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OBJECTIVE: Bipolar and depressive disorders are distinct disorders with clearly different clinical courses, however, distinguishing between them often presents clinical challenges. This study investigates the utility of self-report questionnaires, the Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS), with machine learning-based multivariate analysis, to classify patients with bipolar and depressive disorders. METHODS: A total of 189 patients with bipolar disorders and depressive disorders were included in the study, and all participants completed both the MDQ and BSDS questionnaires. Machine-learning classifiers, including support vector machine (SVM) and linear discriminant analysis (LDA), were exploited for multivariate analysis. Classification performance was assessed through cross-validation. RESULTS: Both MDQ and BSDS demonstrated significant differences in each item and total scores between the two groups. Machine learning-based multivariate analysis, including SVM, achieved excellent discrimination levels with area under the ROC curve (AUC) values exceeding 0.8 for each questionnaire individually. In particular, the combination of MDQ and BSDS further improved classification performance, yielding an AUC of 0.8762. CONCLUSION: This study suggests the application of machine learning to MDQ and BSDS can assist in distinguishing between bipolar and depressive disorders. The potential of combining high-dimensional psychiatric data with machine learning-based multivariate analysis as an effective approach to psychiatric disorders.
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This study investigated differential gene expression between granulation patterns in growth hormone (GH)-secreting pituitary tumors, aiming to elucidate novel transcriptomes that explain clinical variances in patients with acromegaly. Transcriptome analysis was conducted on 6 normal pituitary tissues and 15 GH-secreting pituitary tumors, including 9 densely granulated somatotroph tumors (DGSTs) and 6 sparsely granulated somatotroph tumors (SGSTs). We identified 3111 differentially expressed genes (DEGs) in tumors compared to normal pituitaries, with 1117 DEGs unique to a specific granulation within tumors. SGST showed enrichment of neuronal development and acute inflammatory response pathways, along with a significant enhancement of JAK-STAT, phosphatidylinositol 3-kinase, and MAPK signaling. The results suggest that granulation-specific gene expression may underpin diverse clinical presentations in acromegaly, highlighting the potential for further investigation into these transcriptomic variations and their roles in disease pathology, particularly the involvement of genes linked to neuronal development, inflammatory response, and JAK-STAT signaling in SGST.
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Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model's performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier. The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively. The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion: The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.
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Schizophrenia (SPR) is the most devastating mental illness that causes severe deterioration in social and occupational functioning, but, the etiology remains unknown. The objective of this study is to explore the genetic underpinnings of novelty seeking behavior in schizophrenic family within the Korean population. By conducting a family-based genome-wide association study, we aim to identify potential genetic markers and variations associated with novelty seeking traits in the context of SPR. We have recruited 27 probands (with SPR) with their parents and siblings whenever possible. DNA was extracted from blood sampling of 58 individuals in 27 families and analyzed in an Illumina core exome single nucleotide polymorphism (SNP) array. A family-based association test (qFAM) was used to derive SNP association values across all chromosomes. Although none of the final 800,000 SNPs reached the genome-wide significant threshold of 8.45â ×â 10-7, the most significant 4 SNPs were within the 10-5 to 10-7. This study identifies genetic associations between novelty seeking behavior and SPR within families. RAPGEF5 emerges as a significant gene, along with other neuropsychiatric-related genes. Noteworthy genes like DRD4 and COMT did not show associations, possibly due to the focus on schizophrenic family. While shedding light on this complex relationship, larger studies are needed for robust conclusions and deeper mechanistic insights.
