innovative approach to enhance pain modulation, functional recovery, and cortical reorganization in patients with PSPT-2. This approach has the potential to inform future evidence-based strategies for neurorehabilitation and pain management. Full article

Background/Objectives: Chagas disease is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi, which currently affects around 8 million people worldwide. The therapeutic arsenal against T. cruzi is so far limited to only two approved drugs, benznidazole and nifurtimox, that have considerable side effects and limited efficacy in the chronic stage of the disease. Here, we have resorted to supervised phenotypic machine learning models to explore drug repurposing opportunities and identify potential new therapeutic solutions for Chagas disease. Methods: More than 100,000 bioactivity data points were retrieved from ChEMBL and carefully curated according to the data-centric machine learning paradigm. After curation, two datasets comprising 344 compounds tested against T. cruzi Y strain trypomastigotes and 785 compounds tested against Tulahuen strain amastigotes were obtained and used to infer ensemble learning models with excellent average and early enrichment metrics in retrospective screening experiments (AUROC > 0.96 and EF_0.01 > 58). A prospective screening campaign was then performed on DrugBank and the Drug Repurposing Hub databases, submitting eight in silico hits for experimental confirmation. Results: Six of the in silico hits confirmed their predicted trypanocidal effects. Conclusions: We have built portable meta-classifiers capable of identifying small molecules with trypanocidal activity against amastigotes, the clinically most relevant stage of T. cruzi. The predictive ability of this meta-classifier was experimentally validated. Full article

Wearable ECG monitoring devices have become indispensable in personalized healthcare. However, dynamic signal acquisition during daily activities often introduces transient noise, which complicates signal classification and denoising, and may compromise diagnostic reliability. To address this challenge, this study proposes an ECG preprocessing framework based on multi-task learning, in which a fine-grained noise localization task is introduced to guide and assist both ECG signal quality assessment and denoising. Built upon a Transformer backbone and optimized with three task-specific loss functions, the proposed model leveraged weak supervision and pathological ECG data to learn robust noise-invariant representations. This design incorporates intra-class awareness, enabling the model to overcome various noise within the same quality category and to perform adaptive denoising beyond conventional inter-class-based approaches. Extensive experiments demonstrated state-of-the-art performance in both denoising and quality assessment, with weighted average F1-scores ranging from 95.72% to 98.49% and classification accuracy exceeding 95.68%. Moreover, under extremely severe noise conditions, the signal-to-noise ratio (SNR) is improved from −1.95 ± 3.83 dB to 12.20 ± 2.51 dB while preserving waveform fidelity. After pruning and quantization, the model could be effectively compressed, thereby enhancing its suitability for real-time deployment in edge computing scenarios. Overall, the proposed method not only preserved diagnostically important ECG waveforms and provided interpretable noise localization but also offers an efficient and clinically relevant solution for large-scale, real-time ECG monitoring. Full article

Background: Knee osteoarthritis (OA) is a prevalent musculoskeletal condition associated with pain, functional limitation, and reduced quality of life. Telerehabilitation has emerged as a scalable intervention, yet many platforms lack neuromuscular feedback or objective-monitoring capabilities. The KneE-PAD system uniquely integrates electromyographic and inertial sensing to provide personalized feedback and remote performance tracking. Objective: To evaluate the clinical effectiveness of a sensor-augmented telerehabilitation system (KneE-PAD) compared to conventional face-to-face physiotherapy in older adults with mild-to-moderate knee OA. Methods: In this single-blind randomized controlled trial, 42 older adults (mean age 68.4 ± 5.7 years) were randomly assigned to either KneE-PAD telerehabilitation or conventional physiotherapy for eight weeks. KneE-PAD sessions incorporated real-time electromyographic and motion feedback, while physiotherapists remotely supervised training. Assessments were performed at baseline, post-intervention, and 12-week follow-up. Primary outcomes included quadriceps strength, neuromuscular activation, and WOMAC scores. Secondary outcomes covered functional mobility, psychological distress, self-efficacy, and fear of movement. Results: The telerehabilitation group demonstrated notable improvements in neuromuscular activation, quadriceps strength, and functional capacity, all exceeding clinically meaningful thresholds. Functional mobility and pain outcomes showed substantial gains compared with the control group, while psychological indicators (self-efficacy and depressive symptoms) exhibited modest but positive trends. Between-group comparisons consistently favored KneE-PAD, with effects maintained at the 12-week follow-up, confirming both clinical and functional robustness. Conclusions: Sensor-augmented telerehabilitation using the KneE-PAD platform appears to be a feasible and potentially effective alternative to conventional physiotherapy for knee OA. By combining real-time feedback, motor learning reinforcement, and remote monitoring, the system may enhance neuromuscular and functional recovery. These findings should be confirmed in larger and longer-term trials. Trial Registration: ClinicalTrials.gov: NCT06416332. Full article

