External Support of Autologous Internal Jugular Vein Grafts with FRAME Mesh in a Porcine Carotid Artery Model.

BACKGROUND: Autologous vein grafts are widely used for bypass procedures in cardiovascular surgery. However, these grafts are susceptible to failure due to vein graft disease. Our study aimed to evaluate the impact of the latest-generation FRAME external support on vein graft remodeling in a preclinical model. METHODS: We performed autologous internal jugular vein interposition grafting in porcine carotid arteries for one month. Four grafts were supported with a FRAME mesh, while seven unsupported grafts served as controls. The conduits were examined through flowmetry, angiography, macroscopy, and microscopy. RESULTS: The one-month patency rate of FRAME-supported grafts was 100% (4/4), whereas that of unsupported controls was 43% (3/7, Log-rank p = 0.071). On explant angiography, FRAME grafts exhibited significantly more areas with no or mild stenosis (9/12) compared to control grafts (3/21, p = 0.0009). Blood flow at explantation was higher in the FRAME grafts (145 ± 51 mL/min) than in the controls (46 ± 85 mL/min, p = 0.066). Area and thickness of neo-intimal hyperplasia (NIH) at proximal anastomoses were similar for the FRAME and the control groups: 5.79 ± 1.38 versus 6.94 ± 1.10 mm2, respectively (p = 0.558) and 480 ± 95 vs. 587 ± 52 µm2/µm, respectively (p = 0.401). However, in the midgraft portions, the NIH area and thickness were significantly lower in the FRAME group than in the control group: 3.73 ± 0.64 vs. 6.27 ± 0.64 mm2, respectively (p = 0.022) and 258 ± 49 vs. 518 ± 36 µm2/µm, respectively (p = 0.0002). CONCLUSIONS: In our porcine model, the external mesh FRAME improved the patency of vein-to-carotid artery grafts and protected them from stenosis, particularly in the mid regions. The midgraft neo-intimal hyperplasia was two-fold thinner in the meshed grafts than in the controls.

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue.

BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model's viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists. METHODS: We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue. RESULTS: The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist. CONCLUSIONS: The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms.

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.

Alpha-1 Antitrypsin and Hepatocellular Carcinoma in Liver Cirrhosis: SERPINA1 MZ or MS Genotype Carriage Decreases the Risk.

Heterozygotes for Z or S alleles of alpha-1-antrypsin (AAT) have low serum AAT levels. Our aim was to compare the risk of hepatocellular carcinoma (HCC) in patients with liver cirrhosis carrying the SERPINA1 MM, MZ and MS genotypes. The study groups consisted of 1119 patients with liver cirrhosis of various aetiologies, and 3240 healthy individuals served as population controls. The MZ genotype was significantly more frequent in the study group (55/1119 vs. 87/3240, p < 0.0001). The MS genotype frequency was comparable in controls (32/119 vs. 101/3240, p = 0.84). MZ and MS heterozygotes had lower serum AAT level than MM homozygotes (medians: 0.90 g/L; 1.40 g/L and 1.67 g/L; p < 0.001 for both). There were significantly fewer patients with HCC in the cirrhosis group among MZ and MS heterozygotes than in MM homozygotes (5/55 and 1/32 respectively, vs. 243/1022, p < 0.01 for both). The risk of HCC was lower in MZ and MS heterozygotes than in MM homozygotes (OR 0.3202; 95% CI 0.1361-0.7719 and OR 0.1522; 95% CI 0.02941-0.7882, respectively). Multivariate analysis of HCC risk factors identified MZ or MS genotype carriage as a protective factor, whereas age, male sex, BMI and viral aetiology of cirrhosis increased HCC risk.

Local Angiotensin-Converting Enzyme 2 Gene Expression in Kidney Allografts Is Not Affected by Renin-Angiotensin-Aldosterone Inhibitors.

BACKGROUND: Preclinical studies suggested that pharmacological inhibition of the renin-angiotensin-aldosterone system (RAAS) by ACE inhibitors (ACEis) or angiotensin II receptor blockers (ARBs) may increase local angiotensin-converting enzyme 2 (ACE2) expression. METHODS: In this study, we evaluated the effect of ACEi or ARB treatment on expression of ACE2, ACE, and AGTR1 in 3-month protocol kidney allograft biopsies of stable patients using RT-qPCR (n = 48). Protein ACE2 expression was assessed using immunohistochemistry from paraffin sections. RESULTS: The therapy with RAAS blockers was not associated with increased ACE2, ACE, or ATGR1 expression in kidney allografts and also ACE2 protein immunohistochemistry did not reveal differences among groups. CONCLUSIONS: ACEis or ARBs in kidney transplant recipients do not affect local ACE2 expression. This observation supports long-term RAAS treatment in kidney transplant recipients, despite acute complications such as COVID-19 where ACE2 serves as the entry protein for infection.