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The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

The objective of this study was to evaluate the effects of diabetes mellitus (DM) on the rate of carpal tunnel release (CTR) using a large nationwide cohort in Korea and to identify risk factors, including comorbidities and socioeconomic status (SES), associated with CTR. Patients with a primary or secondary diagnosis of carpal tunnel syndrome (CTS; ICD-10 code: G560) were selected and divided into two groups according to the presence of DM. A Cox proportional hazard model was used to assess the rate of CTR between the two groups. To evaluate the influence of demographic factors, comorbidities, and SES on CTR, multivariate Cox proportional hazard regression models were used to adjust for confounding variables. In total, 12,419 patients with CTS were included in the study: 2487 in DM cohort and 9932 in non-DM cohort. DM duration was negatively related with the rate of CTR (HR = 0.89, 95% CI 0.87–0.91) in CTS patients with DM. The rate of CTR was decreased in patients with DM compared to those without DM in the unadjusted model; however, after adjusting for comorbidities, DM had no significant effect on the rate of CTR. Female sex (HR = 1.50, 95% CI 1.36–1.67) correlated with the rate of CTR, and an inverse relationship between the number of comorbidities and CTR was found (p

Synbiotics are feed supplements with the potential to promote health and productivity in pigs partly, through modulation of the intestinal microbiome. Our study used shotgun sequencing and 16S rRNA gene sequencing techniques to characterize the effect of a synbiotic containing three Lactobacillus species and a fructo-oligosaccharide on the proximal colonic microbiome of 4- to 7-month-old Korean native black gilts. With shotgun sequencing we constructed unique metagenome-assembled genomes of gut microbiota in Native Black Pig for the first time, which we then used for downstream analysis. Results showed that synbiotic treatment did not alter microbial diversity and evenness within the proximal colons, but altered composition of some members of the Lactobacillaceae, Enterococcaceae and Streptococcaceae families. Functional analysis of the shotgun sequence data revealed 8 clusters of orthologous groups (COGs) that were differentially represented in the proximal colonic microbiomes of synbiotic-treated Jeju black pigs relative to controls. In conclusion, our results show that administering this synbiotic causes changes in the functional capacity of the proximal colonic microbiome of the Korean native black pig. This study improves our understanding of the potential impact of synbiotics on the colonic microbiome of Korean native black pigs.

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

Underlying chronic respiratory disease may be associated with the severity of coronavirus disease 2019 (COVID-19). This study investigated the impact of chronic obstructive pulmonary disease (COPD) on the risk for respiratory failure and mortality in COVID-19 patients. A nationwide retrospective cohort study was conducted in 4610 patients (≥ 40 years old) infected with COVID-19 between January 20 and May 27, 2020, using data from the Ministry of Health and Welfare and Health Insurance Review and Assessment Service in Korea. The clinical course and various clinical features were compared between COPD and non-COPD patients, and the risks of respiratory failure and all-cause mortality in COPD patients were analyzed using a multivariate logistic regression model. Among 4610 COVID-19 patients, 4469 (96.9%) and 141 (3.1%) were categorized into the non-COPD and COPD groups, respectively. The COPD group had greater proportions of older (≥ 60 years old) (78.0% vs. 45.2%, P P P = 0.041) and mechanical ventilation (5.7% vs. 2.4%, P = 0.015). Multivariate analyses showed that COPD was not a risk factor for respiratory failure but was a significant independent risk factor for all-cause mortality (OR = 1.80, 95% CI 1.11–2.93) after adjustment for age, sex, and Charlson Comorbidity Index score. Among COVID-19 patients, relatively greater proportions of patients with COPD received mechanical ventilation and intensive critical care. COPD is an independent risk factor for all-cause mortality in COVID-19 patients in Korea.

Nurr1, a transcription factor belonging to the orphan nuclear receptor, has an essential role in the generation and maintenance of dopaminergic neurons and is important in the pathogenesis of Parkinson’ disease (PD). In addition, Nurr1 has a non-neuronal function, and it is especially well known that Nurr1 has an anti-inflammatory function in the Parkinson’s disease model. However, the molecular mechanisms of Nurr1 have not been elucidated. In this study, we describe a novel mechanism of Nurr1 function. To provide new insights into the molecular mechanisms of Nurr1 in the inflammatory response, we performed Chromatin immunoprecipitation sequencing (ChIP-Seq) on LPS-induced inflammation in BV2 cells and finally identified the RasGRP1 gene as a novel target of Nurr1. Here, we show that Nurr1 directly binds to the RasGRP1 intron to regulate its expression. Moreover, we also identified that RasGRP1 regulates the Ras-Raf-MEK-ERK signaling cascade in LPS-induced inflammation signaling. Finally, we conclude that RasGRP1 is a novel regulator of Nurr1’s mediated inflammation signaling.

Postoperative pulmonary complications (PPCs) significantly impact surgical outcome. We investigated the predictive ability of controlling nutritional status (CONUT) for PPC after lung resection in patients with non-small cell lung cancer (NSCLC). We retrospectively reviewed data of 922 patients with NSCLC who underwent complete resection from January 2016–December 2017. We analyzed the frequency and characteristics of PPCs and compared receiver operating characteristic (ROC) curves of various prognostic models to predict PPCs. A CONUT score higher than 1 was considered as a high CONUT score. Total incidence of PPCs was 8.6% (n = 79). The proportion of pneumonia was significantly larger in the high CONUT group (P P = 0.002] and high CONUT score (OR = 1.91, P = 0.009) as independent PPCs prognostic factors. One-year mortality rate for high CONUT score was significantly higher (hazard ratio = 7.97; 95% confidence interval, 1.78–35.59). Preoperative CONUT score is an independent predictor of PPCs and 1-year mortality in patients with resectable NSCLC.

Human endothelial progenitor cells (hEPCs) are promising therapeutic resources for wound repair through stimulating neovascularization. However, the hEPCs-based cell therapy has been hampered by poor engraftment of transplanted cells. In this study, we explored the effects of N-acetylated Proline-Glycine-Proline (Ac-PGP), a degradation product of collagen, on hEPC-mediated cutaneous wound healing and neovascularization. Treatment of hEPCs with Ac-PGP increased migration, proliferation, and tube-forming activity of hEPCs in vitro. Knockdown of CXCR2 expression in hEPCs abrogated the stimulatory effects of Ac-PGP on migration and tube formation. In a cutaneous wound healing model of rats and mice, topical application of Ac-PGP accelerated cutaneous wound healing with promotion of neovascularization. The positive effects of Ac-PGP on wound healing and neovascularization were blocked in CXCR2 knockout mice. In nude mice, the individual application of Ac-PGP treatment or hEPC injection accelerated wound healing by increasing neovascularization. Moreover, the combination of Ac-PGP treatment and hEPC injection further stimulated wound healing and neovascularization. Topical administration of Ac-PGP onto wound bed stimulated migration and engraftment of transplanted hEPCs into cutaneous dermal wounds. Therefore, these results suggest novel applications of Ac-PGP in promoting wound healing and augmenting the therapeutic efficacy of hEPCs.