BACKGROUND: The accumulation of lipoprotein and monocyte in the intima of the arterial wall is the most important step of the development of coronary artery disease (CAD). Lipoprotein lipase (LPL) plays an anti-atherogenic role by lipolysis of triglyceride-rich lipoproteins, but, it may also act as a receptor of some lipoproteins and monocyte at the arterial wall and act as a atherogenic molecule. Previous studies showed somewhat contradictory results about the association of CAD and LPL polymorphisms and mutations. Racial and dietary difference may contribute to these contradictory results. In this study, we tried to find out the association of CAD and the genetic variation of the LPL (PvuII RFLP in intron 6, HindIII RFLP in intron 8 and Ser 447 Ter mutation in exon 9) in Korean population. METHOD AND RESULT: CAD patients (n=146), confirmed by coronary angiography and healthy Korean adult volunteers (n=110) were genotyped for PvuII/HindIII RFLP and Ser447Ter mutation of the LPL gene by PCR-digestion method. Between two groups, the genotype frequency of these genetic variations was not different. But, the genetic variations showed different effect on lipid profile and body mass index (BMI) in the CAD group and in the control group. In the CAD group, P1 allele carriers showed higher total cholesterol (P1P1+P1P2:P2P2=216+-51 mg/dl:198+/-38 mg/dl, p=0.039) and higher LDL cholesterol level (P1P1+P1P2:P2P2=143+/-46 mg/dl:126+/-36 mg/dl, p=0.047), and H1 allele carriers had lower Body mass index than non-carriers (23.8+/-2.3 kg/m2 :24.8+/-2.9 kg/m2 , p=0.047). In the control group, the Ser447Ter mutation carriers had higher HDL cholesterol level than non-carriers (59+/-10mg/dl versus 53+/-11mg/dl, p=0.049) and patients with P1 allele showed lower body mass index (P1P1+P1P2: P2P2=23.1+/-2.6 kg/m 2 :24.5+/-2.6 kg/m2 , p=0.006). CONCLUSION: In Korean, PvuII/HindIII RFLP and Ser447Ter mutation was not associated with CAD, and they showed different effect on the lipid profile and on the body mass index according to the study group. These results suggests that the phenotypic characteristics of the LPL gene of the Korean people are different from those of occidental people.
Adult ; Alleles ; Body Mass Index ; Cholesterol ; Cholesterol, HDL ; Cholesterol, LDL ; Coronary Angiography ; Coronary Artery Disease* ; Coronary Vessels* ; Exons ; Genetic Variation ; Genotype ; Humans ; Introns ; Lipolysis ; Lipoprotein Lipase* ; Lipoproteins* ; Monocytes ; Polymorphism, Restriction Fragment Length ; Volunteers

PURPOSE: The avian origin canine influenza virus H3N2 has been recently isolated and found to be currently in dog population in South Korea and China. The purpose of this study was to clarify the relationship between immunosuppressive glucocorticoids used in veterinary clinical practice and viral shedding pattern of influenza in dogs. MATERIALS AND METHODS: Eight conventional beagle dogs were divided into control infection group and immunocompromised group. Dogs of both groups were infected with H3N2 canine influenza virus (2x106.0 EID50/0.1 mL). Dogs in immunocompromised group were given orally 3.0 mg/kg prednisolone for 7 days. Virus shedding was monitored using real-time polymerase chain reaction. After necropsy, histopathologic lesions were compared. RESULTS: We found that immunocompromised dogs exhibited more prolonged (8 days vs. 13 days) and higher magnitude viral shedding than control group (peak titer of viral shedding 4.6 vs. 5.5 EID50). CONCLUSION: Restricted use of immunosuppressive drugs in the clinical setting might help control the rapid spread of H3N2 through local dog populations.
Animals ; China ; Dogs ; Glucocorticoids ; Immunosuppression ; Influenza A Virus, H3N2 Subtype ; Influenza, Human ; Orthomyxoviridae ; Prednisolone ; Real-Time Polymerase Chain Reaction ; Republic of Korea ; Viral Load ; Virus Shedding

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS: The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.
Cardiopulmonary Resuscitation ; Epidemiology ; Heart Arrest ; Holidays ; Hospital Mortality ; Incidence ; Korea ; Mortality ; Out-of-Hospital Cardiac Arrest ; Prospective Studies

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS: The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
Artificial Intelligence ; Early Diagnosis ; Echocardiography ; Electrocardiography ; Forests ; Heart Failure ; Heart ; Humans ; Learning ; Logistic Models ; Machine Learning ; Mass Screening ; ROC Curve