Search Results (610)

Background: Pain is a problem faced by critically ill surgical patients and has a major impact on their outcomes. Pain assessment is therefore essential for effective pain management, with a combination of pharmacological and non-pharmacological treatment. Clinical supervision, supported by models such as SafeCare, can improve professional development, safety and the quality of care in intensive care units. Objectives: This study aimed to: (1) assess current pain assessment practices in a polyvalent Intensive Care Unit (ICU) in the Porto district; (2) identify nurses’ training needs regarding the Clinical Supervision-Sensitive Indicator—Pain; and (3) evaluate the impact of clinical supervision sessions on pain assessment practices. Methods: A quantitative, quasi-experimental, cross-sectional study with a pre- and post-intervention design was conducted. Based on the SafeCare model, it included a situational diagnosis, 6 clinical supervision sessions (February 2023), and outcome evaluation via nursing record audits (November 2022 and May 2023) in 31 total critical ill patients. Pain was assessed using standardised tools, in line with institutional protocols. Data was analysed using Software Statistical Package for the Social Sciences v25.0. Results: Pain was highly prevalent in the first 24 h, decreasing during hospitalisation. Generalised acute abdominal pain predominated, with mild to moderate intensity, and was exacerbated by wound care and mobilisation/positioning. Pain management combined pharmacological and non-pharmacological treatment. There was an improvement in all the parameters of the pain indicator post-intervention. Conclusions: Despite routine assessments, gaps remained in reassessing pain post-analgesia and during invasive procedures. Targeted clinical supervision and ongoing training proved effective in improving compliance with protocols and supporting safer, more consistent pain management. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on large-scale labeled datasets. To address this, we propose Dynamic Cross-Scale Swin Transformer (DCS-ST), a robust and efficient framework tailored for histopathology image classification with scarce annotations. Specifically, DCS-ST integrates a dynamic window predictor and a cross-scale attention module to enhance multi-scale feature representation and interaction while employing a semi-supervised learning strategy based on pseudo-labeling and denoising to exploit unlabeled data effectively. This design enables the model to adaptively attend to diverse tissue structures and pathological patterns while maintaining classification stability. Extensive experiments on three public datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that DCS-ST consistently outperforms existing state-of-the-art methods across various magnifications and classification tasks, achieving superior quantitative results and reliable visual classification. Furthermore, empirical evaluations validate its strong generalization capability and practical potential for real-world weakly-supervised medical image analysis. Full article

Background: In clinical practice, visual outcomes with anti-VEGF therapy may be worse than those observed in clinical trials. In this study, we aim to investigate the long-term outcomes of neovascularization treated with intravitreal aflibercept injections (IAI) in a teaching hospital setting. Methods: This is a retrospective, single-center study including 81 nAMD patients (116 eyes), those both newly diagnosed and switched from ranibizumab. All patients had a follow-up duration of at least seven years. Treatment involved three monthly injections followed by either a pro re nata (PRN) or treat and extend regimen. Follow-up care was primarily conducted by training physicians. The primary endpoint was the change in best-corrected visual acuity (BCVA) over seven years. Secondary endpoints included central retinal thickness changes, qualitative OCT parameters, macular atrophy progression, injection frequency, and treatment adherence. Results: Among the 116 eyes, 52 (44.8%) completed the seven-year follow-up. Visual acuity improved by +2.1 letters in the overall population (+6.3 letters in treatment-naive eyes) after the loading phase but gradually declined, resulting in a loss of −12.3 letters at seven years. BCVA remained stable (a loss of fewer than 15 letters) in 57.7% of eyes. Central retinal thickness (CRT) decreased significantly during follow-up in both naive and switcher eyes. Macular atrophy occurred in 94.2% of eyes, progressing from 1.42 mm² to 8.55 mm² over seven years (p < 0.001). The mean number of injections was 4.1 ± 1.8 during the first year and 3.7 per year thereafter. Advanced age at diagnosis was a risk factor for loss to follow-up, with bilaterality being a protective factor against loss to follow-up (p < 0.05). Conclusions: This study highlights the challenges faced by a retina clinic in a teaching hospital. Suboptimal functional and anatomical outcomes in real life may derive from insufficient patient information and inconsistent monitoring, which contributes to undertreatment and affects long-term visual outcomes. It also raises concerns about supervision in a teaching hospital which needs to be improved. Full article

