3D printing has been widely used for on-demand prototyping of complex three-dimensional structures. In biomedical applications, PEDOT:PSS has emerged as a promising material in versatile bioelectronics due to its tissue-like mechanical properties and suitable electrical properties. However, previously developed PEDOT:PSS inks have not been able to fully utilize the advantages of commercial 3D printing due to its long post treatment times, difficulty in high aspect ratio printing, and low conductivity. We propose a one-shot strategy for the fabrication of PEDOT:PSS ink that is able to simultaneously achieve on-demand biocompatibility (no post treatment), structural integrity during 3D printing for tall three-dimensional structures, and high conductivity for rapid-prototyping. By using ionic liquid-facilitated PEDOT:PSS colloidal stacking induced by a centrifugal protocol, a viscoplastic PEDOT:PSS-ionic liquid colloidal (PILC) ink was developed. PILC inks exhibit high-aspect ratio vertical stacking, omnidirectional printability for generating suspended architectures, high conductivity (~286 S/cm), and high-resolution printing (~50 µm). We demonstrate the on-demand and versatile applicability of PILC inks through the fabrication of 3D circuit boards, on-skin physiological signal monitoring e-tattoos, and implantable bioelectronics (opto-electrocorticography recording, low voltage sciatic nerve stimulation and recording from deeper brain layers via 3D vertical spike arrays).

Objective: The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system. Methods: A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes. Results: For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance. Conclusion: The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake. Significance: The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.

Background: For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods: We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results: In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion: In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

Work-related stress causes serious negative physiological and socioeconomic effects on employees. Detecting stress levels in a timely manner is important for appropriate stress management; therefore, this study proposes a deep learning (DL) approach that accurately detects work-related stress by using multimodal signals. We designed a protocol that simulates stressful situations and recruited 24 subjects for the experiments. Then, we collected electrocardiogram (ECG), respiration (RESP), and video data. The datasets were pre-processed and 10-s ECG and RESP signals and a sequence of facial features were fed into our deep neural network. Sixty-eight facial landmarks’ coordinates were extracted, and facial textures were extracted from a pre-trained network based on facial expression recognition. Each signal was processed by each of its network branch, and data were fused at two different levels: 1) feature-level and 2) decision-level. The feature-level fusion that used RESP and facial landmarks’ coordinates showed average accuracy of 73.3%, AUC of 0.822, and F1 score of 0.700 in two-level stress classification, and the feature-level fusion that used ECG, RESP, and the coordinates showed average accuracy of 54.4%, AUC of 0.727, and F1 score of 0.508 in three-level stress classification. When analyzing the weights in the decision-level fusion, we found that the importance of each information item varied according to the stress classification problem. When comparing t-stochastic neighbor embedding results, we observed that overlapped samples of different classes caused performance degradation in both classifications. Our findings suggest that the proposed DL approach fusing multimodal and heterogeneous signals can enhance stress detection.