Efficacy and tolerability of initial triple combination therapy with metformin, dapagliflozin and saxagliptin compared with stepwise add-on therapy in drug-naïve patients with type 2 diabetes (TRIPLE-AXEL study): A multicentre, randomized, 104-week, open-label, active-controlled trial.

AIM: To evaluate the efficacy and tolerability of an initial triple combination therapy (TCT) compared with conventional stepwise add-on therapy (SAT) in patients with newly diagnosed type 2 diabetes (T2D). MATERIALS AND METHODS: This multicentre, randomized, 104-week, open-label trial randomized 105 patients with drug-naïve T2D (with HbA1c level ≥ 8.0%, < 11.0%) to the TCT (1000 mg of metformin, 10 mg of dapagliflozin and 5 mg of saxagliptin once daily) or SAT (initiated with metformin, followed by glimepiride and sitagliptin) groups. The primary outcome was the proportion of patients who achieved an HbA1c level of less than 6.5% without hypoglycaemia, weight gain of 5% or higher, or discontinuation of drugs because of adverse events at week 104. RESULTS: HbA1c reduction from baseline at week 104 was similar between the groups (the least squares mean change was -2.56% in the TCT group vs. -2.75% in the SAT group). The primary outcome was achieved in 39.0% and 17.1% of the TCT and SAT groups, respectively, with a risk difference of 22.0 (95% confidence interval 3.0, 40.8; P = .027). HbA1c level less than 6.5% at week 104 was 46.3% in both the TCT and SAT groups, whereas the incidence of hypoglycaemia, weight gain, or discontinuation of drugs was 16.7% and 62.0% in the TCT and SAT groups, respectively (P < .001). TCT was well-tolerated and had fewer adverse events than SAT. CONCLUSIONS: Among newly diagnosed patients with T2D, initial TCT effectively lowered HbA1c levels with higher tolerability and safety than SAT for 104 weeks, suggesting a novel strategy for initial combination therapy in T2D patients.

Reference Standards for C-Peptide in Korean Population: A Korean Endocrine Hormone Reference Standard Data Center Study.

Background: The Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population. Methods: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (≥100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs. Results: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34-3.18, 4.74±3.57 and 1.14-8.33, and 4.85±3.58 and 1.25-8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels. Conclusion: The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields.

Nondestructive Single-Atom-Thick Crystallographic Scanner via Sticky-Note-Like van der Waals Assembling-Disassembling.

Crystallographic characteristics, including grain boundaries and crystallographic orientation of each grain, are crucial in defining the properties of two-dimensional materials (2DMs). To date, local microstructure analysis of 2DMs, which requires destructive and complex processes, is primarily used to identify unknown 2DM specimens, hindering the subsequent use of characterized samples. Here, a nondestructive large-area 2D crystallographic analytical method through sticky-note-like van der Waals (vdW) assembling-disassembling is presented. By the vdW assembling of veiled polycrystalline graphene (PCG) with a single-atom-thick single-crystalline graphene filter (SCG-filter), detailed crystallographic information of each grain in PCGs is visualized through a 2D Raman signal scan, which relies on the interlayer twist angle. The scanned PCGs are seamlessly separated from the SCG-filter using vdW disassembling, preserving their original condition. The remaining SCG-filter is then reused for additional crystallographic scans of other PCGs. It is believed that the methods can pave the way for advances in the crystallographic analysis of single-atom-thick materials, offering huge implications for the applications of 2DMs.

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis.

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.

Safety and Clinical Outcomes of Insulin Degludec in Korean Patients with Diabetes in Real-World Practices: A Prospective, Observational Study.