Background: Breast and prostate cancer represent a significant global public health burden. Among the adverse effects of oncological treatments, fatigue is one of the most prevalent, persistent, and disabling symptoms. Therapeutic exercise has been shown to be effective for its management, with supervision identified as a key factor that may enhance adherence, safety, and intensity control. This systematic review and meta-analysis aimed to compare the effects of supervised exercise programs versus usual care on cancer-related fatigue in patients with breast or prostate cancer. Methods: A systematic search (September–December 2024) was conducted in six databases (PubMed, Web of Science, Scopus, Cochrane, PEDro, Scielo), selecting RCTs from the past 10 years in English or Spanish. Studies compared supervised exercise with unsupervised exercise or usual care in stage I–III breast or prostate cancer patients within five years post-treatment. Methodological quality was assessed with the PEDro scale and risk of bias with Cochrane’s RoB 2.0. A random-effects model was used to calculate pooled effect sizes (ES, 95% CI), with heterogeneity (I²), sensitivity, subgroup, and publication bias analyses. Results: A total of 25 interventions from 19 randomized controlled trials involving over 2200 participants were included. Supervised exercise significantly reduced cancer-related fatigue compared to usual care (effect size = 0.34; 95% CI: 0.22–0.47; p < 0.001; I² = 56%). Sensitivity analyses supported the robustness of the findings. Subgroup analyses revealed greater effects in combined exercise programs, in men, and in patients with prostate cancer. No evidence of publication bias was observed. While 73.7% of studies were rated as having good methodological quality, the risk of bias was often unclear or high. Conclusions: Supervised therapeutic exercise programs are effective and safe for reducing fatigue in breast and prostate cancer survivors. These interventions should be incorporated into comprehensive care plans, with individualization based on patients’ clinical and demographic characteristics. Further research is needed to identify the most effective and sustainable strategies for different patient subgroups. Full article

Background/Objectives: Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s Disease (PD) and other movement disorders. Despite its benefits, DBS explantation occurs in 5.6% of cases, with costs exceeding USD 22,000 per implant. Traditional statistical methods have struggled to identify reliable risk factors for explantation. We hypothesized that supervised machine learning would more effectively capture complex interactions among perioperative factors, enabling the identification of novel risk factors. Methods: The Medical Informatics Operating Room Vitals and Events Repository was queried for patients with DBS, adequate clinical data, and at least two years of follow-up (n = 38). Fisher’s exact test assessed demographic and medical history variables. Data were analyzed using Anaconda Version 2.3.1. with pandas, numpy, sklearn, sklearn-extra, matplotlin. pyplot, and seaborn. Recursive feature elimination with cross-validation (RFECV) optimized factor selection was used. A multivariate logistic regression model was trained and evaluated using precision, recall, F1-score, and area under the curve (AUC). Results: Fisher’s exact test identified chronic pain (p = 0.0108) and tobacco use (p = 0.0026) as risk factors. RFECV selected 24 optimal features. The logistic regression model demonstrated strong performance (precision: 0.89, recall: 0.86, F1-score: 0.86, AUC: 1.0). Significant risk factors included tobacco use (OR: 3.64; CI: 3.60–3.68), primary PD (OR: 2.01; CI: 1.99–2.02), ASA score (OR: 1.91; CI: 1.90–1.92), chronic pain (OR: 1.82; CI: 1.80–1.85), and diabetes (OR: 1.63; CI: 1.62–1.65). Conclusions: Our study suggests that supervised machine learning can identify risk factors for early DBS explantation. Larger studies are needed to validate our findings. Full article

