Cox process representation and inference for stochastic reaction-diffusion processes

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling

The role of metabolic setting in predicting the risk of early tumour relapse of differentiated thyroid cancer (DTC)

Background: The role of insulin resistance and adipocytokines in determining the phenotype and recurrence of differentiated thyroid cancer (DTC) is still unknown. In a previous study, we observed an association between metabolic setting, circulating adipocytokines and thyroid cancer phenotype. The aim of this study was to evaluate the clinical follow-up of patients with DTC and the predictive role of metabolic setting on the risk of tumour relapse. Methods: Between September 2016 and January 2017, 57 patients were admitted to our institution to undergo total thyroidectomy because of suspected DTC. Thirty patients with post-surgical histological diagnosis of DTC were included in the study. Each subject underwent pre-surgical analysis of anthropometric parameters, thyroid function and autoimmunity, glucose metabolism, insulin resistance (HOMA-IR) and levels of unacylated and acylated ghrelin, obestatin, leptin and adiponectin. Tumour recurrence at 1 and 3 years from diagnosis was assessed. Results: Most patients were females (21F, 9M) with a median age at diagnosis of 50.0 (41.0\u201358.8). At baseline, overweight was found in 7 patients and obesity in 6 cases. Insulin resistance was detected in 14 patients. Overall, 17 patients (56.7%) underwent radioiodine treatment after surgery. During the follow-up, we observed a persistent biochemical disease in one patient whereas tumour relapse was found in six patients at 1 year from diagnosis (lymph node metastases) and in one patient at 3 years from diagnosis (lung metastases). Independently from age, sex, stage of disease and the presence of lymph node metastasis at diagnosis, higher BMI, leptin and insulin levels as well as HOMA-IR were associated with a higher risk of tumour relapse (p < 0.05 for all). Conclusions: Our results highlight a possible role for BMI, leptin and insulin resistance as predictors of early DTC relapse

Gestational Diabetes Mellitus: Clinical Characteristics and Perinatal Outcomes in a Multiethnic Population of North Italy

Aim: To evaluate clinical characteristics and perinatal outcomes in a heterogeneous population of Caucasians born in Italy and High Migration Pressure Countries (HMPC) women with GDM living in Piedmont, North Italy. Methods: We retrospectively analyzed data from 586 women referring to our unit (2015-2020). Epidemiological (age and country of origin) and clinical-metabolic features (height, weight, family history of DM, parity, previous history of GDM, OGTT results, and GDM treatment) were collected. The database of certificates of care at delivery was consulted in relation to neonatal/maternal complications (rates of caesarean sections, APGAR score, fetal malformations, and neonatal anthropometry). Results: 43.2% of women came from HMPC; they were younger (p < 0.0001) and required insulin treatment more frequently than Caucasian women born in Italy (χ 2 = 17.8, p=0.007). Higher fasting and 120-minute OGTT levels and gestational BMI increased the risk of insulin treatment (OGTT T0: OR = 1.04, CI 95% 1.016-1.060, p=0.005; OGTT T120: OR = 1.01, CI 95% 1.002-1.020, p=0.02; BMI: OR = 1.089, CI 95% 1.051-1.129, p < 0.0001). Moreover, two or more diagnostic OGTT glucose levels doubled the risk of insulin therapy (OR = 2.03, IC 95% 1.145-3.612, p=0.016). We did not find any association between ethnicities and neonatal/maternal complications. Conclusions: In our multiethnic GDM population, the need for intensive care and insulin treatment is high in HPMC women although the frequency of adverse peripartum and newborn outcomes does not vary among ethnic groups. The need for insulin therapy should be related to different genetic backgrounds, dietary habits, and Nutrition Transition phenomena. Thus, nutritional intervention and insulin treatment need to be tailored