Pentraxin 3 as a marker of development and severity of stable coronary artery disease.

PURPOSE: Inflammation plays a crucial role in the development of atherosclerotic plaques. Pentraxin 3 (PTX3) is produced at the site of inflammation and has been identified as a specific marker of atherosclerosis, vascular inflammation, and progression of the coronary artery disease (CAD). The aim of the study was to establish if PTX3 has potential relations with classical markers of cardiovascular risk, and if PTX3 may act as an independent risk factor of CAD occurrence and advancement. MATERIALS AND METHODS: The study included 98 patients with stable CAD confirmed in coronary angiography (CAD group) (median age 65 interquartile range [IQR] 61-72 years; 72% men). The control group consisted of 40 patients without CAD. RESULTS: The CAD group had significantly higher PTX3 concentration compared to the control group. There was a correlation with age, male gender, lipid profile and intima-media thickness. There was no correlation between PTX3 concentration and the number of coronary vessels with significant atherosclerotic lesions and the advancement of atherosclerotic lesions on the Gensini scoring scale. The cut-off point was determined for 0.89 ng/ml for the exclusion of angiographically significant atherosclerotic lesions. CONCLUSIONS: Patients with CAD have significantly higher concentration of PTX3. There was no correlation between PTX3 and the advancement of angiographically significant atherosclerotic lesions in coronary arteries. Low PTX3 concentration may serve as an indicator for the absence of atherosclerosis.

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients.

The diagnostic process in Intensive Care Units has been revolutionized by ultrasonography and accelerated by artificial intelligence. Patients in critical condition are often sonoanatomically challenging, with time constraints being an additional stress factor. In this paper, we describe the technology behind the development of AI systems to support diagnostic ultrasound in intensive care units. Among the AI-based solutions, the focus was placed on systems supporting cardiac ultrasound, such as Smart-VTI, Auto-VTI, SmartEcho Vue, AutoEF, Us2.ai, and Real Time EF. Solutions to assist hemodynamic assessment based on the evaluation of the inferior vena cava, such as Smart-IVC or Auto-IVC, as well as to facilitate ultrasound assessment of the lungs, such as Smart B-line or Auto B-line, and to help in the estimation of gastric contents, such as Auto Gastric Antrum, were also discussed. All these solutions provide doctors with support by making it easier to obtain appropriate diagnostically correct ultrasound images by automatically performing time-consuming measurements and enabling real-time analysis of the obtained data. Artificial intelligence will most likely be used in the future to create advanced systems facilitating the diagnostic and therapeutic process in intensive care units.

Artificial Intelligence-Supported Ultrasonography in Anesthesiology: Evaluation of a Patient in the Operating Theatre.

Artificial intelligence has now changed regional anesthesia, facilitating, therefore, the application of the regional block under the USG guidance. Innovative technological solutions make it possible to highlight specific anatomical structures in the USG image in real time, as needed for regional block. This contribution presents such technological solutions as U-Net architecture, BPSegData and Nerveblox and the basis for independent assisting systems in the use of regional blocks, e.g., ScanNav Anatomy PNB or the training system NeedleTrainer. The article describes also the systems integrated with the USG devices, such as Mindray SmartNerve or GE cNerve as well as the robotic system Magellan which substantially increases the patient's safety, time needed for the regional block and quality of the procedure. All the solutions presented in this article facilitate the performance of regional blocks by less experienced physicians and appear as an excellent educational tool which, at the same time, improves the availability of the more and more popular regional anesthesia. Will, therefore, artificial intelligence replace physicians in regional block procedures? This seems unlikely. It will, however, assist them in a significant manner, contributing to better effectiveness and improved safety of the patient.