Bioengineering

The anatomy and mechanical strength of aortic valve leaflets are critical determinants of their biomechanical behavior and long-term structural integrity. The embryonic developmental period, when valves are forming, is critical to establish baseline leaflet properties. However, fetal stages of valve development, when valve leaflets are still forming and remodeling, are not well understood. The goal of this study is to investigate the biomechanical stress and deformation modes of developing valve leaflets during systole, and how leaflet biomechanics are affected by anatomy and material properties. To this end, the study employs a parametric approach to model the leaflet anatomy of an HH40 chick embryo, used here as a model of fetal cardiac development. To perform biomechanical analysis, a pressure profile derived from in ovo Doppler ultrasound measurements was applied, and an Ogden hyperelastic material model was employed following a sensitivity analysis. To determine the effect of valve anatomy on leaflet tissue deformation and stresses, we changed the leaflet midline curve (belly curve) from its native curvature to a linear profile and quantified biomechanical responses. Our analysis revealed a strong decrease in average leaflet effective stress as the belly curvature was shifted towards a linear profile. However, this reduction in average stress was at the expense of a biomechanical trade-off. The shift induced a progressive localization of stress concentration at the leaflet tips and commissures, and a distinct bending deformation mode at the tip under peak load. Our findings demonstrate that while the belly curve of the leaflet modulates tissue stress during valve opening, a low-stress anatomy does not align with hemodynamic performance. This work characterizes competing leaflet biomechanical responses (stress reduction versus failure modes) that shape valve leaflet formation, providing fundamental insights into developmental valve biomechanics.

Retinal transplantation offers promise for restoring vision in advanced retinal degeneration. However, manual delivery of retinal sheets is often hindered by imprecise placement and collateral tissue damage resulting from instrument instability. We introduce a novel, 3D-printed, motorized retinal sheet injector designed to enhance placement accuracy and minimize tissue injury. The motorized injector features an Arduino-controlled foot pedal with three discrete actuator positions (“Min”, “Mid”, “Max”). When compared via frame-by-frame motion analysis, the motorized system reduced tip variance by approximately threefold over manual methods. In addition, in in vitro gelatin trials, the motorized injector achieved significantly higher placement accuracy versus the manual injector, which suffered from occasional complete misplacements. The novel motorized retinal sheet injector markedly improves stability and placement accuracy relative to manual methods, potentially reducing complications associated with subretinal delivery. Safer subretinal delivery can pave the way for innovative research and advanced treatment for retinal disease.

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms.