RESUMO
Pulmonary fibrosis is a deadly disease that involves the dysregulation of fibroblasts and myofibroblasts, which are mechanosensitive. Previous computational models have succeeded in modeling stiffness-mediated fibroblasts behaviors; however, these models have neglected to consider stretch-mediated behaviors, especially stretch-sensitive channels and the stretch-mediated release of latent TGF-ß. Here, we develop and explore an agent-based model and spring network model hybrid that is capable of recapitulating both stiffness and stretch. Using the model, we evaluate the role of mechanical signaling in homeostasis and disease progression during self-healing and fibrosis, respectively. We develop the model such that there is a fibrotic threshold near which the network tends towards instability and fibrosis or below which the network tends to heal. The healing response is due to the stretch signal, whereas the fibrotic response occurs when the stiffness signal overpowers the stretch signal, creating a positive feedback loop. We also find that by changing the proportional weights of the stretch and stiffness signals, we observe heterogeneity in pathological network structure similar to that seen in human IPF tissue. The system also shows emergent behavior and bifurcations: whether the network will heal or turn fibrotic depends on the initial network organization of the damage, clearly demonstrating structure's pivotal role in healing or fibrosis of the overall network. In summary, these results strongly suggest that the mechanical signaling present in the lungs combined with network effects contribute to both homeostasis and disease progression.
RESUMO
BACKGROUND: Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. OBJECTIVE: In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. METHODS: We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. RESULTS: The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively. CONCLUSION: We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.
RESUMO
Acute respiratory distress syndrome (ARDS) alters the dynamics of lung inflation during mechanical ventilation. Repetitive alveolar collapse and expansion (RACE) predisposes the lung to ventilator-induced lung injury (VILI). Two broad approaches are currently used to minimize VILI: (1) low tidal volume (LVT) with low-moderate positive end-expiratory pressure (PEEP); and (2) open lung approach (OLA). The LVT approach attempts to protect already open lung tissue from overdistension, while simultaneously resting collapsed tissue by excluding it from the cycle of mechanical ventilation. By contrast, the OLA attempts to reinflate potentially recruitable lung, usually over a period of seconds to minutes using higher PEEP used to prevent progressive loss of end-expiratory lung volume (EELV) and RACE. However, even with these protective strategies, clinical studies have shown that ARDS-related mortality remains unacceptably high with a scarcity of effective interventions over the last two decades. One of the main limitations these varied interventions demonstrate to benefit is the observed clinical and pathologic heterogeneity in ARDS. We have developed an alternative ventilation strategy known as the Time Controlled Adaptive Ventilation (TCAV) method of applying the Airway Pressure Release Ventilation (APRV) mode, which takes advantage of the heterogeneous time- and pressure-dependent collapse and reopening of lung units. The TCAV method is a closed-loop system where the expiratory duration personalizes VT and EELV. Personalization of TCAV is informed and tuned with changes in respiratory system compliance (CRS) measured by the slope of the expiratory flow curve during passive exhalation. Two potentially beneficial features of TCAV are: (i) the expiratory duration is personalized to a given patient's lung physiology, which promotes alveolar stabilization by halting the progressive collapse of alveoli, thereby minimizing the time for the reopened lung to collapse again in the next expiration, and (ii) an extended inspiratory phase at a fixed inflation pressure after alveolar stabilization gradually reopens a small amount of tissue with each breath. Subsequently, densely collapsed regions are slowly ratcheted open over a period of hours, or even days. Thus, TCAV has the potential to minimize VILI, reducing ARDS-related morbidity and mortality.