Spectrometer-based wavelength interrogation SPR imaging via Hadamard transform.

We present spectrometer-based wavelength interrogation surface plasmon resonance imaging (SPRi) without mechanical scanning. A polarized broadband light source illuminates an object via a gold-coated prism; the reflected light is spatially modulated by a digital mirror device (DMD) and then measured with a spectrometer. Reflectance spectral images are reconstructed via the Hadamard transform (HT), and a refractive index (RI) map is visualized from the reflectance spectral images by analyzing the resonance peak shift of the spectrum at each image pixel. We demonstrate the feasibility of our method by evaluating the resolution, sensitivity, and dynamic detection range, experimentally obtained as ∼2.203 × 10-6 RI unit (RIU), ∼3,407 nm/RIU, and ∼0.1403 RIU, respectively. Furthermore, simulations are performed to validate the experimental results.

Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life.

Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.

Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors.

Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.

Magnetic polymer bowl for enhanced catalytic activity and recyclability.

This work introduces the fabrication of a magnetic polymer bowl for enhanced catalytic activity and recyclability, which involves the synthesis of silica-coated Fe3O4 magnetic clusters, seeded dispersion polymerization using the magnetic clusters, and transformation into a bowl-like structure via a phase separation route. The additional treatment with tannic acid (TA) on the bowls allows the in situ formation of silver nanoparticles (AgNPs) on their surfaces. The openness and larger surface area of the bowls, as compared with those of other structured particles, such as spheres and flowers, enable a considerably higher immobilization of AgNPs, thus leading to an excellent catalytic reduction for 4-nitrophenol (4-NP), methylene blue (MB), and rhodamine B. Furthermore, the strong magnetic response originating from the magnetic clusters inside the bowls endows a good magnetic recovery and an excellent reusability for the repeated reduction of the organic dyes without loss of catalytic activity.

Poly(ε-caprolactone) (PCL) Hollow Nanoparticles with Surface Sealability and On-Demand Pore Generability for Easy Loading and NIR Light-Triggered Release of Drug.

A new system for the easy loading and NIR light-triggered release of drugs is introduced. It consists of poly(ε-caprolactone) (PCL) hollow nanoparticles with surface openings containing a biodegradable fatty acid with phase-change ability and a biocompatible photothermal agent. These openings, which can enhance the connectivity between the interior and the exterior, enable the easy loading of drug molecules into the interior voids, and their successive sealing ensures a stable encapsulation of the drug. Upon exposure to an external NIR light irradiation, the photothermal agent generates heat that raises the local temperature of the hollow particles above the melting point of the fatty acid, leading to the formation of nanopores on their shells, and consequently, the instant release of the encapsulated drug molecules through the pores. The synergistic activity of the hyperthermia effect from the photothermal agent and the NIR-triggered release of the drug molecules results in noticeable anticancer efficacy.