INTRODUCTION: To investigate the safety and effectiveness of insulin degludec (IDeg) in a real-world population of Korean patients with diabetes requiring insulin therapy. METHODS: This was a multicenter, prospective, single-arm, open-label, non-interventional study. Patients aged ≥ 12 months and treated with previous glucose-lowering medications were eligible to switch to IDeg. The primary endpoint was the incidence of adverse events (AEs), and the secondary endpoints were changes in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), postprandial glucose (PPG), and target HbA1c < 7.0%. RESULTS: In total, 3225 and 2450 patients were included in the safety analysis set (SAS) and effectiveness analysis set (EAS), respectively. The mean baseline HbA1c and duration of diabetes were 9.4% and 13.0 years, respectively. Adverse events were reported in 740 patients (22.9%); the majority were mild and resolved. Significant improvements were observed in HbA1c, FPG, and PPG at week 26 (all p < 0.0001). The target of HbA1c < 7% was achieved in 22.2% of patients at week 26. CONCLUSION: In real-world clinical practice, 26 weeks of IDeg treatment resulted in significant reductions in glycemic parameters with a low incidence of AEs in Korean patients with diabetes. No new safety signals were observed. CLINICAL TRIALS REGISTRY AND REGISTRATION NUMBER: This trial is registered under ClinicalTrials.gov (NCT02779413) and the universal trial number is [U1111-1176-2287].

Effects of dapagliflozin compared with glimepiride on body composition in Asian patients with type 2 diabetes inadequately controlled with metformin: The BEYOND study.

AIMS: To evaluate the effect of dapagliflozin on body composition such as total body fat (BF) mass, abdominal visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) areas compared with glimepiride in Korean patients with type 2 diabetes. MATERIALS AND METHODS: This was a 52-week, multicentre, randomized, parallel-group, open-label, Phase IV (NCT02564926) study. Patients with inadequate glycaemic control (glycated haemoglobin ≥7.0% and <10.0%) on metformin monotherapy (≥1000 mg/day) were randomized 1:1 to receive dapagliflozin 10 mg/day or glimepiride 1-2 mg/day for 12 months as an add-on to metformin. Baseline and end of study body composition evaluations included dual-energy X-ray absorptiometry and abdominal computed tomography scans. RESULTS: Of 124 enrolled patients from 14 centres, 121 received study treatment (dapagliflozin: 60; glimepiride: 61) and 106 (85.5%) completed the study. Over 52 weeks, the dapagliflozin group showed the following differences versus the glimepiride group: -2.59 kg BF mass, -1.94% BF%, -17.55 cm2 VAT area, -18.39 cm2 SAT area, -0.46% glycated haemoglobin, -18.25 mg/dl fasting blood glucose, -3.7 kg weight, -2.21 cm waist circumference, -1.37 kg/m2 body mass index, -6.81 mmHg systolic blood pressure and +657.71 ng/ml in adiponectin; all were statistically significant. Both groups had similar incidences of adverse events; however, hypoglycaemic events were mainly (12 of 15) reported in the glimepiride group. CONCLUSION: Dapagliflozin reduced total BF mass, abdominal VAT and SAT areas, and showed better glycaemic control than glimepiride. Being safe and well-tolerated, dapagliflozin appears to be a more favourable alternative to sulphonylureas as add-on therapy after metformin monotherapy failure in Korean patients with type 2 diabetes.

Comparison on genomic prediction using pedigree BLUP and single step GBLUP through the Hanwoo full-sib family.

OBJECTIVE: When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method. METHODS: The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations. RESULTS: For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual. CONCLUSION: Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

Cell-free fetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study.

