Right-sided heart failure is the major cause of death among patients who suffer from various forms of pulmonary hypertension and congenital heart disease. The right ventricle (RV) and left ventricle (LV) originate from different progenitor cells and function against very different blood pressures. However, differences between the RV and LV formed after birth have not been well defined. Work from our laboratory and others has accumulated evidence that redox signaling, oxidative stress and antioxidant regulation are important components that define the RV/LV differences. The present article summarizes the progress in understanding the roles of redox biology in the RV chamber-specificity. Understanding the mechanisms of RV/LV differences should help develop selective therapeutic strategies to help patients who are susceptible to and suffering from right-sided heart failure. Modulations of redox biology may provide effective therapeutic avenues for these conditions.

Pulmonary arterial hypertension (PAH) is a lethal disease and improved therapeutic strategies are needed. Increased pulmonary arterial pressure, due to vasoconstriction and vascular remodeling, causes right ventricle (RV) failure and death in patients. The treatment of Sprague-Dawley rats with SU5416 injection and exposure to chronic hypoxia for three weeks followed by maintenance in normoxia promote progressive and severe PAH with pathologic features that resemble human PAH. At 5–17 weeks after the SU5416 injection, PAH is developed with pulmonary vascular remodeling as well as RV hypertrophy and fibrosis. The present study investigated subsequent events that occur in these PAH animals. At 35 weeks after the SU5416 injection, rats still maintained high RV pressure, but pulmonary vascular remodeling was significantly reduced. Metabolomics analysis revealed that lungs of normal rats and rats from the 35-week time point had different metabolomics profiles. Despite the maintenance of high RV pressure, fibrosis was resolved at 35-weeks. Masson’s trichrome stain and Western blotting monitoring collagen 1 determined 12% fibrosis in the RV at 17-weeks, and this was decreased to 5% at 35-weeks. The level of myofibroblasts was elevated at 17-weeks and normalized at 35-weeks. These results suggest that biological systems possess natural ways to resolve pulmonary and RV remodeling. The resolution of RV fibrosis appears to involve the reduction of myofibroblast-dependent collagen synthesis. Understanding these endogenous mechanisms should help improve therapeutic strategies to treat PAH and RV failure.

(1) Background: Recent studies report high accuracies when using machine learning (ML) algorithms to classify prostate cancer lesions on publicly available datasets. However, it is unknown if these trained models generalize well to data from different institutions. (2) Methods: This was a retrospective study using multi-parametric Magnetic Resonance Imaging (mpMRI) data from our institution (63 mpMRI lesions) and the ProstateX-2 challenge, a publicly available annotated image set (112 mpMRI lesions). Residual Neural Network (ResNet) algorithms were trained to classify lesions as high-risk (hrPCA) or low-risk/benign. Models were trained on (a) ProstateX-2 data, (b) local institutional data, and (c) combined ProstateX-2 and local data. The models were then tested on (a) ProstateX-2, (b) local and (c) combined ProstateX-2 and local data. (3) Results: Models trained on either local or ProstateX-2 image data had high Area Under the ROC Curve (AUC)s (0.82–0.98) in the classification of hrPCA when tested on their own respective populations. AUCs decreased significantly (0.23–0.50, p < 0.01) when models were tested on image data from the other institution. Models trained on image data from both institutions re-achieved high AUCs (0.83–0.99). (4) Conclusions: Accurate prostate cancer classification models trained on single-institutional image data performed poorly when tested on outside-institutional image data. Heterogeneous multi-institutional training image data will likely be required to achieve broadly applicable mpMRI models.