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- Improved regeneration and de novo bone formation in a diabetic zebrafish model treated with paricalcitol and cinacalcetPublication . Pires De Carvalho, Filipe Ricardo; Fernandes, Ana R.; Leonor Cancela, M.; Gavaia, PauloBone changes related to diabetes have been well stablished, but few strategies have been developed to prevent this growing health problem. In our work, we propose to investigate the effects of calcitriol as well as of a vitamin D analog (paricalcitol) and a calcimimetic (cinacalcet), in fin regeneration and de novo mineralization in a zebrafish model of diabetes. Following exposure of diabetic transgenic Tg(ins: nfsb-mCherry) zebrafish to calcitriol, paricalcitol and cinacalcet, caudal fins were amputated to assess their effects on tissue regeneration. Caudal fin mineralized and regenerated areas were quantified by in vivo alizarin red staining. Quantitative real-time PCR was performed using RNA from the vertebral column. Diabetic fish treated with cinacalcet and paricalcitol presented increased regenerated and mineralized areas when compared with non-treated diabetic group, while no significant increase was observed in nondiabetic fish treated with both drugs. Gene expression analysis showed an up-regulation for runt-related transcription factor 2b (runx2b), bone gamma-carboxyglutamic acid-containing protein (bglap), insulin a (insa) and insulin b (insb) and a trend of increase for sp7 transcription factor (sp7) in diabetic groups treated with cinacalcet and paricalcitol. Expression of insra and vdra was up-regulated in both diabetic and nondiabetic fish treated with cinacalcet. In nondiabetic fish treated with paricalcitol and cinacalcet a similar increase in gene expression could be observed but not so pronounced. The increased mineralization and regeneration in diabetic zebrafish treated with cinacalcet and paricalcitol can be explained by increased osteoblastic differentiation and increased insulin expression indicating pro-osteogenic potential of both drugs.
- Altered bone microarchitecture in a type 1 diabetes mouse model Ins2 (Akita)Publication . Pires De Carvalho, Filipe Ricardo; Calado, Sofia; Silva, Gabriela A.; Diogo, Gabriela S.; Moreira da Silva, Joana; Reis, Rui L.; Cancela, M. Leonor; Gavaia, PauloType 1 diabetes mellitus (T1DM) has been associated to several cartilage and bone alterations including growth retardation, increased fracture risk, and bone loss. To determine the effect of long term diabetes on bone we used adult and aging Ins2 Akita mice that developed T1DM around 3-4 weeks after birth. Both Ins2 Akita and wild-type (WT) mice were analyzed at 4, 6, and 12 months to assess bone parameters such as femur length, growth plate thickness and number of mature and preapoptotic chondrocytes. In addition, bone microarchitecture of the cortical and trabecular regions was measured by microcomputed tomography and gene expression of Adamst-5, Col2, Igf1, Runx2, Acp5, and Oc was quantified by quantitative real-time polymerase chain reaction. Ins2 Akita mice showed a decreased longitudinal growth of the femur that was related to decreased growth plate thickness, lower number of chondrocytes and to a higher number of preapoptotic cells. These changes were associated with higher expression of Adamst-5, suggesting higher cartilage degradation, and with low expression levels of Igf1 and Col2 that reflect the decreased growth ability of diabetic mice. Ins2 Akita bone morphology was characterized by low cortical bone area (Ct.Ar) but higher trabecular bone volume (BV/TV) and expression analysis showed a downregulation of bone markers Acp5, Oc, and Runx2. Serum levels of insulin and leptin were found to be reduced at all-time points Ins2 Akita . We suggest that Ins2 Akita mice bone phenotype is caused by lower bone formation and even lower bone resorption due to insulin deficiency and to a possible relation with low leptin signaling.
- Mapping gene expression in social anxiety reveals the main brain structures involved in this disorderPublication . Carvalho, Filipe Ricardo Pires de; Nóbrega, Clévio; Martins, A.Social Anxiety Disorder (SAD) is characterized by emotional and attentional biases as well as distorted negative self-beliefs. According this, we proposed to identify the brain structures and hub genes involved in SAD. An analysis in Pubmed and TRANSFAC was conducted and 72 genes were identified. Using Microarray data, from Allen Human Brain Atlas, it was possible to identify three modules of co-expressed genes from our gene set (R package WGCNA). Higher mean gene expression was found in cortico-medial group, basomedial nucleus, ATZ in amygdala and in head and tail of the caudate nucleus, nucleus accumbens and putamen in striatum. Our enrichment analysis identified the followed hub genes: DRD2, HTR1A, JUN, SP1 and HDAC4. We suggest that SAD is explained by delayed extinction of circuitry for conditioned fear. Caused by reduced activation of the dopaminergic and serotonergic systems,due diminished expectation of reward during social interactions.
- OrthoMortPred: Predicting one-year mortality following orthopedic hospitalization.Publication . Pires de Carvalho, Filipe Ricardo; Gavaia, Paulo; Brito Camacho, AntónioPredicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, 'EMERGENCY ADMISSION DATE TIME' emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of 'EMERGENCY ADMISSION DATE TIME' as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.
- OrthoMortPred: predicting one-year mortality following orthopedic hospitalizationPublication . Pires de Carvalho, Filipe Ricardo; Gavaia, Paulo; Brito Camacho, AntónioObjective: Predicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. Methods: We analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. Results: Our model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, ’EMERGENCY ADMISSION DATE TIME’ emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. Conclusion: Our study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of ’EMERGENCY ADMISSION DATE TIME’ as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.
- 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.
- 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.