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Comportamento Exploratório , Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Esquizofrenia , Humanos , Esquizofrenia/genética , Masculino , Feminino , República da Coreia/epidemiologia , Projetos Piloto , Adulto , Pessoa de Meia-Idade , Predisposição Genética para Doença , Adulto JovemRESUMO
OBJECTIVE: Due to the high frequency of depressive symptoms associated with breast cancer, it is crucial to screen for depression in breast cancer patients. While numerous screening tools are available for depression in this population, there is a need for a brief and convenient tool to enhance clinical use. This study aims to investigate the psychometric properties of the Patient Health Questionnaire-9 (PHQ-9) in patients with breast cancer. METHODS: Patients with breast cancer (n=327) who visited the Breast Cancer Clinic were included in this study. The reliability of the PHQ-9 was analyzed by Cronbach's α, and the construct validity of the PHQ-9 was explored by factor analysis. The concurrent validity of the PHQ-9 was evaluated by Pearson correlation analysis with the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). RESULTS: The values of Cronbach's α ranged from 0.800 to 0.879 was acceptable. The exploratory factor analysis revealed that the one-factor model and two-factor model of the PHQ-9 explained 46% and 57% of the variance, respectively. The PHQ-9 were significantly correlated with those of HADS (r=0.702, p<0.001) and PSS (r=0.466, p<0.001). Consequently, the PHQ-9 demonstrated acceptable reliability and validity in breast cancer patients. CONCLUSION: The findings of this study indicate that the PHQ-9 exhibits acceptable reliability and validity in patients with breast cancer. The convenience of this brief self-report questionnaire suggests its potential as a reliable and valid tool for assessing depression in breast cancer clinics.
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OBJECTIVE: Mental health promotion programs using virtual reality (VR) technology have been developed in various forms. This study aimed to investigate the subjective experience of a VR-assisted mental health promotion program for the community population, which was provided in the form of VR experience on a bus to increase accessibility. METHODS: Ninety-six people participated in this study. The relationship between the subjective experience and mental health states such as depression, anxiety, perceived stress, and quality of life was explored. The subjective experience on depression and stress before and after VR program treatment was compared using the Wilcoxon signed-rank test. The satisfaction with the VR-assisted mental health promotion program was examined after using the VR program. RESULTS: The VR-assisted mental health promotion program on a bus significantly improved subjective symptoms such as depression (p=0.036) and perceived stress (p=0.010) among all the participants. Among the high-risk group, this VR program significantly relieved subjective depressive feeling score (p=0.033), and subjective stressful feeling score (p=0.035). In contrast, there were no significant changes in subjective depressive feelings (p=0.182) and subjective stressful feelings (p=0.058) among the healthy group. Seventy-two percent of the participants reported a high level of satisfaction, scoring 80 points or more. CONCLUSION: The findings of this study suggest that the VR-assisted mental health promotion program may effectively improve the subjective depressive and stressful feelings. The use of VR programs on buses to increase of accessibility for the community could be a useful approach for promoting mental health among the population.
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B lymphocytes play a pivotal role in the adaptive immune system by facilitating antibody production. Young B cell progenitors originate in the bone marrow and migrate to the spleen for antigen-dependent maturation, leading to the development of diverse B cell subtypes. Thus, tracking B cell trajectories through cell type distinction is essential for an appropriate checkpoint assessment. Despite its significance, monitoring specific B cell subclasses in live states has been hindered by a lack of suitable molecular tools. In this study, we introduce CDoB as the first mature B cell-selective probe, enabling real-time discrimination of three classified stages in B-cell development: progenitor, transitional, and mature B cells, through a single analysis using CyTOF. The selective mechanism of CDoB, elucidated as gating-oriented live-cell distinction (GOLD), targets SLC25A16, identified through systematic screening of SLC-CRISPRa and CRISPRi libraries. CDoB selectively brightens mature B cells in the mitochondrial area using SLC25A16 as the main gate, and the staining intensity correlates positively with the expression level of SLC25A16 along the B cell maturation continuum. In spleen tissues, CDoB demonstrates selective marking in mature B cell areas in live tissue status, representing the first performance achieved by a small-molecule fluorescent probe.
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Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders. In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion: Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.
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BACKGROUND: Korea is known for its technological prowess, has the highest smartphone ownership rate in the world at 95%, and the smallest gap in smartphone ownership between generations. Since the onset of the COVID-19 pandemic, problematic smartphone use is becoming more prevalent among Korean children and adolescent owing to limited school attendance and outdoor activities, resulting in increased reliance on smartphones. 40.1% of adolescents are classified as high-risk, with only the adolescent group showing a persistent rise year after year. OBJECTIVE: The study purpose is to present data-driven analysis results for predicting and preventing smartphone addiction in Korea, where problematic smartphone use is severe. PARTICIPANTS AND METHODS: To predict the risk of problematic smartphone use in Korean children and adolescents at an early stage, we used data collected from the Smartphone Overdependence Survey conducted by the National Information Society Agency between 2017 and 2021. Eight representative machine and deep learning algorithms were used to predict groups at high risk for smartphone addiction: Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), extreme Gradient Boosting (XGBoost), Light GBM, Categorical Boosting, Multilayer Perceptron, and Convolutional Neural Network. RESULTS: The XGBoost ensemble algorithm predicted 87.60% of participants at risk of future problematic smartphone usebased on precision. Our results showed that prolonged use of games, webtoons/web novels, and e-books, which have not been found in previous studies, further increased the risk of problematic smartphone use. CONCLUSIONS: Artificial intelligence algorithms have potential predictive and explanatory capabilities for identifying early signs of problematic smartphone use in adolescents and young children. We recommend that a variety of healthy, beneficial, and face-to-face activities be offered as alternatives to smartphones for leisure and play culture.
