Anti-Müllerian hormone (AMH) is now accepted as an important clinical marker of ovarian reserve and is increasingly measured as an initial evaluation at infertility clinics. The aim of this study was to establish reference values for the revised second generation (Gen II) assay using population-based data. In this population-based cohort study, AMH data from unselected infertile women aged 25–45 years from June 2013 to June 2014 (n = 15,801) were collected. The AMH values were measured using the revised Gen II assay. We established and validated 5 AMH-age regression models. Based on the optimal AMH-age model, reference values and centile charts were obtained. The quadratic model (log AMH = 0.410 × age − 0.008 × age²− 3.791) was the most appropriate for describing the age-dependent decrease in AMH measured using the revised Gen II assay. This is the largest population-based study to establish age-specific reference values of AMH using the revised Gen II assay. These reference values may provide more specific information regarding the ovarian reserve estimation of infertile women.
Biomarkers ; Cohort Studies ; Female ; Humans ; Infertility ; Ovarian Reserve ; Reference Values*

OBJECTIVE: To investigate the epidemiology of sepsis in Korea and identify risk factors for death in sepsis.METHODS: We conducted a longitudinal, population-based epidemiological study of sepsis in Korea from 2005 to 2012 using the National Health Insurance Service-National Sample Cohort, a population-based cohort representing 2.2% of the Korean population. The primary objective was to assess the incidence, mortality and cost of sepsis. The secondary objective was to identify the risk factors for death in sepsis. Claim records of admitted adult patients (aged ≥15 years) were analyzed. Sepsis was defined as 1) bacterial or fungal infection or the conditions they often complicate, 2) prescription of intravenous antibiotics, and 3) presence of any organ dysfunction. Comorbidities were defined using the Charlson/Deyo method. Risk factors for 6-month mortality were assessed using multivariable logistic regression.RESULTS: A total of 22,882 cases were identified. Both incidence and 6-month mortality increased from 265.7 (95% confidence interval [CI], 254.7 to 277.1) to 453.1 (95% CI, 439.0 to 467.5) per 100,000 person-years (P-trend <0.001) and from 26.5% (95% CI, 24.4% to 28.8%) to 30.1% (95% CI, 28.4% to 31.9%), respectively. After standardization, the increasing trend of incidence was slower but still significant (P-trend <0.001), while that for mortality was not (P-trend 0.883). The average cost increased by 75.5% (P-trend <0.001). Multivariable logistic regression identified various risk factors for mortality.CONCLUSION: The burden of sepsis in Korea was high and is expected to increase considering the aging population. Proactive measures to curtail this increase should be sought and implemented.
Adult ; Aging ; Anti-Bacterial Agents ; Cohort Studies ; Comorbidity ; Epidemiologic Studies ; Epidemiology ; Humans ; Incidence ; Korea ; Logistic Models ; Methods ; Mortality ; National Health Programs ; Prescriptions ; Risk Factors ; Sepsis

Bioinformatics, the study of integrating high throughput biological data and statistical model through intensive computation, has been attracting great interest in recent times and Sequencing is at the very center of it. The large amount of information obtained from sequencing has deepened our understanding and fundamental knowledge of organisms. This review will aim to provide a brief summary of new sequencing technology, current issues, and projects focused on medical applications. The article is organized in three parts. Part I explains common terminologies and background of sequencing technology, and Part II compares distinct features of currently available platforms. Part III contains applications in various medical fields.
Computational Biology ; High-Throughput Nucleotide Sequencing ; Models, Statistical

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

OBJECTIVES: We aimed to investigate the acute effects of heat stress on body temperature and blood pressure of elderly individuals living in poor housing conditions. METHODS: Repeated measurements of the indoor temperature, relative humidity, body temperature, and blood pressure were conducted for 20 elderly individuals living in low-cost dosshouses in Seoul during hot summer days in 2010. Changes in the body temperature, systolic blood pressure (SBP) and diastolic blood pressure (DBP) according to variations in the indoor and outdoor temperature and humidity were analyzed using a repeated-measures ANOVA controlling for age, sex, alcohol, and smoking. RESULTS: Average indoor and outdoor temperatures were 31.47degrees C (standard deviation [SD], 0.97degrees C) and 28.15degrees C (SD, 2.03degrees C), respectively. Body temperature increased by 0.21degrees C (95% confidence interval [CI], 0.16 to 0.26degrees C) and 0.07degrees C (95% CI, 0.04 to 0.10degrees C) with an increase in the indoor and outdoor temperature of 1degrees C. DBP decreased by 2.05 mmHg (95% CI, 0.05 to 4.05 mmHg), showing a statistical significance, as the indoor temperature increased by 1degrees C, while it increased by 0.20 mmHg (95% CI, -0.83 to 1.22 mmHg) as outdoor temperature increased by 1degrees C. SBP decreased by 1.75 mmHg (95% CI, -1.11 to 4.61 mmHg) and 0.35 mmHg (95% CI, -1.04 to 1.73 mmHg), as the indoor and outdoor temperature increased by 1degrees C, respectively. The effects of relative humidity on SBP and DBP were not statistically significant for both indoor and outdoor. CONCLUSIONS: The poor and elderly are directly exposed to heat waves, while their vital signs respond sensitively to increase in temperature. Careful adaptation strategies to climate change considering socioeconomic status are therefore necessary.
Aged ; Blood Pressure ; Body Temperature ; Climate Change ; Hot Temperature ; Housing ; Humans ; Humidity ; Infrared Rays ; Social Class ; Vital Signs

