Abstract In this case, we have presented a 55-year old patient with dysuria and bloody urine. He was hospitalized at the Urology Department of County Zenica Hospital due to obstructive uropathy. Diagnostics showed the cause is a large bleeding mass in prostatic part of urethra. After cystectomy, immunohistochemistry revealed urachal adenocarcinoma, rare type of urogenital carcinomas, presented only in 5% of all cancer types. He was treated with dual modality, chemotherapy and radiotherapy

BackgroundThe risk stratification of patients with diabetes mellitus (DM) is a major objective for the clinicians, and it can be achieved by coronary flow velocity reserve (CFVR) or with coronary artery calcium score (CS). CS evaluates underlying coronary atherosclerotic plaque burden and CFVR estimates both presence of coronary artery stenosis and microvascular function. Consequently, CFVR may provide unique risk information beyond the extent of coronary atherosclerosis.AimOur aim is to assess joint prognostic value of CFVR and CS in asymptomatic DM patients.Materials and methodsWe prospectively included 200 asymptomatic patients (45,5 % male, mean age 57,35 ± 11,25), out of which, there were 101 asymptomatic patients with DM and 99 asymptomatic patients without DM, but with one or more conventionally risk factors for coronary artery disease. We analyzed clinical, biochemical, metabolic, inflammatory parameters, CS by Agatston method, transthoracic Doppler echocardiography CFVR of left anterior descending artery and echocardiographic parameters.ResultsTotal CS and CS LAD were significantly higher, while mean CFVR was lower in diabetics compared to the nondiabetics. During 1 year follow-up, 24 patients experienced cardio-vascular events (one cardiovascular death, two strokes, three myocardial infarctions, nine new onsets of unstable angina and nine myocardial revascularizations): 19 patients with DM and five non DM patients, (p = 0,003). Overall event free survival was significantly higher in non DM group, compared to the DM group (94,9 % vs. 81,2 %, p = 0,002 respectively), while the patients with CS ≥200 and CFVR <2 had the worst outcome during 1 year follow up in the whole study population as well as in the DM group. At multivariable analysis CFVR on LAD (HR 12.918, 95 % CI 3.865–43.177, p < 0.001) and total CS (HR 13.393, 95 % CI 1.675–107.119, p = 0.014) were independent prognostic predictors of adverse events in DM group of patients.ConclusionBoth CS and CFVR provide independent and complementary prognostic information in asymptomatic DM patients. When two parameters are analyzed together, the risk stratification ability improves, even when DM patients are analyzed together with non DM patients. As a result, DM patients with CS ≥200 and CFVR <2 had the worst outcome. Consequently, the use of two tests identified subset of patients who can derive the most benefit from the intensive prevention measures.

BackgroundStanding from a bed or chair may cause a significant lowering of blood pressure (ΔBP), which may have severe consequences such as, for example, falls in older subjects. The goal of this study was to develop a mathematical model to predict the ΔBP due to standing in healthy subjects, based on their Heart Rate Variability, recorded in the 5 minutes before standing.MethodsHeart Rate Variability was extracted from an electrocardiogram, recorded from 10 healthy subjects during the 5 minutes before standing. The blood pressure value was measured before and after rising. A mathematical model aiming to predict ΔBP based on Heart Rate Variability measurements was developed using a robust multi-linear regression and was validated with the leave-one-subject-out cross-validation technique.ResultsThe model predicted correctly the ΔBP in 80% of experiments, with an error below the measurement error of sphygmomanometer digital devices (±4.5 mmHg), a false negative rate of 7.5% and a false positive rate of 10%. The magnitude of the ΔBP was associated with a depressed and less chaotic Heart Rate Variability pattern.ConclusionsThe present study showes that blood pressure lowering due to standing can be predicted by monitoring the Heart Rate Variability in the 5 minutes before standing.

BackgroundFalls in the elderly is a major problem. Although falls have a multifactorial etiology, a commonly cited cause of falls in older people is poor vision. This study proposes a method to discriminate fallers and non-fallers among ophthalmic patients, based on data-mining algorithms applied to health and socio-demographic information.MethodsA group of 150 subjects aged 55 years and older, recruited at the Eye Clinic of the Second University of Naples, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. A subject who reported at least one fall within one year was considered as faller, otherwise as non-faller. Different tree-based data-mining algorithms (i.e., C4.5, Adaboost and Random Forest) were used to develop automatic classifiers and their performances were evaluated by assessing the receiver-operator characteristics curve estimated with the 10-fold-crossvalidation approach.ResultsThe best predictive model, based on Random Forest, enabled to identify fallers with a sensitivity and specificity rate of 72.6% and 77.9%, respectively. The most informative variables were: intraocular pressure, best corrected visual acuity and the answers to the total difficulty score of the Activities of Daily Vision Scale (a questionnaire for the measurement of visual disability).ConclusionsThe current study confirmed that some ophthalmic features (i.e. cataract surgery, lower intraocular pressure values) could be associated with a lower fall risk among visually impaired subjects. Finally, automatic analysis of a combination of visual function parameters (either self-evaluated either by ophthalmological test) and other health information, by data-mining algorithms, could be a feasible tool for identifying fallers among ophthalmic patients.