PREVES: A Population-Based Survey Focused on Cancer and Nutrition.

INTRODUCTION: Approximately a third of cancer-related deaths are attributable to modifiable factors. METHODS: As a pilot experience, a cross-sectional survey was conducted in 8,000 citizens residing in four different municipalities of the Salerno province (Sarno, Pagani, San Valentino Torio, and San Marzano sul Sarno) to investigate key lifestyle and dietary habits. RESULTS: A total of 703 of participants (8.7%) reported a history of malignancy. Alarmingly, 30.5% declared to be a current smoker, while 78.8% did not report any kind of physical activity. Encouragingly, 64.5% declared to be abstemious, and 83.0% declared to consume fruit and vegetables every day, while 4.7% and 31.9% declared not to consume meat and fried food, respectively, at any time. Never-consumers of fruit and vegetables had higher odds of having a history of colorectal cancer (OR = 5.01; 95% CI = 1.46-17.15; p = 0.01). CONCLUSIONS: The PREVES study has served to prove the validity of an operational model allowing to integrate hospital and territorial healthcare services, which we expect to be applied at a larger scale. Key information regarding dietary and lifestyle habits of the investigated population was obtained. Larger studies conducted using more accurate approaches to investigate diet, such as 24-h recalls and food frequency questionnaires, are warranted.

Statistical analysis and first-passage-time applications of a lognormal diffusion process with multi-sigmoidal logistic mean.

We consider a lognormal diffusion process having a multisigmoidal logistic mean, useful to model the evolution of a population which reaches the maximum level of the growth after many stages. Referring to the problem of statistical inference, two procedures to find the maximum likelihood estimates of the unknown parameters are described. One is based on the resolution of the system of the critical points of the likelihood function, and the other is on the maximization of the likelihood function with the simulated annealing algorithm. A simulation study to validate the described strategies for finding the estimates is also presented, with a real application to epidemiological data. Special attention is also devoted to the first-passage-time problem of the considered diffusion process through a fixed boundary.

Childbirth Care among SARS-CoV-2 Positive Women in Italy.

The new coronavirus emergency spread to Italy when little was known about the infection's impact on mothers and newborns. This study aims to describe the extent to which clinical practice has protected childbirth physiology and preserved the mother-child bond during the first wave of the pandemic in Italy. A national population-based prospective cohort study was performed enrolling women with confirmed SARS-CoV-2 infection admitted for childbirth to any Italian hospital from 25 February to 31 July 2020. All cases were prospectively notified, and information on peripartum care (mother-newborn separation, skin-to-skin contact, breastfeeding, and rooming-in) and maternal and perinatal outcomes were collected in a structured form and entered in a web-based secure system. The paper describes a cohort of 525 SARS-CoV-2 positive women who gave birth. At hospital admission, 44.8% of the cohort was asymptomatic. At delivery, 51.9% of the mothers had a birth support person in the delivery room; the average caesarean section rate of 33.7% remained stable compared to the national figure. On average, 39.0% of mothers were separated from their newborns at birth, 26.6% practised skin-to-skin, 72.1% roomed in with their babies, and 79.6% of the infants received their mother's milk. The infants separated and not separated from their SARS-CoV-2 positive mothers both had good outcomes. At the beginning of the pandemic, childbirth raised awareness and concern due to limited available evidence and led to "better safe than sorry" care choices. An improvement of the peripartum care indicators was observed over time.

Generalized Entropies, Variance and Applications.

The generalized cumulative residual entropy is a recently defined dispersion measure. In this paper, we obtain some further results for such a measure, in relation to the generalized cumulative residual entropy and the variance of random lifetimes. We show that it has an intimate connection with the non-homogeneous Poisson process. We also get new expressions, bounds and stochastic comparisons involving such measures. Moreover, the dynamic version of the mentioned notions is studied through the residual lifetimes and suitable aging notions. In this framework we achieve some findings of interest in reliability theory, such as a characterization for the exponential distribution, various results on k-out-of-n systems, and a connection to the excess wealth order. We also obtain similar results for the generalized cumulative entropy, which is a dual measure to the generalized cumulative residual entropy.

