Detection Of Pulmonary Fibrosis Research Articles

Regulatory role of RGMb in lung injury promoted by the combination of carbon ion irradiation and anti-PD-1 antibody through Erk1/2 and p38 MAPK pathways

The combination of carbon ion radiotherapy and anti-PD-1 antibody represents a new approach to treating thoracic tumors. However, the lung damage caused by this combination therapy may limit its use, and the potential mechanisms for this are worthy of investigation. The objective of this research was to examine the potential involvement of repulsive guidance molecule b (RGMb) in lung damage promoted by the utilization of carbon ion irradiation combined with an anti-PD-1 antibody. The C57BL/6 mice have been randomly separated into four distinct groups: control, anti-PD-1, whole thorax carbon ion irradiation, and irradiation in combination with anti-PD-1 treatment groups (combination group). Detection of pathological changes in lung tissue using HE staining. Detection of pulmonary fibrosis by Masson staining and the hydroxyproline assay. ELISA to detect TNF-α, TGF-β, IL-6, and IL-1β expression levels within lung homogenates. The expression of RGMb, p38 MAPK, and Erk1/2 pathways was detected using a fully automated digital Western blotting system WES (ProteinSimple, USA). Flow cytometry was employed to analyze tissue-resident memory T cells (TRM) within the lung. Subsequently, the siRNA gene was employed to induce the downregulation of RGMb in mice in order to validate the involvement of RGMb in radiation-immune lung injury. The present study observed a significant increase in both inflammatory and fibrotic indicators within the mice group's lung tissue that received the combination treatment. The combination group exhibited elevated levels of TGF-β, TNF-α, IL-6, and IL-1β in lung homogenates. Anti-PD-1 antibody and carbon ion irradiation, upregulated RGMb, phospho-p38 MAPK and phospho-Erk1/2. The results obtained from the flow cytometry analysis indicated that the combination group was significantly higher in the number of clonal expansion TRMs, which were predominantly characterized by the expression of CD8+CD103+CD69-TRMs. The downregulate of RGMb via siRNA in mice resulted in a decrease in phospho-p38 MAPK and phospho-Erk1/2. The combination group exhibited a reduction in TNF-α, TGF-β, IL-6, and IL-1β in their lung tissues, and the number of CD8+CD103+CD69-TRM was significantly reduced. The combination group exhibited a significant improvement in inflammatory and fibrotic indicators within the lung tissues. Anti-PD-1 antibody and carbon ion irradiation synergistically regulate RGMb, leading to strong clonal expansion of lung TRM through the p38 MAPK and Erk1/2 pathways. The present study offers valuable insights into the treatment of lung injury due to the combined administration of carbon ion radiotherapy and anti-PD-1 antibody therapy.

Deep Transfer Learning Techniques-Based Automated Classification and Detection of Pulmonary Fibrosis from Chest CT Images

Pulmonary Fibrosis (PF) is a non-curable chronic lung disease. Therefore, a quick and accurate PF diagnosis is imperative. In the present study, we aim to compare the performance of the six state-of-the-art Deep Transfer Learning techniques to classify patients accurately and perform abnormality localization in Computer Tomography (CT) scan images. A total of 2299 samples comprising normal and PF-positive CT images were preprocessed. The preprocessed images were split into training (75%), validation (15%), and test data (10%). These transfer learning models were trained and validated by optimizing the hyperparameters, such as the learning rate and the number of epochs. The optimized architectures have been evaluated with different performance metrics to demonstrate the consistency of the optimized model. At epoch 26, using an optimized learning rate of 0.0000625, the ResNet50v2 model achieved the highest training and validation accuracy (training = 99.92%, validation = 99.22%) and minimum loss (training = 0.00428, validation = 0.00683) for CT images. The experimental evaluation on the independent testing data confirms that optimized ResNet50v2 outperformed every other optimized architecture under consideration achieving a perfect score of 1.0 in each of the standard performance measures such as accuracy, precision, recall, F1-score, Mathew Correlation Coefficient (MCC), Area under the Receiver Operating Characteristic (ROC-AUC) curve, and the Area under the Precision recall (AUC_PR) curve. Therefore, we can propose that the optimized ResNet50v2 is a reliable diagnostic model for automatically classifying PF-positive patients using chest CT images.

Patient Reported Experiences and Delays During the Diagnostic Pathway for Pulmonary Fibrosis: A Multinational European Survey.

Introduction: Pulmonary fibrosis includes a spectrum of diseases and is incurable. There is a variation in disease course, but it is often progressive leading to increased breathlessness, impaired quality of life, and decreased life expectancy. Detection of pulmonary fibrosis is challenging, which contributes to considerable delays in diagnosis and treatment. More knowledge about the diagnostic journey from patients' perspective is needed to improve the diagnostic pathway. The aims of this study were to evaluate the time to diagnosis of pulmonary fibrosis, identify potential reasons for delays, and document patients emotions.Methods: Members of European patient organisations, with a self-reported diagnosis of pulmonary fibrosis, were invited to participate in an online survey. The survey assessed the diagnostic pathway retrospectively, focusing on four stages: (1) time from initial symptoms to first appointment in primary care; (2) time to hospital referral; (3) time to first hospital appointment; (4) time to final diagnosis. It comprised open-ended and closed questions focusing on time to diagnosis, factors contributing to delays, diagnostic tests, patient emotions, and information provision.Results: Two hundred and seventy three participants (214 idiopathic pulmonary fibrosis, 28 sarcoidosis, 31 other) from 13 countries responded. Forty percent of individuals took ≥1 year to receive a final diagnosis. Greatest delays were reported in stage 1, with only 50.2% making an appointment within 3 months. For stage 2, 73.3% reported a hospital referral within three primary care visits. However, 9.9% reported six or more visits. After referral, 76.9% of patients were assessed by a specialist within 3 months (stage 3) and 62.6% received a final diagnosis within 3 months of their first hospital visit (stage 4). Emotions during the journey were overall negative. A major need for more information and support during and after the diagnostic process was identified.Conclusion: The time to diagnose pulmonary fibrosis varies widely across Europe. Delays occur at each stage of the diagnostic pathway. Raising awareness about pulmonary fibrosis amongst the general population and healthcare workers is essential to shorten the time to diagnosis. Furthermore, there remains a need to provide patients with sufficient information and support at all stages of their diagnostic journey.

SP0098 Ultrasound in Lung Fibrosis

Promising data have recently been obtained by lung ultrasound (LUS), which allows the detection of pulmonary fibrosis by the evaluation of B-lines (previously called ultrasound lung comets). B-lines are the...

SP0179 Ultrasound in Lung Fibrosis

Promising data have recently been obtained by lung ultrasound (LUS), which allows the detection of pulmonary fibrosis by the evaluation of B-lines (previously called ultrasound lung comets). B-lines are the...