Unit of Surgical Pathology

Parma, Italy

Unit of Surgical Pathology

Parma, Italy

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Spinillo A.,University of Pavia | Gardella B.,University of Pavia | Bariselli S.,University of Pavia | Alfei A.,University of Pavia | And 2 more authors.
Journal of Perinatal Medicine | Year: 2014

Objective: To correlate placental pathologic lesions, as defined by the Society for Pediatric Pathology, to the severity of the ratio of the pulsatility Doppler index (PI) of the fetal middle cerebral artery to that of the umbilical artery (cerebroplacental ratio, CPR). Study design: A cohort-study of 176 singleton pregnancies complicated by fetal growth restriction (FGR). Results: The mean values of gestational age, birth weight and CPR of the entire cohort were 33.9 ± 3.6 weeks, 1552 ± 561 g, and 1.33 ± 0.68, respectively. In ordered logistic regression analysis, after adjustment for potential confounders, muscularised arteries (Odds Ratio [OR] = 3.14; 95% confidence intervals [CI] = 1.58-6.28, P = 0.001), mural hypertrophy (OR = 2.35; 95% CI = 1.26-4.4, P = 0.008), immature intermediate trophoblast (OR = 2.0; 95% CI = 1.07-3.71, P = 0.03) and maternal vascular underperfusion (OR = 2.32; 95% CI = 1.25-4.23, P = 0.007) were the only parameters associated with severity of CPR. Conclusions: The correlation between placental histological findings indicating maternal underperfusion and placental occlusion suggest that forced centralization of fetal circulation in FGR could be at least partially attributable to the hemodynamic consequences of increased placental vascular resistance.


Ricci L.,University of Trento | Vescovo V.D.,University of Trento | Cantaloni C.,Unit of Surgical Pathology | Grasso M.,University of Trento | And 2 more authors.
BMC Bioinformatics | Year: 2015

Background: During the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature: the distribution of the measured miRNA expressions and the statistical uncertainty that affects the parameters that characterize a classifier. In this paper, these topics are analysed in detail by discussing a model problem, i.e. the development of a Bayesian classifier that, on the basis of the expression of miR-205, miR-21 and snRNA U6, discriminates samples into two classes of pulmonary tumors: adenocarcinomas and squamous cell carcinomas. Results: We proved that the variance of miRNA expression triplicates is well described by a normal distribution and that triplicate averages also follow normal distributions. We provide a method to enhance a classifiers' performance by exploiting the correlations between the class-discriminating miRNA and the expression of an additional normalized miRNA. Conclusions: By exploiting the normal behavior of triplicate variances and averages, invalid samples (outliers) can be identified by checking their variability via chi-square test or their displacement by the respective population mean via Student's t-test. Finally, the normal behavior allows to optimally set the Bayesian classifier and to determine its performance and the related uncertainty. © 2015 Ricci et al.


Spinillo A.,University of Pavia | Gardella B.,University of Pavia | Bariselli S.,University of Pavia | Alfei A.,University of Pavia | And 2 more authors.
Prenatal Diagnosis | Year: 2012

Objective: The objective of the study was to evaluate the association between placental histological patterns and umbilical artery (UA) Doppler velocimetry in pregnancies complicated by fetal growth restriction (FGR). Methods: A cohort of 126 FGR pregnancies was followed according to a standard protocol. Placental lesions were diagnosed according to consensus nomenclature and standardized criteria. Results: Pulsatility index was normal in 45 (35.7%) and increased in 44 (34.9%) women. End-diastolic UA Doppler flow was absent in 27 (21.4%) and reversed in 10 (7.9%). Fifty-four women (42.9%) had preeclampsia. In preeclampsia, increasing Doppler abnormalities, from normal to reversed UA end-diastolic flow, were directly associated only with an increased number of placental syncytial knots. In normotensive pregnancies, Doppler abnormalities were associated with increased intervillous fibrin deposits, villous hypoplasia, syncytial knots, placental site giant cells, immature intermediate trophoblast, and with pattern of lesions indicating superficial implantation and maternal vascular underperfusion. In the whole cohort, increase of syncytial knots [odds ratio (OR)=28.7; 95% confidence interval (CI)=2.75-298.5], intervillous fibrin deposits (OR=2.1; 95%CI=1.04-4.28), placental site giant cells (OR=3.0; 95%CI=1.05-8.84), and patterns suggesting maternal underperfusion (OR=2.9; 95%CI=1.0-7.1) were independently associated with increased rates of absent/reversed UA end-diastolic flow. Conclusions: In pregnancies complicated by FGR, abnormalities of UA Doppler velocimetry were associated with placental lesions indicating superficial implantation and maternal vascular underperfusion. © 2012 John Wiley & Sons, Ltd.


PubMed | University of Trento and Unit of Surgical Pathology
Type: | Journal: BMC bioinformatics | Year: 2015

During the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature: the distribution of the measured miRNA expressions and the statistical uncertainty that affects the parameters that characterize a classifier. In this paper, these topics are analysed in detail by discussing a model problem, i.e. the development of a Bayesian classifier that, on the basis of the expression of miR-205, miR-21 and snRNA U6, discriminates samples into two classes of pulmonary tumors: adenocarcinomas and squamous cell carcinomas.We proved that the variance of miRNA expression triplicates is well described by a normal distribution and that triplicate averages also follow normal distributions. We provide a method to enhance a classifiers performance by exploiting the correlations between the class-discriminating miRNA and the expression of an additional normalized miRNA.By exploiting the normal behavior of triplicate variances and averages, invalid samples (outliers) can be identified by checking their variability via chi-square test or their displacement by the respective population mean via Students t-test. Finally, the normal behavior allows to optimally set the Bayesian classifier and to determine its performance and the related uncertainty.

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