Metabolic changes in follicular fluids of patients treated with recombinant versus urinary human chorionic gonadotropin for triggering ovulation in assisted reproductive technologies: a metabolomics pilot study.

INTRODUCTION: The main goal of this retrospective cohort study is the assessment of the effects of administration of recombinant-hCG (r-hCG) versus urinary-hCG (u-hCG) on follicular fluid (FF) composition of women who underwent in vitro fertilization (IVF) treatments. MATERIALS AND METHODS: We selected 70 patients with infertility attributable to tubal diseases, unexplained infertility, and male factor. Metabolomics analysis of their FFs was performed by 1H nuclear magnetic resonance (1H NMR) spectroscopy in combination with multivariate analysis to interpret the spectral data. Univariate statistical analysis was applied to investigate the possible correlations between clinical parameters and between clinical parameters and metabolites identified by NMR. RESULTS: According to the type of hCG used, significant differences were detected in FFs of women with male factor and unexplained infertility, both in qualitative and quantitative terms, for some metabolites as cholesterol, citrate, creatine, ß-hydroxybutyrate, glycerol, lipids, amino acids (Glu, Gln, His, Val, Lys) and glucose. No significant difference was observed in women with tubal diseases. Besides, the number of MII oocytes in the u-hCG-treated groups correlates positively with glutamate in tubal disease and with glycerol in unexplained infertility. In the r-hCG-treated groups, the number of MII oocytes correlates positively with lipid in tubal disease, positively with citrate and negatively with glucose in male infertility. CONCLUSIONS: Metabolite composition of FF changes according to different type of hCG treatment and this can be related to oocyte development and subsequent outcome. According to the data of this study, different types of hCG should be used in relation to the diagnosis of infertility to obtain better results in inducing oocyte maturation in women undergoing IVF.

Probabilistic analysis of systems alternating for state-dependent dichotomous noise.

Aiming to construct a simple stochastic model able to describe systems alternating due to state-dependent dichotomous noise, we consider a generalized telegraph process whose sample-paths fluctuates around the zero state. Indeed, the latter process describes the motion of a particle on the real line, which is characterized by constant velocities and state-dependent intensities that vanish when the motion is toward the origin. This assumption allows to adopt an approach based on renewal theory to obtain formal expressions of the forward and backward transition densities of the process. The special case when certain random times of the motion possess gamma distribution leads to closed-form expressions of the transition densities, given in terms of the generalized Mittag-Leffler function. We also analyze a first-passage-time problem for the considered process in the presence of two constant boundaries.

NMR metabolic profiling of follicular fluid for investigating the different causes of female infertility: a pilot study.

INTRODUCTION: Several metabolomics studies have correlated follicular fluid (FF) metabolite composition with oocyte competence to fertilization, embryo development and pregnancy but there is a scarcity of research examining the metabolic effects of various gynaecological diseases. OBJECTIVES: In this study we aimed to analyze and correlate the metabolic profile of FF from women who were following in vitro fertilization (IVF) treatments with their different infertility pathologies. METHODS: We selected 53 women undergoing IVF who were affected by: tubal diseases, unexplained infertility, endometriosis, polycystic ovary syndrome (PCOS). FF of the study participants was collected at the time of oocytes retrieval. Metabolomic analysis of FF was performed by nuclear magnetic resonance (NMR) spectroscopy. RESULTS: FF presents some significant differences in various infertility pathologies. Although it was not possible to discriminate between FF of control participants and women with tubal diseases and unexplained infertility, comparison of FF metabolic profile from control women with patients with endometriosis and PCOS revealed significant differences in some metabolites that can be correlated to the causes of infertility. CONCLUSION: NMR-based metabolic profiling may be successfully applied to find diagnostic biomarkers for PCOS and endometriosis and it might be also used to predict oocyte developmental potential and subsequent outcome.

NMR metabolomics study of follicular fluid in women with cancer resorting to fertility preservation.