Objective: The primary objective of this pilot randomized clinical trial was to determine the effect of adding a supervised resistance exercise programme to a home-based physical activity plan on health-related quality of life in patients with colorectal cancer undergoing active treatment. The secondary objectives were to evaluate its effects on anxiety, depression, sleep quality, and inflammatory parameters. Methods: This is a pilot randomized clinical trial with parallel groups. Patients with CRC were recruited through the Oncology Department at the Salamanca University Health Care Complex in Spain. They were randomly allocated to receive either a home-based physical activity plus a supervised resistance training programme, or the home-based physical activity plan only. The primary outcome was health-related quality of life measures and the secondary outcomes included anxiety, depression and sleep quality evaluations. The supervised training lasted 8 weeks for each patient. Results: A total of 40 patients were recruited, 20 for each group. Adding a supervised resistance exercise programme to the home-based activity plan improved symptoms related to quality of life, such as fatigue (p = 0.040) and constipation (p = 0.015). However, no significant effect was found with regard to other health-related quality of life, anxiety, depression or sleep variables. Conclusions: Fatigue and constipation in patients with CRC receiving chemo- and/or immunotherapy may benefit from the introduction of supervised resistance exercise training programmes. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, among which vocal impairment is one of the earliest and most prevalent. In recent years, voice analysis supported by machine learning (ML) and deep learning (DL) has emerged as a promising non-invasive method for early PD detection. We conducted a systematic review searching PubMed, Scopus, IEEE Xplore, and Web of Science databases for studies published between 2020 and September 2025. A total of 69 studies met the inclusion criteria and were analyzed in terms of dataset characteristics, speech tasks, feature extraction techniques, model architectures, validation strategies, and performance outcomes. Classical ML models such as Support Vector Machines (SVMs) and Random Forests (RFs) achieved high accuracy on small, homogeneous datasets, while DL architectures, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based foundation models, demonstrated greater robustness and scalability across languages and recording conditions. Despite these advances, persistent challenges such as dataset heterogeneity, class imbalance, and inconsistent validation practices continue to hinder reproducibility and clinical translation. Overall, the field is transitioning from handcrafted feature-based pipelines toward self-supervised, representation-learning frameworks that promise improved generalizability. Future progress will depend on the development of large, multilingual, and openly accessible datasets, standardized evaluation protocols, and interpretable AI frameworks to ensure clinically reliable and equitable voice-based PD diagnostics. Full article

Background: Cancer patients often use natural health products (NHPs) during chemotherapy without medical supervision. We have previously described the clinical cases of two patients taking capecitabine in combination with folate supplements who suffered from severe diarrhoea and hand-foot syndrome, emphasising that the combination of NHPs with chemotherapeutic agents such as fluoropyrimidines (FPs) can lead to life-threatening events. Although the potential harmful interaction between folate supplements and capecitabine is reported in the summary of product characteristics for this FP, it remains unclear, and evidence regarding interactions with other NHPs is even more limited. Objectives/Methods: This narrative review aimed to provide an update on the literature regarding the effects of combining NHPs and FPs, describing the results of randomised clinical trials and observational studies to provide a critical analysis of the factors influencing the clinical outcomes of cancer patients following this therapeutic approach. Results: Herbal supplements belonging to traditional Chinese medicine and other NHPs, including polyunsaturated fatty acids and probiotics, may reduce the incidence and severity of gastrointestinal, haematological, and skin toxicities related to FPs. In addition to potential safety benefits, NHPs may improve the efficacy of FP-based therapy. Folate supplements appear to improve efficacy outcomes, such as disease-free survival and overall survival, but have also been associated with serious FP-related adverse events. However, the results are mixed, partly because they are influenced by the patient’s genetic background. Conclusions: Overall, the available data are inconclusive and do not support the introduction of natural products as complementary therapy in cancer patients undergoing FP-based chemotherapy, highlighting the need for further investigation. Full article