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Inteligência Artificial , Pandemias , Criança , Humanos , Adolescente , Pré-Escolar , Smartphone , Aprendizado de Máquina , República da Coreia/epidemiologiaRESUMO
Objective: : As dopamine is closely linked to locomotor activities, animal studies on locomotor activities using dopaminergic agents were widely done. However, most of animal studies were performed for a short period that there is a lack of longitudinal study on the effects of dopaminergic agents on locomotor activities. This study aimed to examine the longterm effect of a dopamine D2, D3 agonist quinpirole on locomotor activities in mice using a home-cage monitoring system. Methods: : The locomotor activities of Institute Cancer Research mice were measured by infrared motion detectors in home-cages under the 12-hour dark and 12-hour light condition for three days after the quinpirole injection. Quinpirole was injected at a concentration of 0.5 mg/kg intraperitoneally in the beginning of the dark phase. The locomotor activities before and after the quinpirole administration were compared by the Wilcoxon signed-rank test and one-way repeated measures ANOVA. Results: : After the quinpirole administration, the 24-hour total locomotor activity did not change (p = 0.169), but activities were significantly increased in the 12-hour dark phase sum (p = 0.013) and decreased in the 12-hour light phase sum (p = 0.009). Significant increases in the activities were observed in the dark-light difference (p = 0.005) and dark-light ratio (p = 0.005) as well. Conclusion: : This study suggests that quinpirole injection entrains the circadian rest-activity rhythm of locomotor activities. Therefore, quinpirole can be a drug that mediates locomotor activity as a dopamine agonist as well as a modulator of the circadian rhythms.
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The clinical characteristics and prognoses of acromegaly vary among patients. Assessment of current and novel predictors can lead to multilevel categorization of patients, allowing integration into new clinical guidelines and a reduction in the increased morbidity and mortality associated with acromegaly. Despite advances in the diagnosis and treatment of acromegaly, its pathophysiology remains unclear. Recent advancements in multiomics technologies, including genomics, transcriptomics, proteomics, metabolomics, and radiomics, have offered new opportunities to unravel the complex pathophysiology of acromegaly. This review comprehensively explores the emerging role of multiomics approaches in elucidating the molecular landscape of acromegaly. We discuss the potential implications of multiomics data integration in the development of novel diagnostic tools, identification of therapeutic targets, and the prospects of precision medicine in acromegaly management. By integrating diverse omics datasets, these approaches can provide valuable insights into disease mechanisms, facilitate the identification of diagnostic biomarkers, and identify potential therapeutic targets for precision medicine in the management of acromegaly.