Canada’s national surveillance shows an 11% year-over-year decline in deaths from opioid and other unregulated drug poisonings, and a 10% drop in related hospitalisations in 2024. In stark contrast, Québec, home to more than nine million residents, and Montréal, the country’s second-largest city, experienced a continued rise in suspected drug-poisoning mortality through 2024, with fentanyl or analogues detected in almost two-thirds of opioid deaths. We conducted a narrative synthesis of provincial coroner and public-health surveillance tables, Health Canada dashboards, and the 2022–2025 Québec Strategy on Psychoactive-Substance Overdose Prevention. Results indicate a 40% increase in opioid-related mortality since 2018, a parallel uptick in stimulant toxicity, and a five-fold rise in overdose reversals at Montréal supervised-consumption services during the COVID-19 pandemic recovery. We aim to summarise the key problems underlying this epidemic and offer province-specific public-health strategies while also sending a call to action for first-line clinicians and psychiatrists to integrate overdose-risk screening, take-home naloxone, and stimulant-use-disorder treatments into routine care. We further urge Québec healthcare professionals to deepen their knowledge of provincial services such as supervised-injection sites and stay up to date with the rapidly evolving substance-use-prevention literature. Québec’s divergent trajectory underscores the need for region-tailored harm-reduction investments and stronger policy-to-clinic feedback loops to reduce preventable deaths. Full article

Prolonged hospital stays separate children from their families and adversely impact the well-being of both. Children with medical complexity (CMC) often have long hospital stays and sometimes spend months to years missing their childhoods, often alone in their rooms. Caregivers of CMC must navigate many barriers to discharge during long hospital stays, which further strains the family system. In this review, we summarize the developmental vulnerabilities of chronically hospitalized CMC and propose that the hospital environment itself confers additional risk for poor neurodevelopmental outcomes. We will discuss the opportunities for pediatric post-acute care (PPAC) hospitals to create spaces where medical treatment, developmental recovery, and family integration in care can exist simultaneously. We then describe how the Care Beyond the Bedside model developed by one PPAC hospital aims to diminish the detrimental effects of prolonged hospitalization on CMC and their families by prioritizing developmental opportunity alongside medical stability. Critical components of this care model are patient and family spaces designed for community, safety training to supervise patients away from the bedside, and investment in staffing and programming to support the model. This care model acknowledges that play and healing are inextricably linked and that children develop best when they are out of bed, participating in life with their families. Full article

Background: Primary progressive aphasia (PPA) is a neurodegenerative disease characterised by progressive impairment of speech and language abilities. Intensive speech and language teletherapy combined with remotely supervised, self-administered transcranial direct current stimulation (tDCS) may be suited to remove barriers to accessing potentially effective treatments, but there is only limited evidence on the feasibility of this combined approach. Methods: This pilot case series investigated the feasibility, tolerability and preliminary efficacy of a novel telerehabilitation programme combined with home-based, self-administered tDCS for people with primary progressive aphasia (pwPPA). The intervention programme was co-developed with pwPPA and their caregivers, to reflect their priorities regarding treatment content and outcomes (i.e., naming, functional communication). Results: Two pwPPA successfully completed the telerehabilitation intervention with daily naming training and communicative-pragmatic therapy paired with tDCS, over 10 consecutive workdays. Caregivers assisted in the setup of equipment required for teletherapy and home-based tDCS. Participants successfully completed the programme with a 95% completion rate. Home-based tDCS was well tolerated. Both participants showed improvements in naming and communication, suggesting preliminary efficacy of the intervention. Conclusions: Overall, this study demonstrates the feasibility and potential benefit of a novel, easily accessible and patient-relevant telerehabilitation intervention for pwPPA, which requires confirmation in a future larger-scale exploratory trial. Full article

