Poly­(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) is a highly conductive, easily processable, self-healing polymer. It has been shown to be useful in bioelectronic applications, for instance, as a biointerfacing layer for studying brain activity, in biosensitive transistors, and in wearable biosensors. A green and biofriendly method for improving the mechanical properties, biocompatibility, and stability of PEDOT:PSS involves mixing the polymer with a biopolymer. Via structural changes and interactions with PEDOT:PSS, biopolymers have the potential to improve the self-healing ability, flexibility, and electrical conductivity of the composite. In this work, we fabricated novel protein–polymer multifunctional composites by mixing PEDOT:PSS with genetically programmable amyloid curli fibers produced byEscherichia coli bacteria. Curli fibers are among the stiffest protein polymers and, once isolated from bacterial biofilms, can form plastic-like thin films that heal with the addition of water. Curli-PEDOT:PSS composites containing 60% curli fibers exhibited a conductivity 4.5-fold higher than that of pristine PEDOT:PSS. The curli fibers imbued the biocomposites with an immediate water-induced self-healing ability. Further, the addition of curli fibers lowered the Young’s and shear moduli of the composites, improving their compatibility for tissue-interfacing applications. Lastly, we showed that genetically engineered fluorescent curli fibers retained their ability to fluoresce within curli-PEDOT:PSS composites. Curli fibers thus allow to modulate a range of properties in conductive PEDOT:PSS composites, broadening the applications of this polymer in biointerfaces and bioelectronics.

(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinicians to spend a great deal of time reviewing the ultrasound images. (2) Methods: In this study, we applied tracking algorithms based on deep learning and correlation filters to detect hyoid locations in ultrasound videos collected during swallowing. Fifty videos were collected from 10 young, healthy subjects for training, evaluation, and testing of the trackers. (3) Results: The best performing deep learning algorithm, Fully-Convolutional Siamese Networks (SiamFC), proved to have reliable performance in getting accurate hyoid bone locations from each frame of the swallowing ultrasound videos. While having a real-time frame rate (175 fps) when running on an RTX 2060, SiamFC also achieved a precision of 98.9% at the threshold of 10 pixels (3.25 mm) and 80.5% at the threshold of 5 pixels (1.63 mm). The tracker’s root-mean-square error and average error were 3.9 pixels (1.27 mm) and 3.3 pixels (1.07 mm), respectively. (4) Conclusions: Our results pave the way for real-time automatic tracking of the hyoid bone in ultrasound videos for swallowing assessment.