Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?

The association between genetics and lifestyle factors is crucial when determining breast cancer susceptibility, a leading cause of deaths globally. This research aimed to compare the body mass index, smoking behavior, hormonal influences, and BRCA gene mutations between affected patients and healthy individuals, all with a family history of cancer. All these factors were then utilized as features to train a machine learning (ML) model to predict the risk of breast cancer development. Between 2020 and 2023, a total of 1389 women provided detailed lifestyle and risk factor data during visits to a familial cancer center in Italy. Descriptive and inferential statistics were assessed to explore the differences between the groups. Among the various classifiers used, the ensemble of decision trees was the best performer, with a 10-fold cross-validation scheme for training after normalizing the features. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside the accuracy, sensitivity, specificity, precision, and F1 score. Analysis revealed that individuals in the tumor group exhibited a higher risk profile when compared to their healthy counterparts, particularly in terms of the lifestyle and genetic markers. The ML model demonstrated predictive power, with an AUC of 81%, 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84. These metrics significantly outperformed traditional statistical prediction models, including the BOADICEA and BCRAT, which showed an AUC below 0.65. This study demonstrated the efficacy of an ML approach in identifying women at higher risk of breast cancer, leveraging lifestyle and genetic factors, with an improved predictive performance over traditional methods

Vesicular and non-vesicular transport feed distinct glycosylation pathways in the Golgi.

Newly synthesized proteins and lipids are transported across the Golgi complex via different mechanisms whose respective roles are not completely clear. We previously identified a non-vesicular intra-Golgi transport pathway for glucosylceramide (GlcCer)--the common precursor of the different series of glycosphingolipids-that is operated by the cytosolic GlcCer-transfer protein FAPP2 (also known as PLEKHA8) (ref. 1). However, the molecular determinants of the FAPP2-mediated transfer of GlcCer from the cis-Golgi to the trans-Golgi network, as well as the physiological relevance of maintaining two parallel transport pathways of GlcCer--vesicular and non-vesicular--through the Golgi, remain poorly defined. Here, using mouse and cell models, we clarify the molecular mechanisms underlying the intra-Golgi vectorial transfer of GlcCer by FAPP2 and show that GlcCer is channelled by vesicular and non-vesicular transport to two topologically distinct glycosylation tracks in the Golgi cisternae and the trans-Golgi network, respectively. Our results indicate that the transport modality across the Golgi complex is a key determinant for the glycosylation pattern of a cargo and establish a new paradigm for the branching of the glycosphingolipid synthetic pathwa

Ten years of Ana: lessons from a transdisciplinary body of literature on online pro-eating disorder websites

This paper offers a methodical review of the scientific literature of the last decade that concerns itself with online services offering supportive advocacy for anorexia nervosa and bulimia nervosa (‘pro-ana’ and ‘pro-mia’). The main question is whether these studies reproduce the traditional divide in the study of eating disorders, between clinical and social science perspectives, with limited mutual exchanges. Having first identified a specific body of literature, the authors investigate its content, methods and approaches, and analyse the network of cross-citations the components generate and share. On this basis, the authors argue that the scientific literature touching on pro-ana websites can be regarded as a single transdisciplinary body of knowledge. What’s more, they show that the literature on computermediated sociabilities centred on eating disorders displays different structural characteristics with respect to the traditional, non-Web-related research on eating disorders. In the latter, the social sciences have usually provided a critical counterpoint to the development of a health sciences mainstream. In the case of Web-related research, however, the social sciences have taken the lead role in defining the field, with the health sciences following suit

Engineering polymeric nanosystems against oral diseases

Nanotechnology and nanoparticles (NPs) are at the forefront of modern research, par-ticularly in the case of healthcare therapeutic applications. Polymeric NPs, specifically, hold high promise for these purposes, including towards oral diseases. Careful optimisation of the production of polymeric NPs, however, is required to generate a product which can be easily translated from a laboratory environment to the actual clinical usage. Indeed, considerations such as biocompati-bility, biodistribution, and biodegradability are paramount. Moreover, a pre-clinical assessment in adequate in vitro, ex vivo or in vivo model is also required. Last but not least, considerations for the scale-up are also important, together with an appropriate clinical testing pathway. This review aims to eviscerate the above topics, sourcing at examples from the recent literature to put in context the current most burdening oral diseases and the most promising polymeric NPs which would be suitable against them