BACKGROUND: One in four pregnancies end in a pregnancy loss. Although the effect on couples is well documented, evidence-based treatments and prediction models are absent. Fetal aneuploidy is associated with a higher chance of a next successful pregnancy compared with euploid pregnancy loss in which underlying maternal conditions might be causal. Ploidy diagnostics are therefore advantageous but challenging as they require collection of the pregnancy tissue. Cell-free fetal DNA (cffDNA) from maternal blood has the potential for evaluation of fetal ploidy status, but no large-scale validation of the method has been done. METHODS: In this prospective cohort study, women with a pregnancy loss were recruited as a part of the Copenhagen Pregnancy Loss (COPL) study from three gynaecological clinics at public hospitals in Denmark. Women were eligible for inclusion if older than 18 years with a pregnancy loss before gestational age 22 weeks (ie, 154 days) and with an intrauterine pregnancy confirmed by ultrasound (including anembryonic sac), and women with pregnancies of unknown location or molar pregnancies were excluded. Maternal blood was collected while pregnancy tissue was still in situ or within 24 h after pregnancy tissue had passed and was analysed by genome-wide sequencing of cffDNA. Direct sequencing of the pregnancy tissue was done as reference. FINDINGS: We included 1000 consecutive women, at the time of a pregnancy loss diagnosis, between Nov 12, 2020, and May 1, 2022. Results from the first 333 women with a pregnancy loss (recruited between Nov 12, 2020, and Aug 14, 2021) were used to evaluate the validity of cffDNA-based testing. Results from the other 667 women were included to evaluate cffDNA performance and result distribution in a larger cohort of 1000 women in total. Gestational age of fetus ranged from 35-149 days (mean of 70·5 days [SD 16·5], or 10 weeks plus 1 day). The cffDNA-based test had a sensitivity for aneuploidy detection of 85% (95% CI 79-90) and a specificity of 93% (95% CI 88-96) compared with direct sequencing of the pregnancy tissue. Among 1000 cffDNA-based test results, 446 (45%) were euploid, 405 (41%) aneuploid, 37 (4%) had multiple aneuploidies, and 112 (11%) were inconclusive. 105 (32%) of 333 women either did not manage to collect the pregnancy tissue or collected a sample classified as unknown tissue giving a high risk of being maternal. INTERPRETATION: This validation of cffDNA-based testing in pregnancy loss shows the potential and feasibility of the method to distinguish euploid and aneuploid pregnancy loss for improved clinical management and benefit of future reproductive medicine and women's health research. FUNDING: Ole Kirks Foundation, BioInnovation Institute Foundation, and the Novo Nordisk Foundation.

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study.

BACKGROUND: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. OBJECTIVE: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. METHODS: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. RESULTS: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; ß=1.6; P=.01). CONCLUSIONS: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.

On-site Ultrasound Screens out Asymptomatic Knee Lesions in Elite Adolescent Male Basketball Players.

Basketball is a popular sport worldwide with a high injury risk. In this study, we conducted survey composed of clinical symptom reporting scale, physical examination and meticulous portable musculoskeletal ultrasound to 19 elite male high school basketball players and 15 regular male high school students. Our study showed the incidence of ultrasonographic findings of any lesion, suprapatellar effusion and proximal patellar tendinopathy is significantly higher in player group, and the incidence of asymptomatic ultrasonographic lesion is also higher in player group. Screening for asymptomatic lesions bares clinical relevance and plays a role in prevention of symptom development. With the concise and easy-to-perform ultrasonography protocol we performed and being interpreted by sports team physician, the protocol can offer precise diagnosis of common injury and screening for asymptomatic lesion potentially progressive.

Analysis of clinical phenotypes of neuropathic symptoms in patients with type 2 diabetes: A multicenter study.

AIMS/INTRODUCTION: We investigated the classification of diabetic peripheral neuropathy (DPN) patients by subjective symptoms, and identification of the relationship between the patterns and intensities of symptoms and the clustered groups of DPN patients. MATERIALS AND METHODS: This multicenter study analyzed epidemiological data and sensory symptoms of 649 patients with DPN. Cluster analysis was carried out to identify subgroups of patients with characteristic symptom profiles. Factor analysis was carried out to investigate the symptom patterns of the clustered groups of DPN patients. RESULTS: Three clusters of patients with DPN were identified: severe symptoms with decreased quality of life (cluster 1, n = 119, 18.3%), predominantly insensate symptoms with relatively good quality of life (cluster 2, n = 318, 49.0%), and moderate pain intensity and decreased quality of life (cluster 3, n = 204, 31.4%). The frequency of symptoms on each item of the Michigan Neuropathy Screening Instrument questionnaire showed a similar distribution according to pain intensities along with the three clusters. CONCLUSIONS: Our study supports the hypothesis that diversity in sensory symptoms exists in patients with DPN. Heterogeneity in DPN patients should be taken into account for a more stratified or individualized treatment approach. Based on a multicenter study, we identified three clusters of patients with DPN. Our research supports the hypothesis that diversity in sensory symptoms exists in patients with DPN. Heterogeneity in DPN patients should be taken into account for a more stratified or individualized treatment approach.

