Fractal analysis of concurrently prepared latex rubber casts of the bronchial and vascular systems of the human lung

Artificial Intelligence-Based Feature Analysis of Pulmonary Vein Morphology on Computed Tomography Scans and Risk of Atrial Fibrillation Recurrence After Catheter Ablation: A Multi-Site Study

BACKGROUND

Atrial fibrillation (AF) recurrence is common after catheter ablation. Pulmonary vein (PV) isolation is the cornerstone of AF ablation, but PV remodeling has been associated with the risk of AF recurrence. We aimed to evaluate whether artificial intelligence-based morphological features of primary and secondary PV branches on computed tomography images are associated with AF recurrence post-ablation.

METHODS

Two artificial intelligence models were trained for the segmentation of computed tomography images, enabling the isolation of PV branches. Patients from Cleveland Clinic (N=135) and Vanderbilt University (N=594) were combined and divided into 2 sets for training and cross-validation (D1, n=218) and internal testing (D2, n=511). An independent validation set (D3, N=80) was obtained from University Hospitals of Cleveland. We extracted 48 fractal-based and 12 shape-based radiomic features from primary and secondary PV branches of patients with AF recurrence (AF+) and without recurrence after catheter ablation of AF (AF-). To predict AFrecurrence, 3 Gradient Boosting classification models based on significant features from primary (Mp), secondary (Ms), and combined (Mc) PV branches were built.

RESULTS

Features relating to primary PVs were found to be associated with AF recurrence. The Mp classifier achieved area under the curve values of 0.73, 0.71, and 0.70 across the 3 datasets. AF+ cases exhibited greater surface complexity in their primary PV area, as evidenced by higher fractal dimension values compared with AF- cases. The Ms classifier results revealed a weaker association with AF+, suggesting higher relevance to AF recurrence post-ablation from primary PV branch morphology.

CONCLUSIONS

This largest multi-institutional study to date revealed associations between artificial intelligence-extracted morphological features of the primary PV branches with AF recurrence in 809 patients from 3 sites. Future work will focus on enhancing the predictive ability of the classifier by integrating clinical, structural, and morphological features, including left atrial appendage and left atrium-related characteristics.

Orthotopic transplantation of the bioengineered lung using a mouse-scale perfusion-based bioreactor and human primary endothelial cells

Whole lung engineering and the transplantation of its products is an ambitious goal and ultimately a viable solution for alleviating the donor-shortage crisis for lung transplants. There are several limitations currently impeding progress in the field with a major obstacle being efficient revascularization of decellularized scaffolds, which requires an extremely large number of cells when using larger pre-clinical animal models. Here, we developed a simple but effective experimental pulmonary bioengineering platform by utilizing the lung as a scaffold. Revascularization of pulmonary vasculature using human umbilical cord vein endothelial cells was feasible using a novel in-house developed perfusion-based bioreactor. The endothelial lumens formed in the peripheral alveolar area were confirmed using a transmission electron microscope. The quality of engineered lung vasculature was evaluated using box-counting analysis of histological images. The engineered mouse lungs were successfully transplanted into the orthotopic thoracic cavity. The engineered vasculature in the lung scaffold showed blood perfusion after transplantation without significant hemorrhage. The mouse-based lung bioengineering system can be utilized as an efficient ex-vivo screening platform for lung tissue engineering.

A critical assessment of the cellular defences of the avian respiratory system: are birds in general and poultry in particular relatively more susceptible to pulmonary infections/afflictions?

In commercial poultry farming, respiratory diseases cause high morbidities and mortalities, begetting colossal economic losses. Without empirical evidence, early observations led to the supposition that birds in general, and poultry in particular, have weak innate and adaptive pulmonary defences and are therefore highly susceptible to injury by pathogens. Recent findings have, however, shown that birds possess notably efficient pulmonary defences that include: (i) a structurally complex three-tiered airway arrangement with aerodynamically intricate air-flow dynamics that provide efficient filtration of inhaled air; (ii) a specialised airway mucosal lining that comprises air-filtering (ciliated) cells and various resident phagocytic cells such as surface and tissue macrophages, dendritic cells and lymphocytes; (iii) an exceptionally efficient mucociliary escalator system that efficiently removes trapped foreign agents; (iv) phagocytotic atrial and infundibular epithelial cells; (v) phagocytically competent surface macrophages that destroy pathogens and injurious particulates; (vi) pulmonary intravascular macrophages that protect the lung from the vascular side; and (vii) proficiently phagocytic pulmonary extravasated erythrocytes. Additionally, the avian respiratory system rapidly translocates phagocytic cells onto the respiratory surface, ostensibly from the subepithelial space and the circulatory system: the mobilised cells complement the surface macrophages in destroying foreign agents. Further studies are needed to determine whether the posited weak defence of the avian respiratory system is a global avian feature or is exclusive to poultry. This review argues that any inadequacies of pulmonary defences in poultry may have derived from exacting genetic manipulation(s) for traits such as rapid weight gain from efficient conversion of food into meat and eggs and the harsh environmental conditions and severe husbandry operations in modern poultry farming. To reduce pulmonary diseases and their severity, greater effort must be directed at establishment of optimal poultry housing conditions and use of more humane husbandry practices.

Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.

A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm

Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.

Fractal Analysis of Lung Structure in Chronic Obstructive Pulmonary Disease

Chest CT is often used for localizing and quantitating pathologies associated with chronic obstructive pulmonary disease (COPD). While simple measurements of areas and volumes of emphysema and airway structure are common, these methods do not capture the structural complexity of the COPD lung. Since the concept of fractals has been successfully applied to evaluate complexity of the lung, this review is aimed at describing the fractal properties of airway disease, emphysema, and vascular abnormalities in COPD. An object forms a fractal if it exhibits the property of self-similarity at different length scales of evaluations. This fractal property is governed by power-law functions characterized by the fractal dimension (FD). Power-laws can also manifest in other statistical descriptors of structure such as the size distribution of emphysema clusters characterized by the power-law exponent D. Although D is not the same as FD of emphysematous clusters, it is a useful index to characterize the spatial pattern of disease progression and predict clinical outcomes in patients with COPD. The FD of the airway tree shape and the D of the size distribution of airway branches have been proposed indexes of structural assessment and clinical predictions. Simulations are also useful to understand the mechanism of disease progression. Therefore, the power-law and fractal analysis of the parenchyma and airways, especially when combined with computer simulations, could lead to a better understanding of the structural alterations during the progression of COPD and help identify subjects at a high risk of severe COPD.