Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures.

The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver's metastatic burden from several acquired images, which can benefit from automatic image segmentation tools. We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis.

An ECG-based machine-learning approach for mortality risk assessment in a large European population.

Aims: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex.

Methods: The study included patients between 40 and 90 years old who underwent ECG recording between January 2008 and October 2022 in the metropolitan area of Modena, Italy. Exclusion criteria established a patient cohort without severe ECG abnormalities, namely, tachyarrhythmias, bradyarrhythmias, Wolff-Parkinson-White syndrome, second- or third- degree AV block, bundle-branch blocks, more than three premature beats, poor signal quality, and presence of pacemakers and implantable cardioverter- defibrillators.

Discrepancy in the Diagnosis of Heart Failure With Preserved Ejection Fraction Between Supine Versus Upright Exercise Hemodynamic Testing.

Background: Invasive exercise right heart catheterization is a gold standard in diagnosing heart failure with preserved ejection fraction (HFpEF). Body positions during the test influence hemodynamics. However, the discrepancy in HFpEF diagnosis between exercise testing in supine versus upright position is unknown.

Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour.

The radiomic analysis of the tissue surrounding colorectal liver metastases (CRLM) enhances the prediction accuracy of pathology data and survival. We explored the variation of the textural features in the peritumoural tissue as the distance from CRLM increases. We considered patients with hypodense CRLMs >10 mm and high-quality computed tomography (CT).

Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective.

Background: Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty.

Purpose: This work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC).

Reconsidering the Impact of Endovascular Repair on Short-Term and Mid-Term Outcomes in Peripheral Arterial Disease: A Retrospective Analysis.

Background: The aim of the study is to compare the short-term and medium-term outcomes in patients who underwent open repair (OR) or endovascular repair (ER) for peripheral arterial disease (PAD) also including stratifications based on severity and year of the first intervention.

Methods: We conducted an observational retrospective single-center cohort study. We evaluated patients with PAD that primarily underwent ER, OR, minor, and major amputations in a single center from 2005 to 2020.

Clustering blood donors via mixtures of product partition models with covariates.

Motivated by the problem of accurately predicting gap times between successive blood donations, we present here a general class of Bayesian nonparametric models for clustering. These models allow for the prediction of new recurrences, accommodating covariate information that describes the personal characteristics of the sample individuals. We introduce a prior for the random partition of the sample individuals, which encourages two individuals to be co-clustered if they have similar covariate values.

Learning Curve Analysis of Robotic-Assisted Mitral Valve Repair with COVID-19 Exogenous Factor: A Single Center Experience.

Renewed interest in robot-assisted cardiac procedures has been demonstrated by several studies. However, concerns have been raised about the need for a long and complex learning curve. In addition, the COVID-19 pandemic in 2020 might have affected the learning curve of these procedures.

Haemodynamic validation of the three-step HFA-PEFF algorithm to diagnose heart failure with preserved ejection fraction.

Aims: The HFA-PEFF algorithm (Heart Failure Association-Pre-test assessment, Echocardiography and natriuretic peptide score, Functional testing in cases of uncertainty, Final aetiology) is a three-step algorithm to diagnose heart failure with preserved ejection fraction (HFpEF). It provides a three-level likelihood of HFpEF: low (score < 2), intermediate (score 2-4), or high (score > 4). HFpEF may be confirmed in individuals with a score > 4 (rule-in approach).

Improving Blood Donor Care in a Collection Center Through Advanced Data Exploitation.

Background: Blood collection centers can take advantage of the huge amount of data collected on donors over the years to predict and detect early the onset of several diseases, However, dedicated tools are needed to carry out these analyses.

Objectives: This work develops a tool that combines available data with predictive tools to provide alerts to physicians and enable them to effectively visualize the history of critical donors in terms of the parameters that led to the alert.

Methods: The developed tool consists of data exchanging functions, interfaces to raise alerts and visualize donor history, and predictive algorithms.

Surgical planning of arteriovenous fistulae in routine clinical practice: A machine learning predictive tool.

Background: Arteriovenous fistula (AVF) is the preferred vascular access (VA) for hemodialysis, but it is associated with high non-maturation and failure rates. Predicting patient-specific AVF maturation and postoperative changes in blood flow volumes (BFVs) and vessel diameters is of fundamental importance to support the choice of optimal AVF location and improve VA survival. The goal of this study was to employ machine learning (ML) in order to give physicians a fast and easy-to-use tool that provides accurate patient-specific predictions, useful to make AVF surgical planning decisions.

A patient-specific image-based approach to estimate pulmonary artery stiffness based on vessel constitutive model.

An accurate non-invasive evaluation of the mechanical properties of the vessel wall is important for a variety of screening protocols and surgical treatments. In this work, we focused on a section of the Pulmonary Artery (PA), and developed a patient-specific approach to estimate its stiffness in terms of the Young's modulus along the circumferential direction (E). First, we developed a patient-specific semi-automatic approach to estimate its expected value and standard deviation.

