Bionic artificial skin with a fully implantable wireless tactile sensory system for wound healing and restoring skin tactile function.

Tactile function is essential for human life as it enables us to recognize texture and respond to external stimuli, including potential threats with sharp objects that may result in punctures or lacerations. Severe skin damage caused by severe burns, skin cancer, chemical accidents, and industrial accidents damage the structure of the skin tissue as well as the nerve system, resulting in permanent tactile sensory dysfunction, which significantly impacts an individual's daily life. Here, we introduce a fully-implantable wireless powered tactile sensory system embedded artificial skin (WTSA), with stable operation, to restore permanently damaged tactile function and promote wound healing for regenerating severely damaged skin. The fabricated WTSA facilitates (i) replacement of severely damaged tactile sensory with broad biocompatibility, (ii) promoting of skin wound healing and regeneration through collagen and fibrin-based artificial skin (CFAS), and (iii) minimization of foreign body reaction via hydrogel coating on neural interface electrodes. Furthermore, the WTSA shows a stable operation as a sensory system as evidenced by the quantitative analysis of leg movement angle and electromyogram (EMG) signals in response to varying intensities of applied pressures.

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

M13 phage-functionalized single-walled carbon nanotubes as nanoprobes for second near-infrared window fluorescence imaging of targeted tumors.

Second near-infrared (NIR) window light (950-1400 nm) is attractive for in vivo fluorescence imaging due to its deep penetration depth in tissues and low tissue autofluorescence. Here we show genetically engineered multifunctional M13 phage can assemble fluorescent single-walled carbon nanotubes (SWNTs) and ligands for targeted fluorescence imaging of tumors. M13-SWNT probe is detectable in deep tissues even at a low dosage of 2 µg/mL and up to 2.5 cm in tissue-like phantoms. Moreover, targeted probes show specific and up to 4-fold improved uptake in prostate specific membrane antigen positive prostate tumors compared to control nontargeted probes. This M13 phage-based second NIR window fluorescence imaging probe has great potential for specific detection and therapy monitoring of hard-to-detect areas.