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Park Chan-kyong is the second artist selected for the \"MMCA Artist Studies\" series. Since the late 1980s, Park has been a member of a community of critics writing art and organizing exhibitions through forums such as the Research Society for Art Criticism (misul bipyeong yeonguhoe), Forum A, BOL. Park took his first steps as an artist with the solo exhibition Black Box: The Memory of Cold War Images, held at the Kumho Museum in 1997, and has since continued to produce film and video works critically examining the Cold War, Korean modernity, and colonialism. Under the title Red Asia Complex, this book examines, from various perspectives, the nearly thirty-years trajectory of Park Chan-kyong's art and writing.--Gallery Website.

This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node ratio (LNR), in patients with papillary thyroid carcinoma (PTC), and attempted to construct a disease recurrence prediction model using machine learning techniques. We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with PTC between 2003 and 2009. We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Among those factors mentioned above, LNR and contralateral LN metastasis were used as important features in all machine learning prediction models. We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. LNR and contralateral LN metastasis were used as important features for constructing a robust machine learning prediction model. In the future, we have a plan to perform large-scale multicenter clinical studies to improve the performance of our prediction models and verify their clinical effectiveness.

Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve.

Cryptococcus neoformans is the leading cause of death by fungal meningoencephalitis; however, treatment options remain limited. Here we report the construction of 264 signature-tagged gene-deletion strains for 129 putative kinases, and examine their phenotypic traits under 30 distinct in vitro growth conditions and in two different hosts (insect larvae and mice). Clustering analysis of in vitro phenotypic traits indicates that several of these kinases have roles in known signalling pathways, and identifies hitherto uncharacterized signalling cascades. Virulence assays in the insect and mouse models provide evidence of pathogenicity-related roles for 63 kinases involved in the following biological categories: growth and cell cycle, nutrient metabolism, stress response and adaptation, cell signalling, cell polarity and morphology, vacuole trafficking, transfer RNA (tRNA) modification and other functions. Our study provides insights into the pathobiological signalling circuitry of C. neoformans and identifies potential anticryptococcal or antifungal drug targets. Cryptococcus neoformans is the leading cause of death by fungal meningoencephalitis. Here, the authors study the roles played by 129 putative kinases in the growth and virulence of C. neoformans , identifying potential targets for development of anticryptococcal drugs.

The gut microbiota is considered a relevant factor in obesity and associated metabolic diseases, for which postmenopausal women are particularly at risk. Increasing physical activity has been recognized as an efficacious approach to prevent or treat obesity, yet the impact of physical activity on the microbiota remains under-investigated. We examined the impacts of voluntary exercise on host metabolism and gut microbiota in ovariectomized (OVX) high capacity (HCR) and low capacity running (LCR) rats. HCR and LCR rats (age = 27 wk) were OVX and fed a high-fat diet (45% kcal fat) ad libitum and housed in cages equipped with (exercise, EX) or without (sedentary, SED) running wheels for 11 wk (n = 7-8/group). We hypothesized that increased physical activity would hinder weight gain, increase metabolic health and shift the microbiota of LCR rats, resulting in populations more similar to that of HCR rats. Animals were compared for characteristic metabolic parameters including body composition, lipid profile and energy expenditure; whereas cecal digesta were collected for DNA extraction. 16S rRNA gene-based amplicon Illumina MiSeq sequencing was performed, followed by analysis using QIIME 1.8.0 to assess cecal microbiota. Voluntary exercise decreased body and fat mass, and normalized fasting NEFA concentrations of LCR rats, despite only running one-third the distance of HCR rats. Exercise, however, increased food intake, weight gain and fat mass of HCR rats. Exercise clustered the gut microbial community of LCR rats, which separated them from the other groups. Assessments of specific taxa revealed significant (p<0.05) line by exercise interactions including shifts in the abundances of Firmicutes, Proteobacteria, and Cyanobacteria. Relative abundance of Christensenellaceae family was higher (p = 0.026) in HCR than LCR rats, and positively correlated (p<0.05) with food intake, body weight and running distance. These findings demonstrate that exercise differentially impacts host metabolism and gut microbial communities of female HCR and LCR rats without ovarian function.

Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to determine whether convolutional neural networks (CNNs) could assess erythema, papulation, excoriation, and lichenification severity at a level of competence comparable to dermatologists. We created a standard dataset of 8,000 clinical images showing AD. Each component of the EASI was scored from 0 to 3 by three dermatologists. We trained four CNNs (ResNet V1, ResNet V2, GoogLeNet, and VGG-Net) with the image dataset and determined which CNN was the most suitable for erythema, papulation, excoriation, and lichenification scoring. The brightness of the images in each dataset was adjusted to − 80% to + 80% of the original brightness (i.e., 9 levels by 20%) to investigate if the CNNs accurately measured scores if image brightness levels were changed. Compared to the dermatologists’ scoring, accuracy rates of the CNNs were 99.17% for erythema, 93.17% for papulation, 96.00% for excoriation, and 97.17% for lichenification. CNNs trained with brightness-adjusted images achieved a high accuracy without the need to standardize camera settings. These results suggested that CNNs perform at level of competence comparable to dermatologists for scoring erythema, papulation, excoriation, and lichenification severity.