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Acromegalia , Humanos , Acromegalia/diagnóstico , Acromegalia/genética , Acromegalia/terapia , Multiômica , Medicina de Precisão , Proteômica , GenômicaRESUMO
BACKGROUND: 18Fluorine-Fluoro-deoxy-glucose (18F-FDG) positron emission tomography (PET) is widely used for diagnosing various malignant tumors and evaluating metabolic activities. Although the usefulness of 18F-FDG PET has been reported in several endocrine diseases, studies on pituitary disease are extremely limited. To evaluate whether dexamethasone (DEX) suppression can improve 18F-FDG PET for the localization of adrenocorticotropic hormone-secreting adenomas in the pituitary gland in Cushing's disease (CD). METHODS: We included 22 patients with CD who underwent PET imaging before and after DEX administration. We compared the success rates of PET before and after DEX suppression, magnetic resonance imaging (MRI), and bilateral inferior petrosal sinus sampling (BIPSS). We determined the final locations of adenomas based on intraoperative multiple-staged resection and tumor tissue identification using frozen sections. Standardized uptake value (SUV) were analyzed to confirm the change of intensity of adenomas on PET. RESULTS: Twenty-two patients were included (age at diagnosis: 37 [13-56] years), and most were women (90.91%). Pituitary adenomas compared to normal pituitaries showed increased maximum SUV after DEX suppression but without statistical significance (1.13 versus. 1.21, z=-0.765, P = 0.444). After DEX suppression, the mean and maximum SUV of adenomas showed a positive correlation with nadir cortisol levels in high-dose DEX suppression test (Rho = 0.554, P = 0.007 and Rho = 0.503, P = 0.017, respectively). In reference sites, mean SUV of cerebellum was significantly decreased (7.65 vs. 6.40, P = 0.006*), but those of the thalamus and gray matter was increased after DEX suppression (thalamus, 8.70 vs. 11.20, P = 0.010*; gray matter, 6.25 vs. 7.95, P = 0.010*). CONCLUSION: DEX suppression did not improve 18F-FDG PET/CT localization in patients with CD.
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Neoplasias Hipofisárias , Humanos , Feminino , Adolescente , Adulto Jovem , Adulto , Pessoa de Meia-Idade , Masculino , Hormônio Adrenocorticotrópico , Fluordesoxiglucose F18 , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Tomografia por Emissão de Pósitrons , DexametasonaRESUMO
BACKGROUND: Korea has the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries. Consequently, central and local governments and private organizations in Korea cooperate in promoting various suicide prevention projects to actively respond to suicide problems. Machine learning has been used to predict suicidal ideation in the fields of health and medicine but not from a social science perspective. OBJECTIVE: Since suicidal ideation is a major predictor of suicide attempts, being able to anticipate and mitigate it helps prevent suicide. Therefore, this study presents a data-based analysis method for predicting suicidal thoughts quickly and effectively and suggests countermeasures against the causes of suicidal thoughts. PARTICIPANTS AND METHODS: To predict early signs of suicidal ideation in children and adolescents, big data collected for approximately 4 years (from 2017 to 2020) from the Korea Youth Policy Institute (NYPI) were used. To accurately predict suicidal ideation, supervised ma- chine learning classification algorithms such as logistic regression, random forest, XGBoost, multilayer perceptron (MLP), and convolutional neural network (CNN) were used. RESULTS: Using CNN, suicidal ideation was predicted with an accuracy of approximately 90 %. The logistic regression results showed that sadness and depression increased suicidal thoughts by more than 25 times, and anxiety, loneliness, and experience of abusive language increased suicidal thoughts by more than three times. CONCLUSIONS: Machine learning and deep learning approaches have the potential to predict and respond to suicidal thoughts in children, adolescents, and the general population, as well as help respond to the suicide crisis by preemptively identifying the cause.
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Aprendizado Profundo , Ideação Suicida , Humanos , Adolescente , Criança , Causas de Morte , Tentativa de Suicídio/prevenção & controle , República da Coreia/epidemiologia , Fatores de RiscoRESUMO
BACKGROUND: Excessive visceral obesity in recipients of living donor liver transplantation (LDLT) is associated with mortality, and a recent study reported the correlation between visceral adiposity of male LDLT recipients and hepatocellular carcinoma (HCC) recurrence. However, there is no study on the relationship between the donor's visceral adiposity and surgical outcomes in LDLT recipients. We investigated the association of the visceral-to-subcutaneous fat area ratio (VSR) in donors and recipients with HCC recurrence and mortality in LDLT. METHODS: We analyzed 1386 sets of donors and recipients who underwent LDLT between January 2008 and January 2018. The maximal chi-square method was used to determine the optimal cutoff values for VSR for predicting overall HCC recurrence and mortality. Cox regression analyses were performed to evaluate the association of donor VSR and recipient VSR with overall HCC recurrence and mortality in recipients. RESULTS: The cutoff values of VSR was determined as 0.73 in males and 0.31 in females. High donor VSR was significantly associated with overall HCC recurrence (adjusted hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.06-1.93, p = 0.019) and mortality (HR: 1.35, 95% CI: 1.03-1.76, p = 0.030). High recipient VSR was significantly associated with overall HCC recurrence (HR: 1.40, 95% CI: 1.04-1.88, p = 0.027) and mortality (HR: 1.50, 95% CI: 1.14-1.96, p = 0.003). CONCLUSIONS: Both recipient VSR and donor VSR were significant risk factors for HCC recurrence and mortality in LDLT recipients. Preoperative donor VSR and recipient VSR may be strong predictors of the surgical outcomes of LDLT recipients with HCC.
