CCM2-Artigos (em revistas ou actas indexadas)
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Percorrer CCM2-Artigos (em revistas ou actas indexadas) por Objetivos de Desenvolvimento Sustentável (ODS) "10:Reduzir as Desigualdades"
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- Advanced nanotherapeutic strategies transforming diabetic wound healingPublication . Ramos, Filipa; Kumar, Girish; Virmani, Tarun; Sharma, Abhishek; Duarte, Sofia O. D.; Fonte, PedroDue to their high recurrence rates and slow healing, diabetic wounds are becoming a greater public health concern [Citation1]. Each year, 1.6 million cases of diabetic wounds occur in the United States alone, affecting approximately 18.6 million people worldwide [Citation2]. Because of poor cellular regeneration, increased inflammation, and reduced angiogenesis, traditional treatments like debridement, antibiotics, and dressings usually do not work [Citation3]. To overcome the limitations of traditional treatments, there is now a significant demand for advanced therapeutic modalities that promise accurate, efficient, and rapid healing processes [Citation4]. These include microneedles (MNs), exosomes, tetrahedral framework nucleic acids (tFNAs), three-dimensional scaffolds, gene therapy, oxygen-releasing biomaterials, phototherapies, and nanozymes.
- Biological therapies for metastatic colorectal cancer: literature reviewPublication . Almeida, Maria Patricia; Condinho, MónicaColorectal cancer is among the most prevalent and lethal malignancies worldwide. Its initially asymptomatic nature contributes to a high incidence of metastatic cases. Although predominantly diagnosed in older adults, the incidence among younger populations is rising at an alarming rate. Historically, treatment has relied on antineoplastic agents such as 5-fluorouracil, irinotecan, and oxaliplatin. While these agents remain in use, their effectiveness is limited, particularly in metastatic disease, with modest improvements in overall survival and progressionfree survival. Moreover, their low target specificity results in significant systemic toxicity. This underscores the urgent need formore selective and less toxic therapeutic strategies, such as monoclonal antibodies. Monoclonal antibodies targeting Vascular Endothelial Growth Factor (VEGF), Epidermal Growth Factor Receptor (EGFR), and immune checkpoints have become integral to the management of metastatic colorectal cancer. Notable examples include bevacizumab (anti-VEGF), cetuximab and panitumumab (anti-EGFR), and the immune checkpoint inhibitors pembrolizumab, nivolumab, and ipilimumab. Their clinical success especially when guided by molecular tumour profiling highlights their contribution to improved patient outcomes. In addition, other targeted therapies distinct from monoclonal antibodies are currently under investigation.
- Carreer profiles: options and insightsPublication . Krug, LilianI hold a bachelor’s degree in oceanography (2004) from the Federal University of Paraná, Brazil; a master’s degree in remote sensing (2008) from the National Institute for Space Research, Brazil; a postgraduate specialization in observational oceanography (2010) from the Nippon Foundation-Partnership for Observation of the Global Ocean (NF-POGO) Centre of Excellence in Observational Oceanography at the Bermuda Institute of Ocean Sciences, Bermuda; and a doctorate in marine and environmental sciences (2018) from the University of Algarve, Portugal. Since my undergraduate studies, I have worked on various applications of satellite remote sensing and modeled data to ocean and coastal research, including shallow water bathymetry, coral bleaching prediction, sea-air CO2 exchange, and phytoplankton phenology and variability, as well as their environmental drivers.
- Enhancing osteoporosis risk prediction using machine learning: a holistic approach integrating biomarkers and clinical dataPublication . Pires de Carvalho, Filipe Ricardo; Gavaia, PauloOsteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annually. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limitations, including restricted availability and radiation exposure, highlight the need for alternative screening methods. We developed a machine learning model to predict OP risk using routinely collected clinical data, deliberately excluding DXA measurements to ensure broad accessibility. Using data from NHANES cycles 2007–2014, we analyzed 7924 participants aged 50 years and older, identifying 1636 OP cases (20.6 %) and 6288 normal cases (79.4 %) through comprehensive criteria incorporating both WHO densitometric standards (T-scores ≤ − 2.5) and anthropometric risk factors. We implemented a stacking ensemble model combining four specialized classifiers (Gradient Boosting, Random Forest, XGBoost, and LightGBM) with a logistic regression meta-classifier. The model achieved 93 % accuracy, an AUC of 0.94, and demonstrated robust performance through cross-validation (mean score: 0.929 ± 0.030). feature importance analysis revealed age (6.04 %), arm muscle circumference (5.61 %), and body weight (5.30 %) as the most influential predictors, followed by gender (3.28 %), BMI (2.71 %), and calcium intake (2.42 %). Additional significant predictors included folate (2.28 %), height (2.23 %), hand grip strength (2.21 %), and alkaline phosphatase (2.16 %). These biologically plausible relationships align with established clinical knowledge of OP risk factors. The model’s strong performance metrics and reliance on readily available clinical data suggest its potential as a practical screening tool, particularly in settings with limited DXA access. All code and implementation details are openly available on GitHub, facilitating integration into existing healthcare systems. This approach offers a promising pathway for enhancing early OP detection and risk assessment across diverse healthcare settings.
- Letter to the editor: robustness of osteoporosis risk prediction models with enhanced statistical analysesPublication . Pires de Carvalho, Filipe Ricardo; Gavaia, PauloIn response to Oka et al.’s letter, we conducted additional statistical analyses to validate the robustness of our osteoporosis risk prediction model using NHANES 2007–2014 data (n = 7924). We evaluated 10 key predictors through Spearman’s rho, Kendall’s tau, Mutual Information (MI), and Total Correlation. Weight (BMX_BMXWT) and arm circumference (BMX_BMXARMC) showed strong negative correlations with osteoporosis risk (rho: 0.49, 0.47, p < 1e-270; MI: 0.17, 0.15), while age (DEMO_RIDAGEYR) exhibited a positive correlation (rho: 0.33, p < 1e-128; MI: 0.08). Total Correlation (32.68) confirmed significant multivariate interactions among predictors. These findings reinforce the model’s predictive strength, addressing Oka et al.’s recommendations and affirming the importance of anthropometric and demographic factors in osteoporosis risk assessment.