Surveillant institutional eyes in South Korea

노트 : This article was originally presented at the workshop, 10–11 February 2006, The Center for Interdisciplinary Research (ZiF), Bielefeld University, Germany

Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells

Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectroscopic microscopy, emphasizing their potential for studying LDs without molecular labels, and we highlight the growing integration of artificial intelligence. Clinical applications in disease diagnosis and therapy are also considered

Gender and Measuring-position Differences in the Radial Pulse of Healthy Individuals

AbstractIn this research, radial pulse differences according to gender and measuring positions in healthy individuals were investigated in an objective manner. A total of 372 healthy volunteers (189 males and 183 females) participated in this study. The radial pulse was measured at six different measuring positions using a multistep tonometry system. The pulse data were compared between males and females and between different measuring positions. Compared to the pulses in females, those in males were deeper and slower, with a longer diastolic proportion and a shorter systolic proportion. Amplitude of the radial pulse increased as it went distal. The pulse was deepest at the Cheock position and shallowest at the Gwan position. Compared to the right pulse, the radial augmentation index was higher and the main peak angle was larger in case of the left pulse. The results of this research show that the radial pulses in healthy individuals differ significantly according to gender and measuring positions

Prediction of Individual Propofol Requirements based on Preoperative EEG Signals

The patient must be given an adequate amount of propofol for safe surgery since overcapacity and low capacity cause accidents. However, the sensitivity of propofol varies from patient to patient, making it very difficult to determine the propofol requirements for anesthesia. This paper aims to propose a neurophysiological predictor of propofol requirements based on the preoperative electroencephalogram (EEG). We exploited the canonical correlation analysis that infers the amount of information on the propofol requirements. The results showed that the preoperative EEG included the factor that could explain the propofol requirements. Specifically, the frontal and posterior regions had crucial information on the propofol requirements. Moreover, there was a significantly different power in the frontal and posterior regions between baseline and unconsciousness periods, unlike the alpha power in the central region. These findings showed the potential that preoperative EEG could predict the propofol requirements.Comment: 5 pages, 1 figure, 1 tabl

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described