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Carcinoma Hepatocelular , Neoplasias Hepáticas , Transplante de Fígado , Feminino , Masculino , Humanos , Carcinoma Hepatocelular/cirurgia , Carcinoma Hepatocelular/patologia , Transplante de Fígado/efeitos adversos , Transplante de Fígado/métodos , Neoplasias Hepáticas/cirurgia , Neoplasias Hepáticas/patologia , Doadores Vivos , Recidiva Local de Neoplasia/epidemiologia , Recidiva Local de Neoplasia/etiologia , Obesidade Abdominal/etiologia , Estudos Retrospectivos , Resultado do TratamentoRESUMO
OBJECTIVE: Face-to-face evaluation is the most important in psychiatric evaluation, but smart healthcare, including non-face-to-face evaluation, can be beneficial considering the situation in which face-to-face evaluation is limited or the preventive aspect of mental illness. In this paper, we aimed to check whether mental health screening tests have the same significance as paper-based tests even when collected through mobile applications. METHODS: A smart mental healthcare screening test was conducted on the 1,327 community subjects. We measured two indicators of depression (Patient Health Questionnaire 9-item scale, PHQ-9) and anxiety (Generalized Anxiety Disorder 7-item scale, GAD-7) to check mental health conditions. RESULTS: The average Cronbach's alpha value of the PHQ-9 questionnaire was good at 0.870. As a result of PHQ-9's principal component analysis, one component with an eigenvalue of 1 or more was identified, which is suitable to be described as a single factor. The average Cronbach's alpha value of the GAD-7 was 0.919. The structural validity of the GAD-7 was confirmed through principal component analysis. CONCLUSION: Our results show that PHQ-9 and GAD-7 scales performed through mobile applications can have the same meaning as paper-based tests. Surveys using a tablet PC, or smartphone application can monitor residents' mental health and accumulate data. Based on these data, smart mental health management can check the mental health of residents and treat mental illness in connection with medical services.
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OBJECTIVE: It is necessary to identify the mental health types of young women considering the importance of the mental health during the peripartum period. This study aimed to classify the mental health types in a community sample of young women with pre-pregnancy, pregnancy, or the postpartum period. METHODS: A total of 293 young women during pre-pregnancy, pregnancy, or the postpartum period were included in this study. The clinical characteristics of depression, anxiety, perceived stress, and quality of life were assessed. The clinical characteristics of the subject were classified by cluster analysis and compared by analysis of variance. RESULTS: From the cluster analysis, the subjects were classified into three groups. Cluster 1 showed significantly lower depression and anxiety and higher quality of life than those of cluster 2 and 3. Cluster 2 demonstrated significantly higher depression and anxiety and lower quality of life than those of cluster 3 and 1. Cluster 3 represented the intermediate levels between cluster 2 and 1. CONCLUSION: This study suggested that young women during pre-pregnancy, pregnancy, or the postpartum period might be in a good mental health group, a high-risk group requiring active monitoring, or a group in need of treatment. By monitoring mental health, the groups with high risk or requiring treatment could be discovered and proper management for prevention or improvement of mental health and quality of life can be provided.
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Objective: Even though studies using machine learning on sleep-wake states have been performed, studies in various conditions are still necessary. This study aimed to examine the performance of the prediction model of locomotor activities on sleep-wake states using machine learning algorithms. Methods: The processed data using moving average of locomotor activities were used as predicting features. The sleep-wake states were used as true labels. The prediction models were established by machine learning classifiers such as support vector machine with radial basis function (SVM-RBF), linear discriminant analysis (LDA), naïve Bayes, and random forest (RF). The prediction model was evaluated by a six-fold cross validation. Results: The SVM-RBF and RF showed acceptable performance within a window of moving average from 480 to 1,200 seconds. The highest accuracy (0.869) was shown by the RF at the interval of 480 seconds. Meanwhile, the highest area under the curve (0.939) was shown by LDA at the interval of 870 seconds. Conclusion: This study suggested that the prediction model on sleep-wake state using machine learning could show an improvement of the model performance when using moving average with raw data. The prediction model using locomotor activity can be useful in research on sleep-wake state.
