Cellular senescence is suspected to be a central contributing cause, a belief which is supported by benefits seen in patients given senolytic therapy.It is hypothesized that the initial or repetitive injury in IPF occurs to the lung cells, called alveolar epithelial cells (AECs, pneumocytes), which line the majority of the alveolar surface. When type I AECs are damaged or lost, it is thought that type II AECs undergo proliferation to cover the exposed basement membranes. In normal repair, the hyperplastic type II AECs die and the remaining cells spread and undergo a differentiation process to become type I AECs. Under pathologic conditions and in the presence of transforming growth factor beta (TGF-β), fibroblasts accumulate in these areas of damage and differentiate into myofibroblasts that secrete collagen and other proteins. In the current classification of the pathogenesis of IPF, it is believed that it occurs by way of the formation of a UIP (usual interstitial pneumonia) lesion, which then undergoes the aforementioned pathological condition characteristic of IPF. Other proposed repeated injury mechanisms indicate that IPF may result not just from a UIP lesion, but also from NSIP and DAD (nonspecific interstitial pneumonia and diffuse alveolar damage) lesions, or a combination of several. In the past, it was thought that inflammation was the first event in initiating lung tissue scarring. Later findings showed that the development of fibroblastic foci precedes the accumulation of inflammatory cells and the consequent deposition of collagen.
These molecules are directed against several growth factors and cytokines that are known to play a role in the proliferation, activation, differentiation or inappropriate survival of fibroblasts.mir-29 microRNA precursor investigations in mice have produced reversal of induced IPF. MRG-201 is currently being tested as-of 2016, but not in IPF patients yet, and no human trials for IPF use have been scheduled as of January 2016.Stem cell therapies for IPF are an area of research.A machine learning algorithm has been proposed that discovers subtle patterns in  individual history of medical encounters to reliably estimate the risk of a future IPF diagnosis, up to four years before current medical practice. The algorithm outputs a score (ZCoR) using medical history on file with no new tests, and might be deployable as a universal IPF screening tool in primary care. ZCoR has been trained and validated on nearly 3 million patients across multiple databases, achieving high predictive performance in out-of-sample data (positive likelihood ratio > 30 with 99% specificity). The authors conclude that past respiratory disorders maximally contribute to IPF risk, followed by known IPF comorbidities, metabolic diseases, cardiovascular abnormalities, and diseases of the eye, with the overall pattern of the importance ranking substantially invariant across the sexes. References
External links

Idiopathic pulmonary fibrosis at Curlie