Background and Objective: Dysregulated tyrosine kinase signaling is a central driver of tumorigenesis, metastasis, and therapeutic resistance. While tyrosine kinase inhibitors (TKIs) have revolutionized targeted cancer treatment, identifying compounds with optimal bioactivity remains a critical bottleneck. This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. Methods: A curated dataset of 28,314 small molecules from the ChEMBL database targeting 11 tyrosine kinases was analyzed. Using Morgan fingerprints and physicochemical descriptors (e.g., molecular weight, LogP, hydrogen bonding), ten supervised models, including dANN, SVM, CatBoost, and CNN, were trained and optimized through a randomized hyperparameter search. Model performance was evaluated using F1-score, ROC–AUC, precision–recall curves, and log loss. Results: SVM achieved the highest F1-score (87.9%) and accuracy (85.1%), while dANNs yielded the lowest log loss (0.25096), indicating superior probabilistic reliability. CatBoost excelled in ROC–AUC and precision–recall metrics. The integration of Morgan fingerprints significantly improved bioactivity prediction across all models by enhancing structural feature recognition. Conclusions: This work highlights the transformative role of machine learning—particularly dANNs and SVM—in rational drug discovery. By enabling accurate bioactivity prediction, our model pipeline can effectively reduce experimental burden, optimize compound selection, and support personalized cancer treatment design. The proposed framework advances kinase inhibitor screening pipelines and provides a scalable foundation for translational applications in precision oncology. By enabling early identification of bioactive compounds with favorable pharmacological profiles, the results of this study may support more efficient candidate selection for clinical drug development, particularly in regards to cancer therapy and kinase-associated disorders. Full article

Head and neck squamous cell carcinoma (HNSCC) is a prevalent and aggressive cancer, and accurate staging using the AJCC system is essential for treatment planning. This study aims to enhance AJCC staging by integrating both clinical and imaging data using a multimodal deep learning pipeline. We propose a framework that employs a VGG16-based masked autoencoder (MAE) for self-supervised visual feature learning, enhanced by attention mechanisms (CBAM and BAM), and fuses image and clinical features using an attention-weighted fusion network. The models, benchmarked on the HNSCC and HN1 datasets, achieved approximately 80% accuracy (four classes) and ~66% accuracy (five classes), with notable AUC improvements, especially under BAM. The integration of clinical features significantly enhances stage-classification performance, setting a precedent for robust multimodal pipelines in radiomics-based oncology applications. Full article

In the realm of critical infrastructure protection, robust intrusion detection systems (IDSs) are essential for securing essential services. This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. The study evaluates supervised learning algorithms alongside unsupervised learning algorithms. The analysis reveals that supervised learning algorithms exhibit exceptional performance with high accuracy and reliability, making them well-suited for handling the diverse and complex nature of anomalies in critical infrastructure. They demonstrate significant capabilities in capturing spatial and temporal variables. Among the unsupervised approaches, valuable insights into anomaly detection are provided without the necessity for labeled data, although they face challenges with higher rates of false positives and negatives. By outlining the benefits and drawbacks of these machine learning algorithms in relation to critical infrastructure, this research advances the field of cybersecurity. It emphasizes the importance of integrating supervised and unsupervised techniques to enhance the resilience of IDSs, ensuring the timely detection and mitigation of potential threats. The findings offer practical guidance for industry professionals on selecting and deploying effective machine learning algorithms in critical infrastructure environments. Full article

