On behalf of all authors, the corresponding author states that there is no conflict of interest. ABSTRACT Purpose:We analyzed the efficacy of a point-of-care ultrasonography protocol, based on a focused multi-organ examination, for the diagnostic process of symptomatic, non-traumatic hypotensive patients in emergency. Methods

Mathematical models are an important tool for neuroscientists. During the last thirty years many papers have appeared on single neuron description and specifically on stochastic Integrate and Fire models. Analytical results have been proved and numerical and simulation methods have been developed for their study. Reviews appeared recently collect the main features of these models but do not focus on the methodologies employed to obtain them. Aim of this paper is to fill this gap by upgrading old reviews on this topic. The idea is to collect the existing methods and the available analytical results for the most common one dimensional stochastic Integrate and Fire models to make them available for studies on networks. An effort to unify the mathematical notations is also made. This review is divided in two parts:1. Derivation of the models with the list of the available closed forms expressions for their characterization; 2. Presentation of the existing mathematical and statistical methods for the study of these models.

Women with a diagnosis of breast cancer are at increased risk of second primary cancers, and the identification of risk factors for the latter may have clinical implications. We have followed‐up for 11 years 10,045 women with invasive breast cancer from a European cohort, and identified 492 second primary cancers, including 140 contralateral breast cancers. Expected and observed cases and Standardized Incidence Ratios (SIR) were estimated using Aalen‐Johansen Markovian methods. Information on various risk factors was obtained from detailed questionnaires and anthropometric measurements. Cox proportional hazards regression models were used to estimate the role of risk factors. Women with breast cancer had a 30% excess risk for second malignancies (95% confidence interval—CI 18–42) after excluding contralateral breast cancers. Risk was particularly elevated for colorectal cancer (SIR, 1.71, 95% CI 1.43–2.00), lymphoma (SIR 1.80, 95% CI 1.31–2.40), melanoma (2.12; 1.63–2.70), endometrium (2.18; 1.75–2.70) and kidney cancers (2.40; 1.57–3.52). Risk of second malignancies was positively associated with age at first cancer, body mass index and smoking status, while it was inversely associated with education, post‐menopausal status and a history of full‐term pregnancy. We describe in a large cohort of women with breast cancer a 30% excess of second primaries. Among risk factors for breast cancer, a history of full‐term pregnancy was inversely associated with the risk of second primary cancer.

Excessive calorie intake and physical inactivity are considered key determinants of the rapid worldwide increase in obesity prevalence, however the relationship between diet and weight gain is complex. We investigated associations between adherence to a Mediterranean diet and long-term changes in weight and waist circumference in volunteers recruited to the Italian section of the prospective European Prospective Investigation into Cancer and Nutrition (EPIC). We investigated 32,119 cohort members who provided anthropometric measures at recruitment and updated information on recall a mean of 12 years later. Adherence to a Mediterranean diet was assessed using the Italian Mediterranean Index (score range 0–11). Associations between index score and weight and waist changes were assessed by multivariate linear regression models. Risks of developing overweight/obesity and abdominal obesity were investigated by multivariate logistic models. Increasing Italian Mediterranean Index score (indicating better adherence) was associated with lower 5-year weight change in volunteers of normal weight at baseline (β −0.12, 95% CI −0.16 to −0.08 for 1 tertile increase in score), but not in those overweight/obese at baseline (P interaction between Index score and BMI 0.0001). High adherence was also associated with reduced risk of becoming overweight/obese (OR 0.91, 95% CI 0.84–0.99 third vs. first tertile); smaller 5-year change in waist circumference (β −0.09, 95% CI −0.14 to −0.03 for 1 tertile increase in score); and lower risk of abdominal obesity (OR 0.91, 95% CI 0.84–0.99 third vs. first tertile). Adherence to a traditional Italian Mediterranean diet may help prevent weight gain and abdominal obesity.

Objectives: To evaluate the usefulness of diffusion-weighted magnetic resonance for distinguishing thymomas according to WHO and Masaoka-Koga classifications and in predicting disease-free survival (DFS) by using the apparent diffusion coefficient (ADC). Methods:Forty-one patients were grouped based on WHO (low-risk vs. high-risk) and MasaokaKoga (early vs. advanced) classifications. For prognosis, seven patients with recurrence at followup were grouped separately from healthy subjects. Differences on ADC levels between groups were tested using Student-t testing. Logistic regression models and areas under the ROC curve (AUROC) were estimated. Results:Mean ADC values were different between groups of WHO (low-risk=1.58±0.20×10-3mm2/sec; high-risk=1.21±0.23×10-3mm2/sec; p<0.0001) and Masaoka-Koga (early=1.43±0.26×10-3mm2/sec; advanced=1.31±0.31×10-3mm2/sec; p=0.016) classifications.Mean ADC of type-B3 (1.05±0.17×10-3mm2/sec) was lower than type-B2 (1.32±0.20×10-3mm2/sec; p=0.023). AUROC in discriminating groups was 0.864 for WHO classification (cutpoint=1.309×10-3mm2/sec; accuracy=78.1 %) and 0.730 for Masaoka-Koga classification (cut-point=1.243×10-3mm2/sec; accuracy=73.2 %). Logistic regression models and two-way ANOVA were significant for WHOclassification (odds ratio[OR]=0.93, p=0.007; p<0.001), but not for Masaoka-Koga classification (OR=0.98, p=0.31; p=0.38). ADC levels were significantly associated with DFS recurrence rate being higher for patients with ADC≤1.299× 10-3mm2/sec (p=0.001; AUROC, 0.834; accuracy=78.0 %).Conclusions: ADC helps to differentiate high-risk from lowrisk thymomas and discriminates the more aggressive type-B3. Primary tumour ADC is a prognostic indicator of recurrence. Key Points• DW-MRI is useful in characterizing thymomas and in predicting disease-free survival.• ADC can differentiate low-risk from high-risk thymomas based on different histological composition • The cutoff-ADC-value of 1.309×10-3mm2/sec is proposed as optimal cut-point for this differentiation • The ADC ability in predicting Masaoka-Koga stage is uncertain and needs further validations• ADC has prognostic value on disease-free survival and helps in stratification of risk