Facing the loss of siblings in childhood: Interactions and dynamics between bereaved siblings and their parents.

INTRODUCTION: The loss of a child in a family is a painful experience. Despite this, most studies focus on the grieving experience of parents. Our understanding of sibling bereavement therefore remains underexplored. This study aims to address this gap by exploring the bereavement experience of younger bereaved siblings from the perspective of the bereaved child and the parents. METHOD: This study is a qualitative study conducted in Hong Kong. We recruited eleven bereaved families from 2019 to 2020. Semi-structured interviews were conducted separately with bereaved children and parents. Thematic analyses were performed. FINDINGS: Eleven bereaved siblings, aged 5 to 11, and their parents were interviewed. Four themes were generated: Parents' unawareness of bereaved siblings' grief, protecting parents from grief, comparison with the deceased siblings, and learning how to grieve. CONCLUSION: Our findings suggest that the family dynamics and interactions in the grieving experience are complex. The impact of grief on the bereaved child could emanate from the loss of the sibling or from the grief of the parents or from family interactions. PRACTICE IMPLICATIONS: This study highlights the importance of viewing the sibling bereavement experience in a family context and of enhancing sibling bereavement services. Helping professionals should not only provide more bereavement support to the bereaved siblings individually, but they should also enhance parents' capacity to support their bereaved children.

Weight Change Alters the Small RNA Profile of Urinary Extracellular Vesicles in Obesity.

INTRODUCTION: Various kidney diseases reportedly show different urinary extracellular vesicle (EV) RNA profiles. Although obesity is one of the main causes of chronic kidney disease, the expression pattern of urinary EV RNA in obesity is uncertain. Our aim was to sequence the small RNA profiles of urinary EVs in obese patients before and after weight reduction and compare them to those of healthy volunteers (HVs). METHODS: We recruited age-sex-matched obese patients and HVs. The small RNA profiles of urinary EVs were analyzed using RNA sequencing. To evaluate the effect of weight reduction, small RNA profiles of urinary EVs 6 months after bariatric surgery were also analyzed. RESULTS: The proportion of urinary EVs transfer RNA and microRNA of obese patients differed from that of HVs. Obese patients showed differential expression of 1,343 small RNAs in urinary EVs compared to HVs (fold change ≥2 and p value <0.05). Among those, 61 small RNAs were upregulated in obese patients and downregulated after weight reduction, whereas 167 small RNAs were downregulated in obese patients and upregulated after weight reduction. RNA sequencing revealed the correlation between the specific urinary EV small RNAs and clinical parameters including body weight, low-density lipoprotein cholesterol, triglyceride, high-density lipoprotein cholesterol, serum glucose, estimated glomerular filtration rate, and albuminuria. CONCLUSION: Obese patients showed distinct urinary EV small RNA profiles compared to HVs. Weight reduction altered urinary EV small-RNA profiles in obese patients.

High-Resolution Ultrasound-Assisted Assessment of Preliminary Short-term Safety Outcomes of an Implant-Based Augmentation Mammaplasty Using a Bioengineered, Cell-Friendly, Smooth-Surface Device in Korean Females.

