Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data. Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis. Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease. Conclusion Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

A single exposure to stress can induce functional changes in neurons, potentially leading to acute stress disorder or post-traumatic stress disorder.In this study, we used in vivo wide-field optical mapping to simultaneously measure neural calcium signals and hemodynamic responses over the whole cortical area. We found that cortical mapping to whisker stimuli was altered under acute stress conditions. In particular, callosal projections in the anterior cortex (primary/secondary motor, somatosensory forelimb cortex) relative to barrel field (S1BF) of somatosensory cortex were weakened. On the contrary, the projections in posterior cortex relative to S1BF were mostly unchanged or were only occasionally strengthened. In addition, changes in intra-cortical connection were opposite to those in inter-cortical connection. Thus, the S1BF connections to the anterior cortex were strengthened while those to the posterior cortex were weakened. This suggests that the well-known barrel cortex projection route was enhanced. In summary, our in vivo wide-field optical mapping study indicates that a single acute stress can impact whole-brain networks, affecting both neural and hemodynamic responses.

BACKGROUND: Retinal degenerative disease (RDD), one of the most common causes of blindness, is predominantly caused by the gradual death of retinal pigment epithelial cells (RPEs) and photoreceptors due to various causes. Cell-based therapies, such as stem cell implantation, have been developed for the treatment of RDD, but potential risks, including teratogenicity and immune reactions, have hampered their clinical application. Stem cell-derived extracellular vesicles (EVs) have recently emerged as a cell-free alternative therapeutic strategy; however, additional invasiveness and low yield of the stem cell extraction process is problematic. METHODS: To overcome these limitations, we developed therapeutic EVs for the treatment of RDD which were extracted from tonsil-derived mesenchymal stem cells obtained from human tonsil tissue discarded as medical waste following tonsillectomy (T-MSC EVs). To verify the biocompatibility and cytoprotective effect of T-MSC EVs, we measured cell viability by co-culture with human RPE without or with toxic all-trans-retinal. To elucidate the cytoprotective mechanism of T-MSC EVs, we performed transcriptome sequencing using RNA extracted from RPEs. The in vivo protective effect of T-MSC EVs was evaluated using Pde6b gene knockout rats as an animal model of retinitis pigmentosa. RESULTS: T-MSC EVs showed high biocompatibility and the human pigment epithelial cells were significantly protected in the presence of T-MSC EVs from the toxic effect of all-trans-retinal. In addition, T-MSC EVs showed a dosedependent cell death-delaying effect in real-time quantification of cell death. Transcriptome sequencing analysis revealed that the efficient ability of T-MSC EVs to regulate intracellular oxidative stress may be one of the reasons explaining their excellent cytoprotective effect. Additionally, intravitreally injected T-MSC EVs had an inhibitory effect on the destruction of the outer nuclear layer in the Pde6b gene knockout rat. CONCLUSIONS Together, the results of this study indicate the preventive and therapeutic effects of T-MSC EVs during the initiation and development of retinal degeneration, which may be a beneficial alternative for the treatment of RDD.

Background/Aims: To compare the performance of the Liver Imaging Reporting and Data System (LI-RADS) v2018 and Korean Liver Cancer Association-National Cancer Center (KLCANCC) 2018 criteria for diagnosing hepatocellular carcinoma (HCC) using magnetic resonance imaging (MRI) with hepatobiliary agent (HBA). Methods: We searched the MEDLINE and EMBASE for studies from January 1, 2018, to October 20, 2021, that compared the diagnostic performance of two imaging criteria on HBA-MRI. A bivariate random-effects model was fitted to calculate the per-observation sensitivity and specificity, and the estimates of paired data were compared. Subgroup analysis was performed based on the observation size. Meta-regression analysis was also performed for study heterogeneity. Results: Of the six studies included, the pooled sensitivity of the definite HCC category of the KLCA-NCC criteria (82%; 95% confidence interval [CI], 74% to 90%; I 2 =84%) was higher than that of LR-5 of LI-RADS v2018 (65%; 95% CI, 52% to 77%; I 2 =96%) for diagnosing HCC (p<0.001), while the specificity was lower for KLCA-NCC criteria (87%; 95% CI, 84% to 91%; I 2 =0%) than LI-RADS v2018 (93%; 95% CI, 91% to 96%; I 2 =0%) (p=0.017). For observations sized ≥20 mm, the sensitivity was higher for KLCA-NCC 2018 than for LI-RADS v2018 (84% vs 74%, p=0.012), with no significant difference in specificity (81% vs 85%, p=0.451). The reference standard was a significant factor contributing to the heterogeneity of sensitivities. Conclusions The definite HCC category of KLCA-NCC 2018 provided a higher sensitivity and lower specificity than the LR-5 of LI-RADS v2018 for diagnosing HCC using MRI with HBA.

Purpose: A meta-analysis was conducted to determine the proportion of contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System category M (LR-M) in hepatocellular carcinomas (HCCs) and non-HCC malignancies and to investigate the frequency of individual CEUS LR-M imaging features. Methods: The MEDLINE and Embase databases were searched from January 1, 2016 to July 23, 2020 for studies reporting the proportion of CEUS LR-M in HCC and non-HCC malignancies. The meta-analytic pooled proportions of HCC and non-HCC malignancies in the CEUS LR-M category were calculated. The meta-analytic frequencies of CEUS LR-M imaging features in nonHCC malignancies were also determined. Risk of bias and applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results: Twelve studies reporting the diagnostic performance of the CEUS LR-M category were identified, as well as seven studies reporting the frequencies of individual CEUS LR-M imaging features. The pooled proportions of HCC and non-HCC malignancies in the CEUS LR-M category were 54% (95% confidence interval [CI], 44% to 65%) and 40% (95% CI, 28% to 53%), respectively. The pooled frequencies of individual CEUS LR-M imaging features in non-HCC malignancies were 30% (95% CI, 17% to 45%) for rim arterial phase hyperenhancement, 79% (95% CI, 66% to 90%) for early (<60 s) washout, and 42% (95% CI, 21% to 64%) for marked washout. Conclusion In total, 94% of CEUS LR-M lesions were malignancies, with HCCs representing 54% and non-HCC malignancies representing 40%. The frequencies of individual CEUS LR-M imaging features varied; early washout showed the highest frequency for non-HCC malignancies.

Purpose: To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods: A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30–50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions.Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results: The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS.

Purpose: To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods: A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30–50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions.Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results: The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS.

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3–80.0% and a specificity of 78.9–89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7–79.0% and a specificity of 93.3–100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66–0.96). Conclusion The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

Objective: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. Materials and Methods: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. Results: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3–80.0% and a specificity of 78.9–89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7–79.0% and a specificity of 93.3–100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Interreader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66–0.96). Conclusion The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.