Computer simulations of cardiovascular flows can be key to improving the predicting capabilities of standard diagnostic tools, to refine surgical techniques and perform virtual tests of innovative prosthetic devices. The reliability of simulations, however, depends on the fidelity level of the model, which, for the heart, involves the interconnected multiphysics dynamics of the various systems: the human heart is among the most complex organs, and simulating its dynamics is an ambitious undertaking from both the modeling and computational viewpoints. In this paper we present a multiphysics computational model of the human heart accounting simultaneously for the electrophysiology, the elasto-mechanics, and the hemodynamics, including their multiway coupled interactions referred to as fluid-structure-electro interaction (FSEI). The developed tool embodies accuracy, versatility, and computational efficiency, thus allowing cardiovascular simulations of physiologic and pathologic configurations within a time to solution compatible with the clinical practice and without resorting to large-scale supercomputers. Results are shown for healthy conditions and for myocardial infarction with the aim of assessing the reliability of the model and proving its predicting capabilities, which could be used to anticipate the outcome of surgical procedures or support clinical decisions.

INTRODUCTION: Adaptive behaviors at mealtime, esophageal symptom-specific anxiety and hypervigilance may affect dysphagia reporting in patients with eosinophilic esophagitis (EoE), but this has not been investigated. Moreover, the relationship between such confounding factors and histological disease activity (HDA) is unclear. METHODS: This was a prospective study on adults with EoE. Dysphagia, anxiety, and hypervigilance were assessed using specific questionnaires (i.e., modified dysphagia symptom questionnaire [mDSQ], Dysphagia Symptom Score [DSS], and Esophageal Hypervigilance and Anxiety Scale). Adaptive behaviors were assessed using the Pisa EoE Adaptation Questionnaire. Appropriate statistics was used to investigate correlation between dysphagia, anxiety, hypervigilance, adaptive behaviors, and HDA. RESULTS: Ninety-five patients were included. Esophageal anxiety, hypervigilance, and use of adaptive behaviors were found in about 50% of patients with EoE. Esophageal anxiety and hypervigilance were significantly higher (P = 0.03 for both), and adaptive behaviors were significantly more prevalent in histologically active EoE compared with EoE in remission (76.8% vs 25.6%, P < 0.001). As a standalone measurement, mDSQ and DSS had area under the receiver operating characteristic curve of 77.7% and 75.3% for predicting HDA. Adjustments of mDSQ and DSS based on individual Esophageal Hypervigilance and Anxiety Scale scores and adaptive behaviors at mealtime significantly improved the area under the receiver operating characteristic curve of mDSQ and DSS to 86.6% and 84.3%, respectively (P < 0.05 for both). DISCUSSION: Use of adaptive behaviors, higher esophageal anxiety and hypervigilance are associated with active EoE and represent clinical markers of HDA. Adaptive behaviors provide complementary clinical information that is not detected by symptoms alone. The assessment of adaptive behaviors, anxiety, and hypervigilance improves the correlation between clinical and HDA in EoE.

Modelling the cardiac electrophysiology entails dealing with the uncertainties related to the input parameters such as the heart geometry and the electrical conductivities of the tissues, thus calling for an uncertainty quantification (UQ) of the results. Since the chambers of the heart have different shapes and tissues, in order to make the problem affordable, here we focus on the left ventricle with the aim of identifying which of the uncertain inputs mostly affect its electrophysiology. In a first phase, the uncertainty of the input parameters is evaluated using data available from the literature and the output quantities of interest (QoIs) of the problem are defined. According to the polynomial chaos expansion, a training dataset is then created by sampling the parameter space using a quasi-Monte Carlo method whereas a smaller independent dataset is used for the validation of the resulting metamodel. The latter is exploited to run a global sensitivity analysis with nonlinear variance-based indices and thus reduce the input parameter space accordingly. Thereafter, the uncertainty probability distribution of the QoIs are evaluated using a direct UQ strategy on a larger dataset and the results discussed in the light of the medical knowledge.

Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC $$89.9\%$$89.9 % using 19 features and $$92.1\%$$92.1 % using 7 of them; while from ABVS we attained an AUC-ROC of $$72.3\%$$72.3 % using 22 features and $$85.8\%$$85.8 % using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT $$71.8\%$$71.8 % - $$74.1\%$$74.1 % ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.

Background: Ustekinumab (UST) has demonstrated effectiveness in treating patients with Crohn’s disease. Monitoring treatment response can improve disease management and reduce healthcare costs. We investigated whether UST trough levels (TLs), serum IL22, and Oncostatin M (OSM) levels could be early indicators of non-response by analysing their correlation with clinical and biochemical outcomes in CD. Methods: Patients with CD initiating UST treatment from October 2018 to September 2020 were enrolled at six Italian centres for inflammatory bowel disease (IBD). Clinical and biochemical data were collected at four time points: baseline, second subcutaneous (SC) dose, fourth SC dose, and 52 weeks. TLs were measured during maintenance, at the second SC dose, and at the fourth SC dose. IL-22 and OSM serum levels were assessed at baseline and the second SC dose. We analysed whether TLs, IL22 levels, and OSM serum levels were associated with clinical response, clinical remission, biochemical remission, and endoscopic remission using the appropriate statistical tests. Results: Out of eighty-four initially enrolled patients, five were lost to follow-up, and eleven discontinued the drug before 52 weeks. At the 52-week time point, 47% achieved biochemical remission based on faecal calprotectin levels, and 61.8% achieved clinical remission. TLs at the second SC dose significantly correlated with biochemical remission at the same time point (p = 0.011). However, TLs did not correlate with clinical remission. Baseline OSM levels did not correlate with biochemical or clinical remission or response. IL22 levels notably decreased during UST therapy (p = 0.000), but its values did not correlate with biochemical or clinical remission. Conclusions: UST is an effective therapy for patients with CD. TLs measured at the second SC dose significantly correlated with biochemical remission, emphasising their potential role in treatment monitoring. Levels of OSM and IL-22, despite a significant decrease in the latter during therapy, did not exhibit correlations with clinical or biochemical outcomes in our study. Further studies are needed to confirm these findings.