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This study evaluates AI-designed protein binders for CAR T cell therapy, identifying challenges and developing strategies to improve their efficacy and safety.

This paper develops a dynamic biaxial pressure myograph with two-photon microscopy to study arterial biomechanics and microstructure under physiological conditions in mice.

The paper calibrates a cardiovascular mechanics model using clinical hemodynamic data to create a reference model for theoretical studies.

This study uses computational modeling and 3D bioprinting to design microalgal structures that enhance growth rates and photosynthetic efficiency compared to flat slabs.

The paper introduces M3 sensors to measure tissue stress dynamics and shows that abrupt stress increases drive epithelial-mesenchymal transition in cancer.

CitriBEiTNet combines MobileNetV2 and BEiT for citrus leaf disease classification, achieving high accuracy in detecting diseases and healthy leaves.

VIP-OT traces drug response heterogeneity in single cells using vibrational imaging and optimal transport to predict post-treatment metabolic states from pre-treatment spectra.

This paper identifies a mechano-epigenetic pathway involving EZH2 that drives phenotypic drift in human MSCs during expansion and shows that EZH2 inhibition preserves stemness and therapeutic potency.

This study uses MRI and micro-CT to show that marrow irradiation and transplantation in a sickle cell disease model partially restores bone marrow structure and function.

This study shows that MAP hydrogel encapsulation improves revascularization and endocrine function of transplanted ovarian tissue in a mouse model of primary ovarian insufficiency.

This paper develops scalable fabrication of anisotropic polymeric microfibers for 3D tissue models, showing they guide cell growth in high-throughput systems.

This study engineers high-fat cultivated tissues with authentic sensory properties by optimizing scaffolds, fatty acid delivery, and culture conditions to mimic Wagyu fat.

This paper develops a method to train deep-learning models for multi-species canopy segmentation using FCNNs and pseudo-labeling, achieving high accuracy with reduced human effort.

This paper develops a deep learning framework for drug repurposing using knowledge graph embeddings and LLMs to provide explainable predictions and drug-disease relationship explanations.

This study expresses cannabinoid biosynthesis enzymes in Chlamydomonas reinhardtii, showing functional NphB but limited CBDAS activity, highlighting challenges in enzyme expression and pathway reconstruction.

This study shows that relaxin-2 reduces fibrosis and promotes regenerative healing by inhibiting TGF-b1-driven ECM deposition in both in vitro and in vivo models.

The paper presents a bioresorbable microelectrode array for transient electrophysiological recordings, validated in cardiac models and applicable to neurological disorders.

This study develops a markerless method using a depth-sensing camera to accurately estimate upper-limb kinematics during hand-cycling, achieving 5 mm landmark detection errors and 6.3 degree joint angle errors.

This paper develops a deep learning framework for breast cancer detection and classification in histopathological images, achieving high accuracy using ensemble methods and comprehensive preprocessing.

The paper introduces MiSTY, a genetic platform in budding yeast that separates reproductive and metabolic tasks into distinct cell types, enabling high-yield biomanufacturing by uncoupling cell growth from product synthesis.