DECODE: An open-source cloud-based platform for the noninvasive management of peripheral artery disease.

Background And Objective: Peripheral artery disease (PAD) is a progressive vascular condition affecting >237 million individuals worldwide. Accurate diagnosis and patient-specific treatment planning are critical but are often hindered by limited access to advanced imaging tools and real-time analytical support. This study presents DECODE, an open-source, cloud-based platform that integrates artificial intelligence, interactive 3D visualization, and computational modeling to improve the noninvasive management of PAD.

Machine learning-based optimization of the prostate health index for prostate cancer detection.

The Prostate Health Index (PHI) is composed of the prostate-specific antigen (PSA), free PSA (fPSA), and the [-2]pro-PSA isoform (p2PSA). The aim of the study was to modify the PHI using machine learning (ML) and to compare its prognostic performance with traditional PHI for any-, low- and high-grade prostate cancer (PCa) detection on biopsy. To obtain better-balanced data set we used the over-sampling strategy.

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance.

BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being.

Machine learning for improved medical device management: A focus on dialysis machines.

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study.

BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Infusion pumps case study.

BackgroundAnalysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety.ObjectiveThe ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one. This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina.

A parametric study of motion and attachment to capillary walls of circulating tumor cells (CTCs) interacting with non-activated and activated platelets.

Background And Objective: Our previous research examined the biophysical conditions required for CTC arrest using a custom-built solid-fluid 2D axisymmetric computational model. Application of that model resulted in calculating the limiting conditions under which a CTC can pass through a narrowed capillary with a platelet attached to the capillary wall. This paper is a step forward in determining the effects of a selected set of parameters: number of platelets, CTC diameter, stiffness, and ligand-receptor bond stiffness of the CTC (with or without attached platelets) on the CTC trajectory, its axial position, and its attachment to the wall.

Machine learning for improved medical device management: A focus on infant incubators.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance.

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: ECG devices case study.

BackgroundAfter 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment.ObjectiveThis study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices.

Development of a surrogate model for predicting atherosclerotic plaque progression based on agent based modeling data.

BackgroundAtherosclerosis of the coronary arteries is a chronic, progressive condition characterized by the buildup of plaque within the arterial walls. Coronary artery disease (CAD), more specifically coronary atherosclerosis (CATS), is one of the leading causes of death worldwide. Computational modeling frameworks have been used for simulation of atherosclerotic plaque progression and with the advancement of agent-based modeling (ABM) the simulation results became more accurate.

Machine learning for improved medical device management: A focus on defibrillator performance.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) had been in force for 25 years, in 2017 the new MD Regulation (MDR) was introduced. One of the more stringent requirement is a need for better control of MD safety and performance post-market surveillance mechanisms.

Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study.

Introduction: Heart failure (HF) is a complex clinical syndrome. Accurate risk stratification and early diagnosis of HF are challenging as its signs and symptoms are non-specific. We propose to address this global challenge by developing the STRATIFYHF artificial intelligence-driven decision support system (DSS), which uses novel analytical methods in determining the risk, diagnosis and prognosis of HF.

Software for optimized virtual stenting of patient-specific coronary arteries reconstructed from angiography images.

Detection of clinically relevant stenosis within coronary arteries as well as planning of treatment (stent implantation) are important topics in clinical cardiology. In this study a thorough methodology for virtual stenting assistance is proposed, that includes the 3D reconstruction of a patient-specific coronary artery from X-ray angiography images, hemodynamic simulations of blood flow, computation of a fractional flow reserve (FFR) equivalent, virtual stenting procedure and an optimization of the virtual stenting, by considering not only the value of computed FFR, but also the low and high WSS regions and the state of arterial wall after stenting. The evaluation of the proposed methodology is performed in two ways: the calculated values of FFR are compared with clinically measured values; and the results obtained for automated optimized virtual stenting are compared with virtual stenting performed manually by an expert clinician for the whole considered dataset.

The ventilatory efficiency parameters outperform peak oxygen consumption in monitoring the therapy effects in patients with hypertrophic cardiomyopathy.

Aim: We sought the cardiopulmonary exercise testing (CPET) parameter that most accurately reflected therapeutic efficacy in patients with hypertrophic cardiomyopathy (HCM).

Methods: Well-being questionnaire, N-terminal brain natriuretic peptide measurements, echocardiography, and CPET were performed in patients with symptomatic non-obstructive HCM during phase II, randomized, open-label multicentre study, before and after 16 weeks of traditional or sacubitril/valsartan treatment. Patients were followed 36 months after the initial CPET.

An Insight into Perfusion Anisotropy within Solid Murine Lung Cancer Tumors.

Blood vessels are essential for maintaining tumor growth, progression, and metastasis, yet the tumor vasculature is under a constant state of remodeling. Since the tumor vasculature is an attractive therapeutic target, there is a need to predict the dynamic changes in intratumoral fluid pressure and velocity that occur across the tumor microenvironment (TME). The goal of this study was to obtain insight into perfusion anisotropy within lung tumors.

From imaging to personalized 3D printed molds in cranioplasty.

Cranioplasty is the surgical repair of a bone defect in the skull resulting from a previous operation or injury, which involves lifting the scalp and restoring the contour of the skull with a graft made from material that is reconstructed from scans of the patient's own skull. The paper introduces a 3D printing technology in creating molds, which are filled with polymethyl methacrylate (PMMA) to reconstruct the missing bone part of the skull. The procedure included several steps to create a 3D model in an STL format, conversion into a G-code which is further used to produce the mold itself using a 3D printer.

Electrospun Gelatin Scaffolds with Incorporated Antibiotics for Skin Wound Healing.

Recent advances in regenerative medicine provide encouraging strategies to produce artificial skin substitutes. Gelatin scaffolds are successfully used as wound-dressing materials due to their superior properties, such as biocompatibility and the ability to mimic the extracellular matrix of the surrounding environment. In this study, five gelatin combination solutions were prepared and successfully electrospun using an electrospinning technique.

Sacubitril/valsartan for the treatment of non-obstructive hypertrophic cardiomyopathy: An open label randomized controlled trial (SILICOFCM).

Aim: Sacubitril/valsartan treatment reduces mortality and hospitalizations in heart failure with reduced ejection fraction but has limited application in hypertrophic cardiomyopathy (HCM). The aim of this study was to evaluate the effect of sacubitril/valsartan on peak oxygen consumption (VO) in patients with non-obstructive HCM.

Methods And Results: This is a phase II, randomized, open-label multicentre study that enrolled adult patients with symptomatic non-obstructive HCM (New York Heart Association class I-III) who were randomly assigned (2:1) to receive sacubitril/valsartan (target dose 97/103 mg) or control for 16 weeks.

On the generality of the finite element modeling physical fields in biological systems by the multiscale smeared concept (Kojic transport model).

The biomechanical and biochemical processes in the biological systems of living organisms are extremely complex. Advances in understanding these processes are mainly achieved by laboratory and clinical investigations, but in recent decades they are supported by computational modeling. Besides enormous efforts and achievements in this modeling, there still is a need for new methods that can be used in everyday research and medical practice.

Software that combines deep learning, 3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging.

Background: Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease.

Objective: This paper presents software methodology that can be used in combination with this imaging technique to provide additional information about the state of patient-specific artery.

Methods: Overall three modules are combined within the proposed methodology.

Bioactivities and Medicinal Value of the Fruiting Body Extracts of Laetiporus sulphureus and Meripilus giganteus Polypore Mushrooms (Agaricomycetes).

In the present investigation methanol and acetone extracts of basidiocarps of mushrooms Laetiporus sulphureus and Meripilus giganteus were evaluated for their antimicrobial, cytotoxic and antioxidant/prooxidant effects. The antimicrobial potential was determined by the microdilution method against ten microorganisms. Cytotoxic effects were evaluated by MTT test, while changes of the redox status parameters (superoxide anion radical, nitrites and reduced glutathione) were determined spectrophotometrically on a human colorectal cancer cell line and human health fibroblasts cells.

Evaluation of Novel Dendrimer-Gold Complex Nanoparticles for Theranostic Application in Oncology.

Aim: Despite some successful examples of therapeutic nanoparticles reaching clinical stages, there is still a significant need for novel formulations in order to improve the selectivity and efficacy of cancer treatment.

Methods: The authors developed two novel dendrimer-gold (Au) complex-based nanoparticles using two different synthesis routes: complexation method (formulation A) and precipitation method (formulation B). Using a biomimetic cancer-on-a-chip model, the authors evaluated the possible cytotoxicity and internalization by colorectal cancer cells of dendrimer-Au complex-based nanoparticles.