PURPOSE: The purpose of this study was to evaluate the possible application of metabolomics to identify follicular fluid changes in cancer patients undergoing fertility preservation. Although metabolomics have been applied already in cancer studies, this is the first application on follicular fluid of cancer patients. METHODS: We selected for the study ten patients with breast cancer and lymphoma who resorted to oocyte cryopreservation to preserve fertility and ten healthy women undergoing in vitro fertilization treatments. Follicular fluid was collected at the time of oocytes retrieval. Metabolomic analysis of follicular fluids was performed by 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate analysis to interpret the spectral data. Univariate statistical analysis was applied to find correlations between patients' features and metabolites identified by NMR. RESULTS: Partial least squares discriminant analysis allowed to discriminate samples from cancer patients and healthy controls. Univariate statistical analysis found significant correlations between patients' features and metabolites identified by NMR. This finding allowed to identify biomarkers to differentiate both healthy controls from cancer patients and the two different classes of oncological patients. CONCLUSION: The follicular fluids of cancer patients display significant metabolic alterations in comparison to healthy subjects. NMR-based metabolomics could be a valid prognostic tool for identifying and selecting the best cryopreserved oocytes and improving the outcome prediction in cancer women undergoing in vitro fertilization.

Analysis of a growth model inspired by Gompertz and Korf laws, and an analogous birth-death process.

We propose a new deterministic growth model which captures certain features of both the Gompertz and Korf laws. We investigate its main properties, with special attention to the correction factor, the relative growth rate, the inflection point, the maximum specific growth rate, the lag time and the threshold crossing problem. Some data analytic examples and their performance are also considered. Furthermore, we study a stochastic counterpart of the proposed model, that is a linear time-inhomogeneous birth-death process whose mean behaves as the deterministic one. We obtain the transition probabilities, the moments and the population ultimate extinction probability for this process. We finally treat the special case of a simple birth process, which better mimics the proposed growth model.

Preface for the special issue of Mathematical Biosciences and Engineering, BIOCOMP 2012.

The International Conference "BIOCOMP2012 - Mathematical Modeling and Computational Topics in Biosciences'', was held in Vietri sul Mare (Italy), June 4-8, 2012. It was dedicated to the Memory of Professor Luigi M. Ricciardi (1942-2011), who was a visionary and tireless promoter of the 3 previous editions of the BIOCOMP conference series. We thought that the best way to honor his memory was to continue the BIOCOMP program. Over the years, this conference promoted scientific activities related to his wide interests and scientific expertise, which ranged in various areas of applications of mathematics, probability and statistics to biosciences and cybernetics, also with emphasis on computational problems. We are pleased that many of his friends and colleagues, as well as many other scientists, were attracted by the goals of this recent event and offered to contribute to its success.

On a spike train probability model with interacting neural units.

We investigate an extension of the spike train stochastic model based on the conditional intensity, in which the recovery function includes an interaction between several excitatory neural units. Such function is proposed as depending both on the time elapsed since the last spike and on the last spiking unit. Our approach, being somewhat related to the competing risks model, allows to obtain the general form of the interspike distribution and of the probability of consecutive spikes from the same unit. Various results are finally presented in the two cases when the free firing rate function (i) is constant, and (ii) has a sinusoidal form.

Analysis of a stochastic neuronal model with excitatory inputs and state-dependent effects.

We propose a stochastic model for the firing activity of a neuronal unit. It includes the decay effect of the membrane potential in absence of stimuli, and the occurrence of time-varying excitatory inputs governed by a Poisson process. The sample-paths of the membrane potential are piecewise exponentially decaying curves with jumps of random amplitudes occurring at the input times. An analysis of the probability distributions of the membrane potential and of the firing time is performed. In the special case of time-homogeneous stimuli the firing density is obtained in closed form, together with its mean and variance.

Input-output behaviour of a model neuron with alternating drift.

The input-output behaviour of the Wiener neuronal model subject to alternating input is studied under the assumption that the effect of such an input is to make the drift itself of an alternating type. Firing densities and related statistics are obtained via simulations of the sample-paths of the process in the following three cases: the drift changes occur during random periods characterised by (i) exponential distribution, (ii) Erlang distribution with a preassigned shape parameter, and (iii) deterministic distribution. The obtained results are compared with those holding for the Wiener neuronal model subject to sinusoidal input.