This paper reports on the green synthesis of heparin-reduced gold nanoparticles and their nanotopography as studied with atomic force microscopy. The study also evaluated the anticoagulant activity of the newly prepared gold nanoparticles. The heparin-reduced gold nanoparticles were homogeneous, showing characteristic surface plasmon resonance bands of approximately 523–527 nm, and their shapes were mostly spherical and amorphous. The average diameter of the nanoparticles measured from atomic force microscopic images was either 20.26±3.35 nm or 40.85±8.95 nm depending on the different precursor salts and heparin concentrations. Atomic force microscopic images revealed that the topography of the heparin polymer aggregated when deposited onto mica, resembling a chain of mountains. This characteristic nanotopography of the heparin disappeared after the synthesis of the gold nanoparticles was performed. Interestingly, prolonged prothrombin time, thrombin time, and activated partial thromboplastin time were observed in the heparin-reduced gold nanoparticles when compared to a control heparin, suggesting the enhancement of anticoagulant activity in heparin-reduced gold nanoparticles. Hence, the green synthesis of gold nanoparticles with heparin using a simple reaction step could be a viable procedure for enhancing heparin's anticoagulant activity.

Due to the development of convenient brain–machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of channels applicable to general BMI paradigms using a compact convolutional neural network for electroencephalography (EEG)-based BMIs. For verification, three types of BMI paradigms are assessed: 1) the typical P300 auditory oddball; 2) the new top-down steady-state visually evoked potential; and 3) the endogenous motor imagery. We observe that the optimized minimal EEG-channel sets are automatically selected in all three cases. Their decoding accuracies using the minimal channels are statistically equivalent to (or even higher than) those based on all channels. The brain areas of the selected channel set are neurophysiologically interpretable for all of these cognitive task paradigms. This study shows that the minimal EEG channel set can be automatically selected, irrespective of the types of BMI paradigms or EEG input features using a deep-learning approach, which also contributes to their portability.

Whether thalamocortical interactions play a decisive role in conscious perception remains an open question. We presented rapid red/green color flickering stimuli, which induced the mental perception of either an illusory orange color or non-fused red and green colors. Using magnetoencephalography, we observed 6-Hz thalamic activity associated with thalamocortical inhibitory coupling only during the conscious perception of the illusory orange color. This sustained thalamic disinhibition was temporally coupled with higher visual cortical activation during the conscious perception of the orange color, providing neurophysiological evidence of the role of thalamocortical synchronization in conscious awareness of mental representation. Bayesian model comparison consistently supported the thalamocortical model in conscious perception. Taken together, experimental and theoretical evidence established the thalamocortical inhibitory network as a gateway to conscious mental representations.

Meticulous monitoring for cardiovascular systems is important for postoperative patients in postanesthesia or the intensive care unit. The continuous auscultation of heart and lung sounds can provide a valuable information for patient safety. Although numerous research projects have proposed the design of continuous cardiopulmonary monitoring devices, they primarily focused on the auscultation of heart and lung sounds and mostly served as screening tools. However, there is a lack of devices that could continuously display and monitor the derived cardiopulmonary parameters. This study presents a novel approach to address this need by proposing a bedside monitoring system that utilizes a lightweight and wearable patch sensor for continuous cardiovascular system monitoring. The heart and lung sounds were collected using a chest stethoscope and microphones, and a developed adaptive noise cancellation algorithm was implemented to remove the background noise corrupted with those sounds. Additionally, a short-distance ECG signal was acquired using electrodes and a high precision analog front end. A high-speed processing microcontroller was used to allow real-time data acquisition, processing, and display. A dedicated tablet-based software was developed to display the acquired signal waveforms and the processed cardiovascular parameters. A significant contribution of this work is the seamless integration of continuous auscultation and ECG signal acquisition, thereby enabling the real-time monitoring of cardiovascular parameters. The wearability and lightweight design of the system were achieved through the use of rigid–flex PCBs, which ensured patient comfort and ease of use. The system provides a high-quality signal acquisition and real-time monitoring of the cardiovascular parameters, thus proving its potential as a health monitoring tool.

Lee SW, et al. J Korean Med Sci. 2024 Jul;39:e241. https://doi.org/10.3346/jkms.2024.39.e241