Background: A low-FODMAP diet is considered as a potential supportive treatment approach in some gastrointestinal disorders. The aim of this study was to systematically review the literature for randomized controlled trials assessing the efficacy of the low-FODMAP diet on the severity of gastrointestinal symptoms and quality of life in patients with gastrointestinal disorders. Methods: This review was conducted in accordance with CASP tool and PRISMA guidelines. A comprehensive search of the PubMed, Scopus, and Web of Science databases resulted in the identification of fourteen randomized controlled trials. Results: Ten studies examined the effect of the low-FODMAP diet in patients with irritable bowel syndrome (IBS), three with inflammatory bowel disease (IBD), and one with symptomatic proton pump inhibitor (PPI) refractory gastroesophageal reflux disease (GERD). All interventions compared the low-FODMAP diet with another diet and lasted from 3 to 12 weeks. Most studies on IBS showed significant improvements in abdominal pain, bloating, and quality of life compared to control diets. In IBD, improvements were mainly observed in functional gastrointestinal symptoms, while no clear benefit was demonstrated in GERD. Heterogeneity in study designs, intervention durations, comparator diets, and outcome measures limited the ability to conduct a meta-analysis. Conclusions: Although a low-FODMAP diet may reduce symptoms in selected individuals, it is not universally necessary. Importantly, the diet’s restrictive nature and potential long-term effects—such as nutritional deficiencies and alterations in gut microbiota—highlight the need for clinical supervision by dietitians with expertise in gastrointestinal disorders. Furthermore, in some cases, symptom improvement may be achievable through less restrictive changes, such as improving food hygiene and reducing intake of processed or high-sugar foods. Further high-quality randomized controlled trials with standardized endpoints and longer follow-up are needed to clarify the efficacy and safety of the low-FODMAP diet across various gastrointestinal conditions. Full article

Multiple sclerosis (MS) is a chronic progressive neurodegenerative disease that leads to disabilities such as difficulty moving and slowed cognitive processing. It is the leading non-traumatic cause of disability worldwide. MS also has a high potential to become a model for neurodegenerative diseases with a progression like Alzheimer’s or Parkinson’s. Cardiovascular diseases (CVDs) remain the leading cause of global deaths and have a considerable economic impact. The higher incidence of cardiovascular comorbidities in patients with MS compared to healthy individuals of the same age worsens the prognosis of neurological pathology, leading to a higher level of disability, poorer physical outcomes, higher depression scores, cognitive aging, and diminished quality of life. Classical observational studies often have questionable elements that can represent a source of error, making it difficult to establish a causal relationship between MS and CVD. Genetic studies, including genome-wide evaluation, may resolve this issue and may represent a topic for future research. We report the case of a 31-year-old male patient with a history of multiple sclerosis (MS) diagnosed seven years prior, who presented with acute chest pain upon returning from vacation. Despite the previous recommendation for disease-modifying therapy, the patient had discontinued treatment by personal choice. Electrocardiography (ECG) revealed ST-segment elevation in inferior leads, and emergent coronary angiography identified severe multi-vessel coronary artery disease (CAD), requiring immediate revascularization. This case highlights the potential cardiovascular risks in young patients with MS and the importance of continuous medical supervision. Full article

Camellia oil, rich in monounsaturated fatty acids, squalene, tocopherols, and polyphenols, is highly valued for its nutritional benefits. However, its high market value and regional variations have led to frequent adulteration, highlighting the need for rapid, non-destructive methods for authentication, geographical traceability, and quality assessment. This study employed portable Raman spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) and Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) to differentiate camellia oil from other edible oils and evaluate its thermal and photo-oxidative stability. PLS-DA, based on VIP-selected spectral variables, effectively distinguished camellia oil, with Raman bands near 1250 cm⁻¹ and 1650 cm⁻¹ contributing significantly. A unique peak at 1525 cm⁻¹, observed in samples from Gongcheng, Guangxi, was associated with carotenoids and served as a potential marker for geographical traceability. MCR-ALS modeling revealed significant reductions in the 1650 cm⁻¹ and 1525 cm⁻¹ peaks when temperatures exceeded 150 °C, indicating degradation of unsaturated fatty acids and carotenoids. Under UV exposure, the 1525 cm⁻¹ peak declined sharply and nearly disappeared after 24 h, suggesting rapid carotenoid degradation via photooxidation. Extended UV treatment also affected the 1650 cm⁻¹ peak and led to oxidative product accumulation. Overall, this study demonstrates the feasibility of integrating Raman spectroscopy with chemometric analysis for efficient oil classification, traceability, and stability monitoring, offering a valuable tool for food quality control and market supervision. Full article