IPF is a specific form of chronic fibrosing interstitial pneumonia of unknown cause, characterized by progressive worsening in lung function and an unfavorable prognosis. Current concepts on IPF pathogenesis are based on a dysregulated wound healing response, leading to an over production of extracellular matrix. Based on recent research however, several other mechanisms are now proposed as potential targets for novel therapeutic strategies. Areas covered: This review analyzes the current investigational strategies targeting extracellular matrix deposition, tyrosine-kinase antagonism, immune and autoimmune response, and cell-based therapy. A description of the pathogenic rationale implied in each novel therapeutic approach is summarized. Expert opinion: New IPF drugs are being evaluated in the context of phase 1 and 2 clinical trials. Nevertheless, many drugs that have shown efficacy in preclinical studies, failed to exhibit the same positive effect when translated to humans. A possible explanation for these failures might be related to the known limitations of animal models of the disease. The recent development of 3D systems composed of cells from individual patients that recreate an ex-vivo model of IPF, could lead to significant improvements in disease pathogenesis and treatment. New drugs could be tested on more genuine models and clinicians could tailor therapy based on patient's response.

The main objective of this review is to explore the wide and expanding field of new clinical trials in IPF. Recent trials have confirmed the efficacy of the approved drugs pirfenidone and nintedanib; nonetheless, the discovery of new biological pathways has opened new horizons in this field. Areas covered: New strategies against matrix deposition are under study and so is for the role of immunity and autoimmunity. Recent advances in the use of stem cells are opening new possibilities for the recovery of damaged lung tissues. The role of microbioma is under investigation in order to evaluate the use of antibiotics in IPF treatment. Analysing all the new and the upcoming clinical trials, we are trying to offer a comprehensive view of the emerging new frontiers in the treatment of IPF. Expert commentary: The key points for the ongoing and upcoming clinical trials will be to avoid previous mistakes and to choose carefully both study populations and efficacy endpoints. The exciting possibility to enrol patients with progressive lung fibrosis, both idiopathic and not, could be a next step forward. How the existing therapies will fit in a futurist scenario of personalized medicine is still a challenge.

Objectives: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Recently Artificial Intelligence systems for the diagnosis of Covid-19 related pneumonia on Chest X ray (CXR) or chest CT have been tested with variable, but not negligible, accuracy. Texture analysis might be an additional tool for the evaluation of CXR in patients with clinical suspicion of Covid-19 related pneumonia.Methods: CXR images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest (ROI) covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 CXR images were selected for the final analysisResults: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning (EML)-Score showing the highest AUCROC (0.971±0.015) was 132.57. Assuming this cut-off the EML model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the EML showed 100% sensitivity, with 80% specificityConclusion: Texture analysis of CXR images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.

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Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest Area Under the Curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.