A simple PSA-based computational approach predicts the timing of cancer relapse in prostatectomized patients

Abstract Recurrences of prostate cancer affect approximately one quarter of patients who have undergone radical prostatectomy. Reliable factors to predict time to relapse in specific individuals are lacking. Here, we present a mathematical model that evaluates a biologically sensible parameter (α) that can be estimated by the available follow-up data, in particular by the PSA series. This parameter is robust and highly predictive for the time to relapse, also after administration of adjuvant androgen deprivation therapies. We present a practical computational method based on the collection of only four postsurgical PSA values. This study offers a simple tool to predict prostate cancer relapse. Cancer Res; 76(17); 4941–7. ©2016 AACR.</jats:p

Credit achievement ability during distance learning era: the case of Statistics in Medicine course

AIM In this study, the effects of the DL on academic career were investigated. BACKGROUND Distance Learning (DL) became mandatory in Italy from March 2020, due to COVID19 emergency. DESIGN The performances of students in Medical Statistics course of the Nursing degree in three campus of the University of Turin (Aosta, Beinasco and Cuneo) in the Academic Years 2019-2020 and 2020-2021 were considered. METHODS The study is based on 308 students, 48% of whom both attended the lessons and took the exams in DL. The effect of DL on student’s performance was evaluated using Logistic regression models and the results are showed in terms of odds ratios adjusted for gender, age and campus. RESULTS The results show that DL did not bring particular limitations to the students, highlighting on the contrary evident benefits in terms of organization and management of lessons and exams. Moreover, the level of students’ satisfaction at the end of the course increased in DL. CONCLUSION DL seems to do not affect the student’s ability on achieve credits, at least in mathematical subjects. More investigations are needed considering all courses’ types

A new numerical method for processing longitudinal data: clinical applications

Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework. Conclusions: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics

A multiscale model for the feto-placental circulation in monochorionic twin pregnancies

AbstractWe developed a mathematical model of monochorionic twin pregnancies to simulate both the normal gestation and the Twin-Twin Transfusion Syndrome (TTTS), a disease in which the interplacental anastomose create a flow imbalance, causing one of the twin to receive too much blood and liquids, becoming hypertensive and polyhydramnios (the Recipient) and the other to become hypotensive and oligohydramnios (the Donor). This syndrome, if untreated, leads almost certainly to death one or both twins.We propose a compartment model to simulate the flows between the placenta and the fetuses and the accumulation of the amniotic fluid in the sacs. The aim of our work is to provide a simple but realistic model of the twins-mother system and to stress it by simulating the pathological cases and the related treatments, i.e. aminioreduction (elimination of the excess liquid in the recipient sac), laser therapy (removal of all the anastomoses) and other possible innovative therapies impacting on pressure and flow parameters

Raggiungimento dei crediti formativi nell’era della DaD: il caso del corso di Statistica Medica

La Didattica a Distanza (DaD) è diventata obbligatoria in Italia da marzo 2020, a causa dell’emergenza COVID19. In questo studio desideravamo quindi analizzare gli effetti della DaD sulle carriere accademiche degli studenti, in particolare sulla loro capacità di superare gli esami. In questo studio pilota, sono state analizzate le performance degli studenti del corso di Statistica Medica del Corso di Laurea in Infermieristica in tre sedi dell'Università degli Studi di Torino (Cuneo, Aosta e Beinasco) negli Anni Accademici 2019-2020 e 2020-2021. Lo studio si basa su 308 studenti, il 48% dei quali ha frequentato sia le lezioni che gli esami in DaD. L'effetto del DaD sulle prestazioni degli studenti è stato valutato utilizzando la regressione logistica modelli; i risultati sono mostrati in termini di Odds Ratio (OR) aggiustati per sesso, età e sede. I risultati mostrano che la DaD non ha portato particolari limitazioni agli studenti, evidenziando al contrario evidenti benefici in termini di organizzazione e gestione di lezioni ed esami. Inoltre, il livello di soddisfazione degli studenti alla fine del corso è aumentato in DaD. La DaD sembra non intaccare la capacità dello studente di conseguire crediti, almeno in materie matematiche. Sono necessarie ulteriori indagini considerando tutti i tipi di corsi

Magnetic nanoparticles in the central nervous system: Targeting principles, applications and safety issues

One of the most challenging goals in pharmacological research is overcoming the Blood Brain Barrier (BBB) to deliver drugs to the Central Nervous System (CNS). The use of physical means, such as steady and alternating magnetic fields to drive nanocarriers with proper magnetic characteristics may prove to be a useful strategy. The present review aims at providing an up-to-date picture of the applications of magnetic-driven nanotheranostics agents to the CNS. Although well consolidated on physical ground, some of the techniques described herein are still under investigation on in vitro or in silico models, while others have already entered in—or are close to—clinical validation. The review provides a concise overview of the physical principles underlying the behavior of magnetic nanoparticles (MNPs) interacting with an external magnetic field. Thereafter we describe the physiological pathways by which a substance can reach the brain from the bloodstream and then we focus on those MNP applications that aim at a nondestructive crossing of the BBB such as static magnetic fields to facilitate the passage of drugs and alternating magnetic fields to increment BBB permeability by magnetic heating. In conclusion, we briefly cite the most notable biomedical applications of MNPs and some relevant remarks about their safety and potential toxicity