Advanced stage ovarian cancer is challenging to treat due to widespread seeding of tumor spheroids throughout the mesothelial lining of the peritoneal cavity. In this work, a therapeutic strategy using graphene nanoribbons (GNR) functionalized with 4‐arm polyethylene glycol (PEG) and chlorin e6 (Ce6), a sonosensitizer, to target metastatic ovarian cancer spheroids is reported. GNR‐PEG‐Ce6 adsorbs onto the spheroids and disrupts their adhesion to extracellular matrix proteins or LP‐9 mesothelial cells. Furthermore, for spheroids that do adhere, GNR‐PEG‐Ce6 delays spheroid disaggregation and spreading as well as mesothelial clearance, key metastatic processes following adhesion. Owing to the sonodynamic effects of Ce6 and its localized delivery via the biomaterial, GNR‐PEG‐Ce6 can kill ovarian cancer spheroids adhered to LP‐9 cell monolayers when combined with mild ultrasound irradiation. The interaction with GNR‐PEG‐Ce6 also loosens cell–cell adhesions within the spheroids, rendering them more susceptible to treatment with the chemotherapeutic agents cisplatin and paclitaxel, which typically have difficulty in penetrating ovarian cancer spheroids. Thus, this material can facilitate effective chemotherapeutic and sonodynamic combination therapies. Finally, the adhesion inhibiting and sonodynamic effects of GNR‐PEG‐Ce6 are also validated with tumor spheroids derived from the ascites fluid of ovarian cancer patients, providing evidence of the translational potential of this biomaterial approach.

The human brain is a complex system whose function depends on interactions between neurons and their ensembles across scales of organization. These interactions are restricted by anatomical and energetic constraints, and facilitate information processing and integration in response to cognitive demands. In this work, we considered the brain as a closed loop dynamic system under sparse feedback control. This controller design considered simultaneously control performance and feedback (communication) cost. As proof of principle, we applied this framework to structural and functional brain networks. Under high feedback cost only a small number of highly connected network nodes were controlled, which suggests that a small subset of brain regions may play a central role in the control of neural circuits, through a tradeoff between performance and communication cost.