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BackgroundSouth Korea is one of the few countries that has succeeded in flattening the curve of new COVID-19 cases and avoiding a second outbreak by implementing multiple strategies, ranging from an individual level to the population level. ObjectiveWe aim to discuss the unique strategies and epidemiological characteristics of COVID-19 in South Korea and present a summary of policies implemented by the Korean government during the COVID-19 pandemic. MethodsWe designed a cross-sectional study of epidemiological data published by the Korea Centers for Disease Control and Prevention on October 1, 2020. We analyzed detailed epidemiological information of COVID-19 cases, including the number of confirmed cases and resulting deaths. ResultsAs of October 1, 2020, a total of 23,889 confirmed COVID-19 cases and 415 deaths were reported in South Korea. In this paper, we present data on the epidemiological characteristics and transmission of the disease and discuss how the South Korean government, health care providers, and society responded to the COVID-19 outbreak. ConclusionsUnderstanding the epidemiological characteristics of COVID-19 in South Korea and the government’s successful efforts in managing the spread of the disease can provide important insights to other countries dealing with the ongoing pandemic.

We propose a predictive model for various sensory textures utilizing machine learning techniques based on the largest rheology and panel-tested sensory texture database ever assembled. In addition to the conventional rheological parameters typically measured in the cosmetics field, rheological parameters obtained from the large amplitude oscillatory shear (LAOS) sequence of physical processes (SPPs) and extensional rheology analyses are employed as feature variables for the predictive model. These feature variables are chosen to mimic real flow conditions during the usage of cosmetics, such as rubbing and tapping, as they are expected to contain more information related to sensory textures. It has been demonstrated that our prediction model, based on the random forest regression algorithm, can effectively predict five sensory textures: spreadability, thickness, softness, adhesiveness, and stickiness. We investigated the rheological characteristics crucial for determining each sensory texture through permutation and feature importance analyses. The important analysis highlighted the close correlation between rheological parameters from LAOS–SPP, extensional analyses, and sensory textures. By using this correlation, we interpret the perception of each sensory texture in the context of rheology.

Background: This study investigated the image quality and detectability of double low-dose hepatic multiphase CT (DLDCT, which targeted about 30% reductions of both the radiation and iodine concentration) using a vendor-agnostic deep-learning-based contrast-boosting model (DL-CB) compared to those of standard-dose CT (SDCT) using hybrid iterative reconstruction. Methods: The CT images of 73 patients with chronic liver disease who underwent DLDCT between June 2023 and October 2023 and had SDCT were analyzed. Qualitative analysis of the overall image quality, artificial sensation, and liver contour sharpness on the arterial and portal phase, along with the hepatic artery clarity was conducted by two radiologists using a 5-point scale. For quantitative analysis, the image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured. The lesion conspicuity was analyzed using generalized estimating equation analysis. Lesion detection was evaluated using the jackknife free-response receiver operating characteristic figures-of-merit. Results: Compared with SDCT, a significantly lower effective dose (16.4 ± 7.2 mSv vs. 10.4 ± 6.0 mSv, 36.6% reduction) and iodine amount (350 mg iodine/mL vs. 270 mg iodine/mL, 22.9% reduction) were utilized in DLDCT. The mean overall arterial and portal phase image quality scores of DLDCT were significantly higher than SDCT (arterial phase, 4.77 ± 0.45 vs. 4.93 ± 0.24, AUCVGA 0.572 [95% CI, 0.507–0.638]; portal phase, 4.83 ± 0.38 vs. 4.92 ± 0.26, AUCVGA 0.535 [95% CI, 0.469–0.601]). Furthermore, DLDCT showed significantly superior quantitative results for the lesion contrast-to-noise ratio (7.55 ± 4.55 vs. 3.70 ± 2.64, p < 0.001) and lesion detectability (0.97 vs. 0.86, p = 0.003). Conclusions: In patients with chronic liver disease, DLDCT using DL-CB can provide acceptable image quality without impairing the detection and evaluation of hepatic focal lesions compared to SDCT. [ABSTRACT FROM AUTHOR]

The auditory brainstem response (ABR) is a noninvasive test that measures neural activity in response to auditory stimuli. Racial differences in head shape have provided strong evidence for specific normative data and accurate device calibration. International standards emphasize the need for standardized procedures and references. This study aimed to outline the standard procedure and related normative ABR values. Standard procedures were performed according to International Electrotechnical Commission (IEC) standards. Five studies from two countries were included to compare the normative values of the ABR. The dataset from the National Standard Reference Data Center (NSRDC) was used as reference. Normative values were described in terms of stimuli, latency, and amplitude. For click stimuli, the latency of the ABR showed different patterns across populations, such as those from Korea and the USA. Although the latencies reported by the NSRDC and for Koreans were relatively short, those reported for USA populations were longer. Using clicks, it was shown that the USA population had the largest ABR amplitude compared to those reported for the other two datasets. For Wave V latency using tone bursts, a similar pattern was identified with click stimuli. Frequency-specific trends were also observed. Although there is a lack of ABR datasets, the information and insights of the present study could be utilized as standard guidelines in research on ABR. [ABSTRACT FROM AUTHOR]

Background and Aim: Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time‐consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. Methods: A still image‐driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real‐world applicability, the model was validated using 113 10‐s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2–3)" or "inadequate (BBPS 0–1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter‐rater variability was analyzed between the AI model and endoscopists. Results: The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. Conclusions: The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large‐scale screening programs. [ABSTRACT FROM AUTHOR]

The purpose of this retrospective cross-sectional study was to examine the degrees of the cervical disc degeneration and the parameters of cervical sagittal balance in plain radiographs, representing cervical lordosis or head posture in subjects with posterior neck pain. A total of 113 patients with posterior neck pain with or without radiating pain were analyzed. The kinematic sagittal parameters of cervical radiographs were obtained at the occipito–cervical (O–C2) angle, sagittal Cobb’s angles of C1–C2, C2–C7, and sagittal vertical axis (SVA) of C1–C7 and C2–C7. Cervical disc degeneration was evaluated using the sum of Pfirrmann grades and total modified Matsumoto scores calculated from MRI of the cervical spine. A significant positive correlation was found for the C2–C7 angle using the sum of the Pfirrmann grades and total modified Matsumoto scores, whereas the O–C2 angle and the C1–C2 angle were negatively correlated. The sagittal cervical parameters representing cervical kyphosis and jaw lifting posture were found to be significantly correlated with the degree of cervical disc degeneration. These findings suggest that the loss of the natural sagittal lordosis of the cervical spine may contribute to the progression of disc degeneration, rather than the forward head posture.