BACKGROUND: The Motiva Ergonomix Round SilkSurface (Establishment Labs Holdings Inc., Alajuela, Costa Rica) is one of the representative brands of the fifth generation of a silicone gel-filled breast implant with a microtextured surface. OBJECTIVES: In this study, the authors describe preliminary short-term safety outcomes of an implant-based augmentation mammaplasty using the Motiva Ergonomix Round SilkSurface in Korean females. METHODS: The authors performed a retrospective analysis of medical records in a total of 69 females (n = 69) receiving an implant-based augmentation mammaplasty using the Motiva Ergonomix Round SilkSurface between September 26, 2017, and December 31, 2020. The authors analyzed incidences of postoperative complications. RESULTS: A total of 6 cases (8.7%) of postoperative complications occurred; these include 2 cases (2.9%) of early seroma, 1 case (1.4%) of capsular contracture, 2 cases (2.9%) of alterations in the shape, and 1 case (1.4%) of foreign body sensation. Time-to-events were estimated at 266.81 ± 273.17 days. CONCLUSIONS: The authors describe our preliminary short-term safety outcomes of an implant-based augmentation mammaplasty using the Motiva Ergonomix Round SilkSurface in Korean females. But this deserves further large-scale studies with long periods of follow-up.

Hypochlorous Acid Versus Saline Nasal Irrigation in Allergic Rhinitis: A Multicenter, Randomized, Double-Blind, Placebo-controlled Study.

BACKGROUND: Low concentrations of hypochlorous acid (HOCl) have proven antipruritic, anti-inflammatory, and antimicrobial effects without toxicity, although the mechanism has not been fully elucidated. OBJECTIVE: The aim of this study was to evaluate the effectiveness of HOCl nasal irrigation to reduce allergic rhinitis (AR) symptoms compared with saline nasal irrigation. METHODS: This was multicenter, randomized, double-blind, placebo-controlled study. Initially, 139 patients with perennial AR were enrolled; however, 25 did not successfully complete the study. Patients were randomly assigned to the nasal irrigation with low-concentration HOCl (n = 55) or normal saline (n = 59) treatment groups for the 4-week study period. Participants completed the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) at every visit (baseline, Weeks 2 and 4), and Total Nasal Symptom Score (TNSS) was determined before and after nasal irrigation every morning and evening. RESULTS: We found that RQLQ scores significantly decreased after 4 weeks in the HOCl and placebo groups, but the decrement of the RQLQ score was similar between the 2 groups. Additionally, TNSS improved in both groups between baseline and Week 4, whereas there were no significant differences in the change of TNSS between the 2 groups. The HOCl group did not show any clinical side effects related to nasal irrigation. CONCLUSION: Allergic symptoms significantly decreased with low-concentration HOCl nasal irrigation, without significant adverse events. However, HOCl showed no additional improvement in symptoms compared with saline nasal irrigation for patients with perennial AR.

Estimation of breeding value and accuracy using pedigree and genotype of Hanwoo cows (Korean cattle).

The genetic improvement of Hanwoo is dependent on the estimated breeding value (EBV) of pedigree-based Korean proven bull's number, and the genetic evaluation for cows is difficult due to insufficient pedigree and test records. Genomic selection involves utilizing the individual's genotype to estimate the breeding value (BV) and is determined to be an appropriate evaluation method for cows who lack test information. This study used pedigree and genotype to estimate and analyse BV and accuracy of Hanwoo cows in the Gyeongnam area using pedigree best linear unbiased prediction (PBLUP) and genomic best linear unbiased prediction (GBLUP). The test group acquired pedigree and genotype of 919 Hanwoo cows in the Gyeongnam area. The traits used for analysis were carcass weight (CWT), eye muscle areas (EMA), backfat thickness (BFT) and marbling score (MS). PBLUP used Reference group 1 containing the pedigree and phenotype of 919 Hanwoo cows and 545,483 heads to construct the numeric relationship matrix and estimated the EBV and accuracy. GBLUP used Reference group 2 containing the genotype and phenotype of 919 Hanwoo cows and 17,226 heads to construct the genomic relationship matrix and estimated the genomic EBV (GEBV) and accuracy. In the order of CWT, EMA, BFT and MS, the accuracy of PBLUP was 0.488, 0.480, 0.482 and 0.486 while the accuracy of GBLUP was higher with 0.779, 0.758, 0.766 and 0.791. And for 104 cows without relationship coefficient on pedigree to the reference group, the accuracy as PBLUP was estimated to be 0, but for GBLUP, it was possible to estimate the accuracy for all individuals. If GBLUP is applied to cows raised in general farms, the genetic evaluation can be performed even on animals without pedigree and high-accuracy estimation, enabling selection of excellent cows. Accordingly, by securing the genetic diversity of cows, it is expected to increase the profitability of farms by decreasing the inbreeding rate and increasing efficiency of elite calf production.

