Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals. The availability of prompt medication error reports can enable health professionals to provide immediate interventions for patients. Furthermore, it can enable clinical researchers to modify experiments easily and predict health levels based on medication compliance. This study proposes a method in which videos of patients taking medications are recorded using a camera image sensor integrated into a wearable device. The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique. As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that uses the combined feature values to perform action recognition (Model 2). Moreover, 50,000 image data were collected from 89 participants, and labeling was performed on different data categories to train the algorithm. The experimental combination of the object detection model (Model 1) and action recognition model (Model 2) was newly developed, and the accuracy was 92.7%, which is significantly high for medication behavior recognition. This study is expected to enable rapid intervention for providers seeking to treat patients through rapid reporting of drug errors

Controlled Delivery Systems of Protein and Peptide Therapeutics

Protein and peptide therapeutics have been widely investigated as effective therapeutic agents for the treatment of various diseases, including cancer, metabolic, and autoimmune disorders [1]. Their global market is dramatically growing with more than hundreds of approved biopharmaceuticals [1]. Protein and peptide therapeutics have several advantages such as higher affinity and specificity to target molecules with low toxicity than small-molecule chemical drugs [2]. However, the administered protein and peptide therapeutics are unstable and easily degraded in the body [2]. In addition, because many protein and peptide therapeutics have their own target molecules, it is an important task to deliver these therapeutics into specific target sites without lysosomal degradation [3]. To improve patients’ compliance, safe and effective drug delivery systems (DDSs) are strongly needed for conventional biopharmaceutics. DDS is the process or method of administering a therapeutic agent for a controlled therapeutic effect [3]. Optimized DDSs enable long-term therapy, reducing the dosage of drugs, elevating therapeutic concentration of drugs for a long time, and targeting the disease site with reduced side effect of drugs [4]. These DDSs improve existing limitations of conventional drugs and bring various advantages in medical and economic issues [3]. Many patents of major protein and peptide therapeutics are expired or will be expired soon [5]. Recently, with the progress of nanotechnology, materials can be engineered at an atomic scale, and more sophisticated systems can be developed responding to various medical needs. In this review, we describe recent advances in protein and peptide DDSs and discuss current and potential strategies to improve the therapeutic efficacy for the treatment of various diseases using the cutting-edge technologies.1

Spectromicroscopic observation of a live single cell in a biocompatible liquid-enclosing graphene system

On-the-spot visualization of biochemical responses of intact live cells is vital for a clear understanding of cell biology. The main obstacles for instant visualization of biochemical responses of living cells arise from the lack of a sophisticated detecting technique which can simultaneously provide chemical analysis tools and the biocompatible wet conditions. Here we introduce scanning transmission X-ray microscopy (STXM) combined with a liquid-enclosing graphene system (LGS), offering biocompatible conditions and improved X-ray absorption spectra to probe the chemical responses of live cells under wet conditions. This set-up enables us to probe a subtle change in absorption spectra depending on the oxidation state of a miniscule amount of oxygen in the functional groups present in each cell and its surroundings containing a minimal amount of liquid water. As an example of in situ biochemical responses of wet cells, chemical responses of a single Colo 205 cell are visualized and analyzed using X-ray absorption near the oxygen K-edge. This spectromicroscopic method using LGS can be applied to diverse biological samples under wet conditions for the analysis of their biochemical responses

TCAD augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area HARC etching process

Cost-effective vertical etching of plug holes and word lines is crucial in enhancing 3D NAND device manufacturability. Even though multiscale technology computer-aided design (TCAD) methodology is suitable for effectively predicting etching processes and optimizing recipes, it is highly time-consuming. This article demonstrates that our deep learning platform called TCAD-augmented Generative Adversarial Network can reduce the computational load by 2 600 000 times. In addition, because well-calibrated TCAD data based on physical and chemical mutual reactions are used to train the platform, the etching profile can be predicted with the same accuracy as TCAD-only even when the actual experimental data are scarce. This platform opens up new applications, such as hot spot detection and mask layout optimization, in a chip-level area of 3D NAND fabrication