Clinical trials evaluating the rate of short-term selective serotonin-reuptake inhibitor (SSRI)-induced hyperprolactinemia have produced conflicting results. Thus, the aim of this study was to clarify whether SSRI therapy can induce hyperprolactinemia and macroprolactinemia. Fifty-five patients with major depressive disorder (MDD) were enrolled in this study. Serum prolactin and macroprolactin levels were measured at a single time point (i.e., in a cross-sectional design). All patients had received SSRI monotherapy (escitalopram, paroxetine, or sertraline) for a mean of 14.75 months. Their mean prolactin level was 15.26 ng/ml. The prevalence of patients with hyperprolactinemia was 10.9% for 6/55, while that of patients with macroprolactinemia was 3.6% for 2/55. The mean prolactin levels were 51.36 and 10.84 ng/ml among those with hyperprolactinemia and a normal prolactin level, respectively. The prolactin level and prevalence of hyperprolactinemia did not differ significantly within each SSRI group. Correlation analysis revealed that there was no correlation between the dosage of each SSRI and prolactin level. These findings suggest that SSRI therapy can induce hyperprolactinemia in patients with MDD. Clinicians should measure and monitor serum prolactin levels, even when both SSRIs and antipsychotics are administered.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is highly contagious and causes coronavirus disease 2019 (COVID-19). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most accurate and reliable molecular assay to detect active SARS-CoV-2 infection. However, a rapid increase in test subjects has created a global bottleneck in testing capacity. Given that efficient nucleic acid extraction greatly affects reliable and accurate testing results, we compared three extraction platforms: MagNA Pure 96 DNA and Viral NA Small Volume kit on MagNA Pure 96 (Roche, Basel, Switzerland), careGENETM Viral/Pathogen HiFi Nucleic Acid Isolation kit (WELLS BIO Inc., Seoul, Korea) on KingFisher Flex (Thermo Fisher Scientific, Rocklin, CA, USA), and SGRespiTM Pure kit (Seegene Inc., Seoul, Korea) on Maelstrom 9600 (Taiwan Advanced Nanotech Inc., Taoyuan, Taiwan). RNA was extracted from 245 residual respiratory specimens from the different types of samples (i.e., NPS, sputum, and saliva) using three different kits. The 95% limits of detection of median tissue culture infectious dose per milliliter (TCID50/mL) for the MagNA Pure 96, KingFisher Flex, and Maelstrom 9600 were 0.37–3.15 × 101, 0.41–3.62 × 101, and 0.33–1.98 × 101, respectively. The KingFisher Flex platform exhibited 99.2% sensitivity and 100% specificity, whereas Maelstrom 9600 exhibited 98.3–100% sensitivity and 100% specificity. Bland–Altman analysis revealed a 95.2% concordance between MagNA Pure 96 and KingFisher Flex and 95.4% concordance between MagNA Pure 96 and Maelstrom 9600, indicating that all three platforms provided statistically reliable results. This suggests that two modifying platforms, KingFisher Flex and Maelstrom 9600, are accurate and scalable extraction platforms for large-scale SARS-CoV-2 clinical detection and could help the management of COVID-19 patients.

Objectives: This study investigated the effects of hot and cold temperature on the renal function of people with chronic diseases, such as diabetes, hypertension, and chronic kidney disease, using large-scale clinical data.Methods: We used retrospective cohort data from the Clinical Data Warehouse of the Seoul St Mary’s Hospital, which contains clinical, diagnostic, laboratory, and other information about all patients who have visited the hospital since 1997. We obtained climate data from the Automated Synoptic Observing System of the Korea Meteorological Administration. The heat index was used as a measuring tool to evaluate heat exposure by indexing the actual heat that individuals feel according to temperature and humidity. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. To investigate changes in renal function trends with heat index, this study used generalized additive mixed models.Results: Renal function decreased linearly with increasing heat index after approximately 25°C, which was considered the flexion point of temperature. A linear decrease in the eGFR was observed with the effects of 0 to 5 lag days. Although there was a correlation observed between the decrease in eGFR and temperatures below −10°C, the results did not indicate statistical significance.Conclusions: The results of our study provide scientific evidence that high temperatures affect the renal function of people with chronic diseases. These results can help prevent heat-related morbidity by identifying those who are more likely to develop renal disease and experience worsening renal function.

Muscle mass and physical function are key risk factors for sarcopenia, with emerging evidence suggesting links to gait variability in older adults. This study investigated the relationships between muscle mass decline, poor physical performance, sarcopenia, and spatiotemporal gait parameters in 242 Korean older adults (mean age: 79.1 ± 4.2 years). Muscle mass (MM) was measured via dual-energy X-ray absorptiometry (DXA), physical performance (PP) via the Short Physical Performance Battery (SPPB), and gait parameters (gait speed, stride length, double-limb stance) via the Optogait® system. Stride length significantly influenced low MM, while double-limb stance was linked to increased risks of poor PP and sarcopenia. The area under the curve (AUC) for double-limb stance was 0.698 (95% CI: 0.633–0.763, p < 0.001) for poor PP and 0.647 (95% CI: 0.568–0.726, p = 0.001) for sarcopenia. A novel model combining gait speed and double-limb stance achieved an AUC of 0.702 (95% CI: 0.622–0.781, p < 0.001) with 78.9% sensitivity and 76.3% specificity. These findings highlight spatiotemporal gait analysis as a promising tool for early sarcopenia detection and management in older adults.