Honokiol-induced SIRT3 upregulation protects hippocampal neurons by suppressing inflammatory processes in pilocarpine-induced status epilepticus.

Status epilepticus (SE), a continuous and self-sustaining epileptic seizure lasting more than 30 min, is a neurological emergency that can cause severe brain injuries and increase the risk for the development of epilepsy. Over the past few decades, accumulating evidence has suggested the importance of brain inflammation in the pathogenesis of epilepsy. Honokiol (HNK), a pharmacological activator of sirtuin 3 (SIRT3), is a bioactive compound extracted from the bark or leaves of Magnolia plants that possesses therapeutic benefits for preventing the development of inflammatory injury. However, the therapeutic effects of HNK against epileptic brain injury via regulating molecular mechanisms related to neuroinflammation remains elusive. Therefore, the present study investigated the effects of HNK on pilocarpine-induced status epilepticus (PCSE) and the therapeutic benefits of HNK in regulating inflammatory processes in the hippocampus. Treatment with HNK before PCSE induction attenuated the initiation of behavioral seizures. Post-treatment with HNK after SE onset increased SIRT3 expression, which mitigated glial activation, including reactive astrocytes and activated microglia, in the hippocampus following PCSE. Moreover, HNK treatment reduced the activation of the nuclear factor-κB/nucleotide-binding domain leucine-rich repeat with a pyrin-domain containing 3 inflammasome pathway, thereby inhibiting the production of interleukin-1ß pro-inflammatory cytokine, subsequently alleviating PCSE-triggered apoptotic neuronal death in the hippocampus. These results indicate that HNK-induced SIRT3 upregulation has the potential to prevent the progression of epileptic neuropathology through its anti-inflammatory properties. Therefore, the present study suggests that HNK is a natural therapeutic agent for epileptic brain injury.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features.

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

Development and Validation of Machine Learning Models to Predict Postoperative Delirium Using Clinical Features and Polysomnography Variables.

Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions and patient outcomes. Methods: We studied 912 adults who underwent surgery under general anesthesia at a tertiary hospital (2013-2024) and had PSG within 5 years of surgery. Delirium was assessed via clinical diagnoses, antipsychotic prescriptions, and psychiatric consultations within 14 days postoperatively. Sleep-related data were collected using PSG and questionnaires. Machine learning predictions were performed to identify postoperative delirium, focusing on model accuracy and feature importance. Results: This study divided the 912 patients into an internal training set (700) and an external test set (212). Univariate analysis identified significant delirium risk factors: midazolam use, prolonged surgery duration, and hypoalbuminemia. Sleep-related variables such as fewer rapid eye movement (REM) episodes and higher daytime sleepiness were also linked to delirium. An extreme gradient-boosting-based classification task achieved an AUC of 0.81 with clinical variables, 0.60 with PSG data alone, and 0.84 with both, demonstrating the added value of PSG data. Analysis of Shapley additive explanations values highlighted important predictors: surgery duration, age, midazolam use, PSG-derived oxygen saturation nadir, periodic limb movement index, and REM episodes, demonstrating the relationship between sleep patterns and the risk of delirium. Conclusions: The artificial intelligence model integrates clinical and sleep variables and reliably identifies postoperative delirium, demonstrating that sleep-related factors contribute to its identification. Predicting patients at high risk of developing postoperative delirium and closely monitoring them could reduce the costs and complications associated with delirium.

USP11 modulates mitotic progression and senescence by regulating the p53-p21 axis through MDM2 deubiquitination.

USP11 is overexpressed in colorectal cancer (CRC) and breast cancer tissues compared to normal tissues, suggesting a role in promoting cell proliferation and inhibiting cell death. In this study, we observed that depleting USP11 inhibits cell proliferation and delays cell cycle progression. This depletion leads to increased p53 protein levels due to an extended half-life, resulting in elevated p21 mRNA levels in a p53-dependent manner. The rise in p53 protein upon USP11 depletion is linked to a reduced half-life of MDM2, a known E3 ligase for p53, via enhanced polyubiquitination of MDM2. These findings indicate that USP11 might act as a deubiquitinase for MDM2, regulating the MDM2-p53-p21 axis. Additionally, USP11 depletion promotes the induction of senescent cells in a manner dependent on its deubiquitinase activity. Our findings provide insights into the physiological significance of high USP11 expression in primary tumors and its reduction in senescent cells, highlighting its potential as a therapeutic target.

Risk Factors and Temporal Patterns of Poststroke Epilepsy across Stroke Subtypes: Insights from a Nationwide Cohort Study in Korea.

