Factors Associated with Primary Liver Cancer Survival in a Southern Italian Setting in a Changing Epidemiological Scenario.

A retrospective observational study utilising cancer incidence data from a population-based registry investigated determinants affecting primary liver cancer survival in a southern Italian region with high hepatitis viral infection rates and obesity prevalence. Among 2687 patients diagnosed between 2006 and 2019 (65.3% male), a flexible hazard-based regression model revealed factors influencing 5-year survival rates. High deprivation levels [HR = 1.41 (95%CI = 1.15-1.76); p < 0.001], poor access to care [HR = 1.99 (95%IC = 1.70-2.35); p < 0.0001], age between 65 and 75 [HR = 1.48 (95%IC = 1.09-2.01); p < 0.05] or >75 [HR = 2.21 (95%CI = 1.62-3.01); p < 0.0001] and residing in non-urban areas [HR = 1.35 (95%CI = 1.08-1.69); p < 0.01] were associated with poorer survival estimates. While deprivation appeared to be a risk factor for primary liver cancer patients residing within the urban area, the geographic distance from specialised treatment centres emerged as a potential determinant of lower survival estimates for residents in the non-urban areas. After balancing the groups of easy and poor access to care using a propensity score approach, poor access to care and a lower socioeconomic status resulted in potentially having a negative impact on primary liver cancer survival, particularly among urban residents. We emphasise the need to interoperate cancer registries with other data sources and to deploy innovative digital solutions to improve cancer prevention.

Implant replacement and anaplastic large cell lymphoma associated with breast implants: a quantitative analysis.

Introduction: Breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) is a rare form of non-Hodgkin T-cell lymphoma associated with breast reconstruction post-mastectomy or cosmetic-additive mammoplasty. The increasing use of implants for cosmetic purposes is expected to lead to an increase in BIA-ALCL cases. This study investigated the main characteristics of the disease and the factors predicting BIA-ALCL onset in patients with and without an implant replacement. Methods: A quantitative analysis was performed by two independent researchers on cases extracted from 52 primary studies (case report, case series, and systematic review) published until April 2022 and searched in PubMed, Scopus, and Google-Scholar databases using "Breast-Implant" AND/OR "Associated" AND/OR "Anaplastic-Large-Cell-Lymphoma". The statistical significance was verified by Student's t-test for continuous variables, while Fisher's exact test was applied for qualitative variables. Cox model with time-dependent covariates was used to estimate BIA-ALCL's onset time. The Kaplan-Meier model allowed the estimation of the probability of survival after therapy according to breast implant exposure time. Results: Overall, 232 patients with BIA-ALCL were extracted. The mean age at diagnosis was 55 years old, with a mean time to disease onset from the first implant of 10.3 years. The hazard of developing BIA-ALCL in a shorter time resulted significantly higher for patients not having an implant replacement (hazard ratio = 0.03; 95%CI: 0.005-0.19; p-value < 0.01). Patients with implant replacement were significantly older than patients without previous replacement at diagnosis, having a median time to diagnosis since the first implant of 13 years (7 years in patients without replacement); anyway, the median time to BIA-ALCL occurrence since the last implantation was equal to 5 years. Discussion: Our findings suggest that, in BIA-ALCL patients, the implant substitution and/or capsulectomy may delay the disease's onset. However, the risk of reoccurrence in an earlier time should be considered in these patients. Moreover, the time to BIA-ALCL onset slightly increased with age. Selection bias, lack of awareness, misdiagnosis, and limited data availability could be identified as limits of our study. An implant replacement should be considered according to a risk stratification approach to delay the BIA-ALCL occurrence in asymptomatic patients, although a stricter follow-up after the implant substitution should be recommended. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO, identifier: CRD42023446726.

A new measure for the attitude to mobility of Italian students and graduates: a topological data analysis approach.

Students' and graduates' mobility is an interesting topic of discussion especially for the Italian education system and universities. The main reasons for migration and for the so called brain drain, can be found in the socio-economic context and in the famous North-South divide. Measuring mobility and understanding its dynamic over time and space are not trivial tasks. Most of the studies in the related literature focus on the determinants of such phenomenon, in this paper, instead, combining tools coming from graph theory and Topological Data Analysis we propose a new measure for the attitude to mobility. Each mobility trajectory is represented by a graph and the importance of the features constituting the graph are evaluated over time using persistence diagrams. The attitude to mobility of the students is then ranked computing the distance between the individual persistence diagram and the theoretical persistence diagram of the stayer student. The new approach is used for evaluating the mobility of the students that in 2008 enrolled in an Italian university. The relation between attitude to mobility and the main socio-demographic variables is investigated.

Isotonic regression for metallic microstructure data: estimation and testing under order restrictions.

Investigating the main determinants of the mechanical performance of metals is not a simple task. Already known physically inspired qualitative relations between 2D microstructure characteristics and 3D mechanical properties can act as the starting point of the investigation. Isotonic regression allows to take into account ordering relations and leads to more efficient and accurate results when the underlying assumptions actually hold. The main goal in this paper is to test order relations in a model inspired by a materials science application. The statistical estimation procedure is described considering three different scenarios according to the knowledge of the variances: known variance ratio, completely unknown variances, and variances under order restrictions. New likelihood ratio tests are developed in the last two cases. Both parametric and non-parametric bootstrap approaches are developed for finding the distribution of the test statistics under the null hypothesis. Finally an application on the relation between geometrically necessary dislocations and number of observed microstructure precipitations is shown.

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel.

This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 steel microstructures consisting of a ferritic matrix and increasing volume fractions of M23C6 carbides. The parameters of the two models were interpreted in terms of carbide volume fractions and texture using linear mixed-effects models.