Università Campus Bio-Medico

About: Università Campus Bio-Medico is a education organization based out in Rome, Italy. It is known for research contribution in the topics: Population & Medicine. The organization has 2829 authors who have published 8519 publications receiving 193689 citations. The organization is also known as: Universita Campus Bio-Medico & Campus Bio-Medico University.

Estimating renal function in older people: a comparison of three formulas

Abstract: Background: estimation of the glomerular filtration rate (GFR) at the bedside is important because renal insufficiency is related to increased mortality and morbidity. A discrepancy between the Cockroft-Gault (CG) and the Modification of Diet in Renal Disease (MDRD) formulas has been observed in older people. Objective: to compare the GFR of inpatients aged 65 or older estimated using the CG and two of the MDRD formulas. Setting: acute care geriatrics and internal medicine wards. Subjects and methods: data come from the Gruppo Italiano di Farmacovigilanza nell'Anziano (GIFA). To quantify the agreement between the formulas, we used the 95% limits of agreement, the K statistic and a graphic approach to evaluate the influence of potential confounders on the magnitude of the difference in the GFR estimates. Results: we studied 7,747 persons [51.1% women, mean age 77.8 (SD 7.2)]. The mean GFR estimated using the CG, MDRD1 and MDRD2 formulas was 51.2 ml/min (21.3), 54.9 ml/min (19.8) and 64.7 ml/min (24.2), respectively. At the individual level, the MDRD formulas can yield estimates that differ by more than 50% compared with the CG formula. The formulas showed a moderate agreement in diagnosing moderate renal insufficiency and a fair agreement in diagnosing severe renal insufficiency. The magnitude of the difference in GFR estimates was influenced by age and weight. Conclusions: the CG and MDRD formulas have a good average agreement, but at the individual level, they can give estimates that differ substantially, and cannot be used interchangeably to measure renal function in elderly people.

The role of novel biomarker HE4 in endometrial cancer: a case control prospective study

Abstract: The aim of the study was to explore the clinical value of serum human epididymis secretory protein E4 (HE4) and CA125 in endometrial carcinoma. From January 2010 to April 2012, serum specimens were collected from consecutive cases of endometrial carcinoma and from cases of uterus benign disease (control group). The CA125 normal value is considered less than 35 U/mL. Two HE4 cutoff are considered: less than 70 pmol/L and less than 150 pmol/L. The specificity analysis was performed using the Mann–Whitney test for the CA125 and HE4 series. The level of statistical significance is set at p < 0.05. The sensitivity of CA125 in detecting endometrial cancer is 19.8 %, whereas the sensitivity of HE4 is 59.4 and 35.6 % for 70 and 150 pmol/L cutoff, respectively. Thus the specificity of HE4 is 100 % (positive predictive value = 100 %, negative predictive value = 71.52 and 61.31 % considering the two HE4 cutoff, respectively), whereas the CA125 specificity is 62.14 % (positive predictive value = 33.9 %, negative predictive value = 44.14 %) in detection of endometrial cancer. Combining CA125 and HE4, the sensitivity to detect endometrial cancer is 60.4 and 34.6 %, at HE4 cutoff of 70 and 150 pmol/L, respectively, with a specificity of 100 %. HE4 may be a new tool for preoperative evaluation and postoperative surveillance of endometrial cancer patients, with a positive predictive value = 100 %. HE4 at cutoff of 70 pmol/L yields the best sensitivity and specificity.

A new expression of diabetes: double diabetes.

Abstract: Diabetes is on the increase worldwide. The incidence of both type 1 and type 2 diabetes has shown a rise, in parallel with a notable increase in the incidence of a new expression of the disease in children and adolescents, with the characteristics of a mixture of the two types of diabetes, and referred to as 'double diabetes'. Insulin resistance and obesity, together with the presence of markers of pancreatic autoimmunity - namely, autoantibodies to islet cell antigens - typically define this condition. However, recognition of double diabetes can pose problems. In most cases, a reduction in the 'autoimmune load' and an increase in the 'metabolic load' are helpful for attaining a correct diagnosis in a diabetic child.

NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation

Abstract: Currently, the typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multi-fingered hand prostheses. Pattern recognition and machine learning techniques applied to sEMG can be effective for a natural control based on the residual muscles contraction of amputated people corresponding to phantom limb movements. As the researches has reached an advanced grade accuracy, these algorithms have been proved and the embedding is necessary for the realization of prosthetic devices. The aim of this work is to provide engineering tools and indications on how to choose the most suitable classifier, and its specific internal settings for an embedded control of multigrip hand prostheses. By means of an innovative statistical analysis, we compare 4 different classifiers: Nonlinear Logistic Regression, Multi-Layer Perceptron, Support Vector Machine and Linear Discriminant Analysis, which was considered as ground truth. Experimental tests have been performed on sEMG data collected from 30 people with trans-radial amputation, in which the algorithms were evaluated for both performance and computational burden, then the statistical analysis has been based on the Wilcoxon Signed-Rank test and statistical significance was considered at p < 0.05. The comparative analysis among NLR, MLP and SVM shows that, for either classification performance and for the number of classification parameters, SVM attains the highest values followed by MLP, and then by NLR. However, using as unique constraint to evaluate the maximum acceptable complexity of each classifier one of the typically available memory of a high performance microcontroller, the comparison pointed out that for people with trans-radial amputation the algorithm that produces the best compromise is NLR closely followed by MLP. This result was also confirmed by the comparison with LDA with time domain features, which provided not significant differences of performance and computational burden between NLR and LDA. The proposed analysis would provide innovative engineering tools and indications on how to choose the most suitable classifier based on the application and the desired results for prostheses control.