Engineering Polymeric Nanosystems against Oral Diseases

Nanotechnology and nanoparticles (NPs) are at the forefront of modern research, particularly in the case of healthcare therapeutic applications. Polymeric NPs, specifically, hold high promise for these purposes, including towards oral diseases. Careful optimisation of the production of polymeric NPs, however, is required to generate a product which can be easily translated from a laboratory environment to the actual clinical usage. Indeed, considerations such as biocompatibility, biodistribution, and biodegradability are paramount. Moreover, a pre-clinical assessment in adequate in vitro, ex vivo or in vivo model is also required. Last but not least, considerations for the scale-up are also important, together with an appropriate clinical testing pathway. This review aims to eviscerate the above topics, sourcing at examples from the recent literature to put in context the current most burdening oral diseases and the most promising polymeric NPs which would be suitable against them

Health assessment of road bridges with Gerber saddles: Non-linear planar models

A correct assessment of the structural behavior and performance of deteriorated structures is crucial both for a proper planning of maintenance works and for a correct design of new structures in aggressive environments. The aim of this work is to investigate the response of Gerber saddles to the variation of the two main parameters representing typical damaged scenarios: the concrete cover and the diameter of the steel reinforcements. The analyses are conducted through Finite Element two-dimensional non-linear models based on the compatible stress field; the results are compared with the relevant 'Strut and Tie' models. At first, parametric analyses are carried out, in order to evaluate the sensitivity of load capacity to the position of steel rebars and to the dimension of concrete cover; this first part of the contribution aims to highlight the importance of an adequate level of knowledge. Then, the contribution focuses on the reduction of saddle capacity due to degradation conditions, referred to both the loss of concrete cover and the corrosion of reinforcing bars. The analyses closes with a comparison among the results provided by the two-dimensional investigations and the ones provided by the relevant 'Strut and Tie' models

Interactive effects of long-Term exposure to air pollutants on SARS-CoV-2 infection and severity: A northern Italian population-based cohort study

Background We examined interactions, to our knowledge not yet explored, between long-Term exposures to particulate matter (PM10) with nitrogen dioxide (NO2) and ozone (O3) on SARS-CoV-2 infectivity and severity. Methods We followed 709,864 adult residents of Varese Province from 1 February 2020 until the first positive test, COVID-19 hospitalization, or death, up to 31 December 2020. We estimated residential annual means of PM10, NO2and O3in 2019 from chemical-Transport and random-forest models. We estimated interactive effects of pollutants with urbanicity on SARS-CoV-2 infectivity, hospitalization, and mortality endpoints using Cox regression models adjusted for socio-demographic factors and comorbidities, and additional cases due to interactions using Poisson models. Results 41,065 individuals were infected, 5,203 were hospitalized and 1,543 died from COVID-19 during follow-up. Mean PM10was 1.6 times higher and NO22.6 times higher than WHO limits, with wide gradients between urban and non-urban areas. PM10and NO2were positively associated with SARS-CoV-2 infectivity and mortality, and PM10with hospitalizations in urban areas. Interaction analyses estimated that the effect of PM10(per 3.5 μg/m3) on infectivity was strongest in urban areas (HR=1.12, 95%CI:1.09-1.16), corresponding to 854 additional cases per 100,000 person-years, and in areas at high NO2co-exposure (HR=1.15, 1.08-1.22). At higher levels of PM10co-exposure the protective association of ozone reversed (HR=1.32, 1.17-1.49), yielding to 278 additional cases per μg/m3increase in O3. We estimated similar interactive effects for severity endpoints. Conclusions We estimate that interactive effects between pollutants exacerbated the burden of SARS-CoV-2 pandemic in urban areas