Browsing by Author "Barros, I"
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- Cistadenocarcinoma do Ovário. A Propósito de um Caso ClínicoPublication . Ribeiro, L; Barros, I; Lourenço, C; Oliveira, M; Pinto, E; Santos, T; Barros Veloso, AJOs autores apresentam o caso clínico de uma doente de 49 anos, internada por poliadenopatias e sintomatologia respiratória, cujo diagnóstico definitivo de cistadenocarcinoma seroso do ovário, foi efectuado através do exame necrópsico. O quadro clínico era dominado por extensa invasão linfática predominantemente supradiafragmática, apresentação não habitual deste tipo de neoplasia.
- A Common Variant in the CDK8 Gene Is Associated with Sporadic Pituitary Adenomas in the Portuguese Population: a Case-Control StudyPublication . Gaspar, L; Gonçalves, C; Fonseca, F; Carvalho, D; Cortez, L; Palha, A; Barros, I; Nobre, E; Duarte, J; Amaral, C; Bugalho, MJ; Marques, O; Pereira, B; Lemos, MThe majority of pituitary adenomas occur in a sporadic context, and in the absence of known genetic predisposition. Three common variants at the NEBL (rs2359536), PCDH15 (rs10763170) and CDK8 (rs17083838) loci were previously associated with sporadic pituitary adenomas in the Han Chinese population, but these findings have not yet been replicated in any other population. The aim of this case-control study was to assess if these variants are associated with susceptibility to sporadic pituitary adenomas in the Portuguese population. Genotype and allele frequencies were determined in 570 cases and in 546 controls. The CDK8 rs17083838 minor allele (A allele) was significantly associated with sporadic pituitary adenomas, under an additive (odds ratio (OR) 1.73, 95% confidence interval (CI) 1.19-2.50, p = 0.004) and dominant (OR 1.82, 95% CI 1.24-2.68, p = 0.002) inheritance model. The NEBL rs2359536 and PCDH15 rs10763170 variants were not associated with the overall risk for the disease, although a borderline significant association was observed between the PCDH15 rs10763170 minor allele (T allele) and somatotrophinomas (dominant model, OR 1.55, 95% CI 1.02-2.35, p = 0.035). These findings suggest that the CDK8 rs17083838 variant, and possibly the PCDH15 rs10763170 variant, may increase susceptibility to sporadic pituitary adenomas in the Portuguese population.
- Enfisema Pulmonar em Doente com Síndrome de TurnerPublication . Sousa, AM; Lourenço, C; Barros, I; Martinho, T; Brás, A; Santos, T; Barros Veloso, AJO Síndrome de Turner foi descrito pela primeira vez em 1938 por Henry Turner e tem uma incidência de 1:3000 mulheres nascidas. Os autores apresentam um caso raro de uma mulher de 48 anos com Síndrome de Turner, cujo cariótipo era (46, X, i (Xq)), tardiamente diagnosticado, associado a enfisema pulmonar e hipertensão pulmonar. O caso e os métodos de estudo são apresentados. Alguns aspectos deste caso, nomeadamente a hipótese do enfisema pulmonar se relacionar com Síndrome de Turner, são discutidos.
- Hepatectomia Esquerda Robótica. Abordagem CaudalPublication . Barros, I; Silva, S; Carrelha, S; Santos Coelho, J; Pinto Marques, H
- Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at RiskPublication . Vigia, E; Ramalhete, L; Ribeiro, R; Barros, I; Chumbinho, B; Filipe, E; Pena, A; Bicho, L; Nobre, A; Carrelha, S; Sobral, M; Lamelas, J; Coelho, JS; Ferreira, A; Pinto Marques, HIntroduction: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. Methods: We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. Results: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. Conclusion: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.
- Predicting Function Delay with a Machine Learning Model: Improve the Long-term Survival of Pancreatic GraftsPublication . Vigia, E; Ramalhete, L; Barros, I; Chumbinho, B; Filipe, E; Pena, A; Bicho, L; Nobre, A; Carrelha, S; Corado, S; Sobral, M; Lamelas, J; Santos Coelho, J; Pinto Marques, H; Pico, P; Costa, S; Rodrigues, F; Bigotte Vieira, M; Magriço, R; Cotovio, P; Caeiro, F; Aires, I; Silva, C; Remédio, F; Martins, A; Ferreira, A; Paulino, J; Nolasco, F; Ribeiro, RThe impact of delayed graft function on outcomes following various solid organ transplants is well documented and addressed in the literature. Delayed graft function following various solid organ transplants is associated with both short- and long-term graft survival issues. In a retrospective cohort study including 106 patients we evaluated whether pancreas graft survival differs according to moment of insulin therapy following simultaneous pancreaskidney transplant. As a result, we aimed to identify possible risk factors and build a machine-learning-based model that predicts the likelihood of dysfunction following SPK transplant patients based on day zero data after transplant, allowing to enhance pancreatic graft survival. Feature selection by Relief algorithm yielded donor features, age, cause of death, hemoglobin, gender, ventilation days, days in ICU, length of cardiac respiratory arrest and recipient features, gender, long-term insulin, dialysis type, time of diabetes mellitus, vPRA pre-Tx, number of HLA-A mismatches and PRDI, all contributed to the models' strength.