Automatic cyst and kidney segmentation in autosomal dominant polycystic kidney disease: Comparison of U-Net based methods.

Autosomal Dominant Polycystic Kidney Disease is a genetic disease that causes uncontrolled growth of fluid-filled cysts in the kidney. Kidney enlargement resulting from the expansion of cysts is continuous and often associated with decreased renal function and kidney failure. Mouse and rat models are necessary to discover new drugs able to halt the progression of the disease.

Communication and Decision Support Tool for an In-Hospital 3D Printing Service.

Background: Effective communication is a key factor in healthcare, essential for improving process efficiency and quality of care. This is particularly true in new services, e.g.

Temporary Reperfusion of the Aneurysm Sac as a Prevention of Spinal Cord Ischemia After Endovascular Treatment of Thoracoabdominal Aortic Aneurysm: Systematic Review and Meta-analysis.

Background: Spinal cord ischemia (SCI) is still a feared complication for patients suffering from thoracoabdominal aortic aneurysm (TAAA) who undergo endovascular treatment. The aims of this work are to review the available literature on different reperfusion methods of the aneurysm sac, and to analyze whether the different reperfusion methods, also in combination with other factors, are effective in reducing SCI risk and if the impact varies with the patient's age.

Methods: PubMed/MEDLINE library was searched for studies published until November 2020 concerning TAAA, endovascular repair, and SCI preventive measures.

A discrete-event simulation model for analysing and improving operations in a blood donation centre.

Background And Objectives: Healthcare systems require effective and efficient blood donation supply chains to provide an adequate amount of whole blood and blood components to hospitals and transfusion centres. However, some crucial steps of the chain, for example blood collection, are not adequately studied in the literature. This work analyses the operations in a blood collection centre with the twofold aim of analysing different configurations and evaluating the effectiveness and feasibility of schedules defined at higher planning levels.

Prediction model of isolated iliac and abdominal aneurysms.

Objectives: We analyse the cardiovascular risk factors in patients undergoing screening for Isolated Iliac Aneurysm (IIA) and Abdominal Aortic Aneurysm (AAA) and propose a logistic regression model to indicate patients at risk of IIA and/or AAA.

Methods: A screening programme was carried out to identify the presence of aneurysm based on Duplex scan examination. Cardiovascular risk factors information was collected from each subject.

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching.

A new scheduling problem arising in the home care context is addressed, whose novelty with respect to the literature lies in the way overtime is paid. In this problem, some clients are willing to pay a higher fee to cover the additional overtime cost, if such overtime is incurred because a caregiver works extra time with the client to preserve continuity of care. These overtime hours charged to clients unburden the company, which no longer has to balance between cost and continuity of care in a traditional way.

Optimal Robust Search for Parameter Values of Qualitative Models of Gene Regulatory Networks.

Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement.

A stochastic risk-averse framework for blood donation appointment scheduling under uncertain donor arrivals.

Blood is a key resource in all health care systems, usually drawn from voluntary donors. We focus on the operations management in blood collection centers, which is a key step to guarantee an adequate blood supply and a good quality of service to donors, by addressing the so-called Blood Donation Appointment Scheduling problem. Its goal is to employ appointment scheduling to balance the production of blood units between days, in order to provide a reasonably constant supply to transfusion centers and hospitals, and reduce non-alignments between physicians' working times and donor arrivals at the collection center.

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI.

The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification.

A compliant aortic model for in vitro simulations: Design and manufacturing process.

We design and manufacture a silicone model of the human aorta, able to mimic both the geometrical and the mechanical properties of physiological individuals, with a specific focus on reproducing the compliance. In fact, while the models available in the literature exhibit an unrealistic compliant behavior, though they are detailed from the geometrical viewpoint, here the goal is to provide an accurate compliant tool for in vitro testing the devices that interface with the vascular system. A parametric design of the aortic model is obtained based on the available literature data, and the model is manufactured with a specific silicone mixture using rapid prototyping and molding techniques.

Correction: Changes in aortic pulse wave velocity of four thoracic aortic stent grafts in an ex vivo porcine model.

[This corrects the article DOI: 10.1371/journal.pone.

A Bayesian approach for the identification of patient-specific parameters in a dialysis kinetic model.

Hemodialysis is the most common therapy to treat renal insufficiency. However, notwithstanding the recent improvements, hemodialysis is still associated with a non-negligible rate of comorbidities, which could be reduced by customizing the treatment. Many differential compartment models have been developed to describe the mass balance of blood electrolytes and catabolites during hemodialysis, with the goal of improving and controlling hemodialysis sessions.

A novel insight into the role of entry tears in type B aortic dissection: pressure measurements in an in vitro model.

Introduction: Predicting aortic growth in acute type B dissection is fundamental in planning interventions. Several factors are considered to be growth predictors in the literature and, among them, size and location of entry tears have been recognized to particularly influence the false lumen pressure. In this study, we develop an in vitro setting to analyze the actual impact of size and location of the entry tears on false lumen pressure, in the absence of other confounding factors such as the deformability of the aortic wall.