Emotional Distress of the COVID-19 Cluster Infection on Health Care Workers Working at a National Hospital in Korea.

BACKGROUND: Frontline healthcare workers responding to coronavirus disease 2019 (COVID-19) inevitably face tremendous psychological burden. Thus, the present study aimed to identify the psychological impact and the factors contributing to the likely increase in emotional distress of healthcare workers. METHODS: The participants include a total of 99 healthcare workers at Bugok National Hospital. Psychometric scales were used to assess emotional distress (12-item General Health Questionnaire; GHQ-12), depression symptoms (Patient Health Questionnaire-9; PHQ-9), and post-traumatic stress disorder-related symptoms (Impact of Events Scale-Revised; IES-R). A supplementary questionnaire was administered to investigate the experience of healthcare workers exposed to COVID-19-infected patients. Based on the results of GHQ-12 survey, participants were categorized into two groups: distress and non-distress. All the assessed scores were compared between the two groups. A logistic regression model was constructed to identify factors associated with emotional distress. RESULTS: Emotional distress was reported by 45.3% (n = 45) of all participants. The emotionally distressed group was more likely to be female, manage close contacts, have higher scores on PHQ-9 and IES-R, feel increased professional risk, and report that proper infection control training was not provided. Female gender, managing close contacts, higher scores on PHQ-9, and a feeling that proper infection control training was not provided were associated with emotional distress in logistic regression. CONCLUSION: Frontline healthcare workers face tremendous psychological burden during the COVID-19 pandemic. Therefore, appropriate psychological interventions should be provided to the HCWs engaged in the management of COVID-19-infected patients.

Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea.

BACKGROUND: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. METHODS: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing's syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. RESULTS: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6ß-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. CONCLUSION: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.

Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography.

DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use. PURPOSE: Osteoporosis is defined as a systemic disease of the bone characterized by a decrease in bone strength and deterioration of bone structure at the microscopic level, leading to bone fragility and increased risk of fracture. Bone mineral density (BMD) is the preferred method for the diagnosis of osteoporosis, and dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Conventional radiography is more suited for the screening of osteoporosis rather than diagnosis, and osteoporosis can be detected on radiographs by experienced physicians only. This study explored the possibility of predicting BMD relative to DXA using patient radiographs. METHODS: A deep learning algorithm of convolutional neural network (CNN) was used for the purpose. The method includes image segmentation, CNN learning, and a convolution-based regression model (DeepDXA) that links the isolated images of the femur bone to predict BMD value. Data were obtained in a single medical center from 2006 to 2018, with a total amount of 3472 pairs of pelvis X-ray and DXA examination within 1 year. RESULTS: The proposed workflow successfully predicted BMD values of the femur bone with the correlation coefficient (R) of 0.85 (P < 0.001) and the accuracy of 0.88 for prediction osteoporosis, a finding that could be reliably ready for further clinical use. CONCLUSION: When suspicious osteoporosis is seen on plain films using the deep learning method we developed, further referral to DXA for the definite diagnosis of osteoporosis is indicated.