Setayesh, T. | Search bioRxiv

bioRxiv2026

Hemoglobin C is prone to oxidative denaturation, resulting in red blood cell membrane damage in HbSC disease

Sickle-hemoglobin-C (HbSC) sickle cell disease is characterized by RBC dehydration (xerocytosis), which promotes polymerization of HbS. HbSC causes substantial morbidity despite lower sickling potential than HbSS, suggesting a critical detrimental role of HbC in the disease pathophysiology. We derived HbCC mice by interbreeding our HbSC mice, which demonstrated a similar RBC phenotype of xerocytosis as humans with HbCC. We compared RBCs from HbCC, HbSC, and HbSS mice. Oxidized ferryl (Fe4+)-Hb, and its oxidative-denaturation, which results in hemichrome formation (Heinz-bodies), was most pronounced in HbCC>HbSC>HbSS, despite significantly higher reactive oxygen species in HbSS, illustrating a higher propensity of HbC to denaturation than HbS. RBC deformability followed a similar pattern, with Elongation Index lowest in HbCC<HbSC<HbSS. Next, we determined if RBC from HbSC patients on hydroxyurea showed improved membrane damage. Hydroxyurea treatment reduced Heinz-body formation and improved RBC deformability, despite negligible/modest fetal hemoglobin (HbF) induction, compared to non-hydroxyurea HbSC controls. The antioxidant quercetin showed a similar reduction in Heinz-body burden and improvement in RBC deformability as hydroxyurea, without affecting Hb or HbF concentration, reticulocyte count, or RBC xerocytosis. HbC-driven oxidative denaturation and membrane damage represent important contributors of RBC dysfunction in HbSC disease; hence, oxidative membrane injury could be targeted besides antisickling approaches.

cell biology↗

bioRxiv2024

XKidneyOnco: An Explainable Framework to Classify Renal Oncocytoma and Chromophobe Renal Cell Carcinoma with a Small Sample Size

Renal oncocytoma and chromophobe renal cell carcinoma are two kidney cancer types that present a diagnostic challenge to pathologists and other clinicians due to their microscopic similarities. While RO is a benign renal neoplasm, ChRCC is considered malignant. Therefore, the differentiation between the two is crucial. In this study, we introduce an explainable framework to accurately differentiate ChRCC from RO, histologically. Our approach examined H&E-stained images of 656 ChRCC and 720 RO, and achieved a diagnostic accuracy of 88.2%, the sensitivity of 87%, and 100% specificity for explainable AI, which either outperforms or operate on par with convolutional neural network (CNN) models. Besides, we enrolled 44 pathology experts (including pathologists and pathology trainees) to differentiate the two tumors. The average accuracy of pathologists was 73%, which is 15.2% lower than our framework. These results indicate that the combination of human expert along with explainable AI achieve higher accuracy in differentiating the two tumors, while it reduces the workload of experts and offers the desired explainability for the medical experts.

pathology↗