Acute lung injury and predictors of mortality

The Regenerative Power of Stem Cells: Treating Bleomycin-Induced Lung Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease with no known cure, characterized by the formation of scar tissue in the lungs, leading to respiratory failure. Although the exact cause of IPF remains unclear, the condition is thought to result from a combination of genetic and environmental factors. One of the most widely used animal models to study IPF is the bleomycin-induced lung injury model in mice. In this model, the administration of the chemotherapeutic agent bleomycin causes pulmonary inflammation and fibrosis, which closely mimics the pathological features of human IPF. Numerous recent investigations have explored the functions of various categories of stem cells in the healing process of lung injury induced by bleomycin in mice, documenting the beneficial effects and challenges of this approach. Differentiation of stem cells into various cell types and their ability to modulate tissue microenvironment is an emerging aspect of the regenerative therapies. This review article aims to provide a comprehensive overview of the role of stem cells in repairing bleomycin-induced lung injury. It delves into the mechanisms through which various types of stem cells, including mesenchymal stem cells, embryonic stem cells, induced pluripotent stem cells, and lung resident stem cells, exert their therapeutic effects in this specific model. We have also discussed the unique set of intermediate markers and signaling factors that can influence the proliferation and differentiation of alveolar epithelial cells both during lung repair and homeostasis. Finally, we highlight the challenges and opportunities associated with translating stem cell therapy to the clinic for IPF patients. The novelty and implications of this review extend beyond the understanding of the potential of stem cells in treating IPF to the broader field of regenerative medicine. We believe that the review paves the way for further advancements in stem cell therapies, offering hope for patients suffering from this debilitating and currently incurable disease.

Predicting ICU survival: a meta-level approach

Background

The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.

Methods

Data on 158 men and 46 women, were used retrospectively (75% of the patients survived). We used Glasgow Coma Scale (GCS), Acute Physiology And Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Injury Severity Score (ISS) values to structure a decision tree (DTM), a neural network (NNM) and a logistic regression (LRM) model and we evaluated the assessment indicators implementing Receiver Operating Characteristics (ROC) plot analysis.

Results

Our findings indicate that regarding the assessment of indicators' capacity there are specific discrete limits that should be taken into account. The Az score ± SE was 0.8773± 0.0376 for the DTM, 0.8061± 0.0427 for the NNM and 0.8204± 0.0376 for the LRM, suggesting that the proposed DTM achieved a near optimal Az score.

Conclusion

The predicting processes of ICU survival may go "one step forward", by using classic composite assessment indicators as variables.