Background: Concern about bisphosphonate-associated subtrochanteric and femoral shaft (ST/FS) fractures has been raised. However, its real risk is still debatable, because there is no study to estimate risk and benefit of bisphosphonate. The objective of this study was to evaluate the risk of typical hip fractures and ST/FS fractures among bisphosphonate users using nationwide database. Methods: We performed a retrospective cohort study using National Health Insurance Service-National Sample Cohort. We evaluated occurrence of the ST/FS and femoral neck and intertrochanteric (FN/IT) fractures among female bisphosphonate new users. Incidence rate of ST/FS and FN/IT fractures were compared between long-term users (≥ 1 year) and short-term users (< 1 year). Number needed to harm (NNH) for ST/FS and number needed to treat (NNT) for typical hip fracture were analyzed. Results: Among 46,420 bisphosphonate users, we identified 14,689 long-term users and 21,840 short-term users. During the study period, 61 long-term users and 36 short-term users had ST/FS fractures, while 204 long-term users and 511 short-term users had FN/IT fractures. The long-term user showed higher incidence rate of ST/FS fractures (67.1/100,000 person-years; 95% confidence interval [CI], 50.3–83.9) comparing with 31.2/100,000 person-years (95% CI, 21.0–41.4) in the short-term users. The incidence rate of FN/IT fractures was 225.5/100,000 person-years (95% CI, 194.6–256.5) in the long-term users and 448.6/100,000 person-years (95% CI, 409.7–487.5) in the short-term users. The NNH for ST/FS was 400, while the NNT for typical hip fracture was 105. Conclusion Our study suggested that physicians keep the significant benefit of bisphosphonate to prevent typical hip fracture in mind, even the concerns about bisphosphonate-associated ST/FS fractures.

Objectives: Periodontitis is the most common chronic disease that causes tooth loss and is related to systemic diseases such as cardiovascular dis-ease and diabetes. An objective indicator of the current activity of periodontitis is necessary. Soluble forms of the receptor for advanced glycation endproducts (sRAGE) are markers that reflect the status of inflammatory diseases. In this study, the relationship between sRAGE and periodontitis was analyzed to determine whether it can be used to diagnose the current state of periodontitis. Patients and Methods: Eighty-four patients without any systemic diseases were diagnosed with periodontitis using three classifications of periodontitis. Demographics and oral examination data such as plaque index (PI), bleeding on probing (BOP) index, and probing pocket depth (PPD) were analyzed according to each classification. In addition, correlation and partial correlation between sRAGE and the values indicating periodontitis were analyzed. Results: In each classification, the level of sRAGE tended to decrease if periodontitis was present or severe, but this change was not statistically significant. sRAGE and periodontitis-related variables exhibited a weak correlation, among which the BOP index showed a relatively strong negative cor-relation (ρ=–0.20). Based on this, on analyzing the correlation between the BOP index and sRAGE in the group with more severe periodontitis (PPD≥5 mm group, severe group of AAP/CDC [American Academy of Periodontology/Centers for Disease Control and Prevention], periodontitis group of López), the correlation further increased (ρ=–0.23, –0.40, –0.50). Partial correlation analysis of the sRAGE and BOP index showed a stronger negative correlation (ρ=–0.36, –0.55, –0.45). Conclusion sRAGE demonstrated a tendency to decrease upon increased severity of periodontitis according to the classifications used. Above all, the correlation with the BOP index, which reflects the current state of periodontitis, was higher in the group with severe periodontitis. This indicates that the current status of periodontitis can be diagnosed through sRAGE.