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The DAMA/LIBRA collaboration has reported the observation of an annual modulation in the event rate that has been attributed to dark matter interactions over the last two decades. However, even though tremendous efforts to detect similar dark matter interactions were pursued, no definitive evidence has been observed to corroborate the DAMA/LIBRA signal. Many studies assuming various dark matter models have attempted to reconcile DAMA/LIBRA's modulation signals and null results from other experiments, however no clear conclusion can be drawn. Apart from the dark matter hypothesis, several studies have examined the possibility that the modulation is induced by variations in detector's environment or their specific analysis methods. In particular, a recent study presents a possible cause of the annual modulation from an analysis method adopted by the DAMA/LIBRA experiment in which the observed annual modulation could be reproduced by a slowly varying time-dependent background. Here, we study the COSINE-100 data using an analysis method similar to the one adopted by the DAMA/LIBRA experiment and observe a significant annual modulation, however the modulation phase is almost opposite to that of the DAMA/LIBRA data. Assuming the same background composition for COSINE-100 and DAMA/LIBRA, simulated experiments for the DAMA/LIBRA without dark matter signals also provide significant annual modulation with an amplitude similar to DAMA/LIBRA with opposite phase. Even though this observation does not directly explain the DAMA/LIBRA results directly, this interesting phenomenon motivates more profound studies of the time-dependent DAMA/LIBRA background data.
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BACKGROUND: A recent study reported a correlation between the muscle mass of male donors and graft failure in living donor liver transplantation (LDLT) recipients. We investigated the association of sex-specific donor skeletal muscle index (SMI) with mortality and graft failure in LDLT recipients. METHODS: We retrospectively analysed 2750 sets of donors and recipients between January 2008 and January 2018. The recipient outcomes were analysed by dividing the data according to donor sex. Cox regression analyses were performed to evaluate the association between donor SMI by sex and 1-year mortality and graft failure in recipients. RESULTS: In the male donor group, robust donor (increased SMI) was significantly associated with higher risks for mortality (hazard ratio [HR]: 1.03, 95% confidence interval [CI]: 1.00-1.06, p = .023) and graft failure (HR: 1.04, 95% CI: 1.01-1.06, p = .007) at 1 year. In the female donor group, the robust donor was significantly associated with lower risks for mortality (HR: 0.92, 95% CI: 0.87-0.97, p = .003) and graft failure (HR: 0.95, 95% CI: 0.90-1.00, p = .032) at 1 year. CONCLUSIONS: Donor SMI was associated with surgical outcomes in recipients. Robust male and female donors were a significant negative and protective factor for grafts respectively.
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Transplante de Fígado , Humanos , Masculino , Feminino , Doadores Vivos , Estudos Retrospectivos , Resultado do Tratamento , Músculo Esquelético , Sobrevivência de EnxertoRESUMO
PURPOSE: This study aimed to identify the perceptions, importance, and performance of midwives' roles among midwives and nurses in Korea. METHODS: A descriptive correlational design was employed. Data were collected from 164 nurses and 79 midwives from April 1 to June 25, 2021. Midwives enrolled in the Korean Midwifery Association and nurses and midwives from two hospitals each Daegu and Gyeonggi Province in Korea were invited to participate. The independent t-test, chi-square test, the Welch-Aspin test, and Pearson correlation coefficient were used for analysis. RESULTS: The midwives' role perception score (3.47±1.46) was lower than that of nurses (3.95±0.85), and the midwives' role performance score (2.98±0.83) was also lower than that of nurses (3.34±0.89). Significant differences were observed between midwives and nurses in their perception and performance of roles related to prenatal management, childbirth management, management of psychological changes, postpartum management, and newborn care. Higher role perception and performance among midwives were linked to the management of psychological changes and women's health, indicating potential areas for future development. CONCLUSION: The study results suggest directions for developing new roles for midwives. It is necessary to find a way to expand the field of midwives in public health by benchmarking the roles of midwives in various countries.