INTRODUCTION: We aimed to investigate the risk factors associated with poststroke epilepsy (PSE) among patients with different subtypes of stroke, focusing on age-related risk and time-varying effects of stroke subtypes on PSE development. METHODS: A retrospective, nationwide, population-based cohort study was conducted using Korean National Health Insurance Service-National Sample Cohort data. Patients hospitalized with newly diagnosed stroke from 2005 to 2015 were included and followed up for up to 10 years. The primary outcome was the development of PSE, defined as having a diagnostic code and a prescription for anti-seizure medication. Multivariable Cox proportional hazard models were used to estimate PSE hazard ratios (HRs), and time-varying effects were also assessed. RESULTS: A total of 8,305 patients with ischemic stroke, 1,563 with intracerebral hemorrhage (ICH), and 931 with subarachnoid hemorrhage (SAH) were included. During 10 years of follow-up, 4.6% of patients developed PSE. Among patients with ischemic stroke, significant risk factors for PSE were younger age (HR = 1.47), living in rural areas (HR = 1.35), admission through the emergency room (HR = 1.33), and longer duration of hospital stay (HR = 1.45). Time-varying analysis revealed elevated HRs for ICH and SAH, particularly in the first 2 years following the stroke. The age-specific HRs also showed an increased risk for those under the age of 65, with a noticeable decrease in risk beyond that age. CONCLUSION: The risk of developing PSE varies according to stroke subtype, age, and other demographic factors. These findings underscore the importance of tailored poststroke monitoring and management strategies to mitigate the risk of PSE.

Changes in heart rate variability over time from symptom onset of transient global amnesia.

Transient global amnesia (TGA) often involves precipitating events associated with changes in autonomic nervous system (ANS), and heart rate variability (HRV) reflects the ANS state. This study aimed to investigate HRV changes after TGA. A retrospective analysis of HRV included patients diagnosed with TGA between January 2015 and May 2020. The time and frequency domains of HRV were compared among three groups: early (< 1 week after TGA, n = 19), late (1-4 weeks after TGA, n = 38), and healthy control (HC, n = 19). The Pearson's correlation between time and time-domain HRV was also examined. The standard deviation of NN intervals (SDNN) (early, 47.2; late, 35.5; HC, 41.5; p = 0.033) and root mean square of successive RR interval differences (RMSSD) (early, 38.5; late, 21.3; HC, 31.0; p = 0.006) differed significantly among the three groups. Post-hoc analysis showed statistically significant differences only in the early and late groups in both SDNN (p = 0.032) and RMSSD (p = 0.006) values. However, the frequency domain with total power, low-frequency and high-frequency powers, and low-frequency/high-frequency ratio did not differ. SDNN (Pearson correlation coefficient =- 0.396, p = 0.002) and RMSSD (Pearson correlation coefficient =- 0.406, p = 0.002) were negatively correlated with time after TGA. Changes in HRV occurred over time after the onset of TGA, with the pattern showing an increase in the first week and then a decrease within 4 weeks.

Sirtuin 3 regulates astrocyte activation by reducing Notch1 signaling after status epilepticus.

Sirtuin3 (Sirt3) is a nicotinamide adenine dinucleotide enzyme that contributes to aging, cancer, and neurodegenerative diseases. Recent studies have reported that Sirt3 exerts anti-inflammatory effects in several neuropathophysiological disorders. As epilepsy is a common neurological disease, in the present study, we investigated the role of Sirt3 in astrocyte activation and inflammatory processes after epileptic seizures. We found the elevated expression of Sirt3 within reactive astrocytes as well as in the surrounding cells in the hippocampus of patients with temporal lobe epilepsy and a mouse model of pilocarpine-induced status epilepticus (SE). The upregulation of Sirt3 by treatment with adjudin, a potential Sirt3 activator, alleviated SE-induced astrocyte activation; whereas, Sirt3 deficiency exacerbated astrocyte activation in the hippocampus after SE. In addition, our results showed that Sirt3 upregulation attenuated the activation of Notch1 signaling, nuclear factor kappa B (NF-κB) activity, and the production of interleukin-1ß (IL1ß) in the hippocampus after SE. By contrast, Sirt3 deficiency enhanced the activity of Notch1/NF-κB signaling and the production of IL1ß. These findings suggest that Sirt3 regulates astrocyte activation by affecting the Notch1/NF-κB signaling pathway, which contributes to the inflammatory response after SE. Therefore, therapies targeting Sirt3 may be a worthy direction for limiting inflammatory responses following epileptic brain injury.

Transient Receptor Potential Vanilloid 6 Modulates Aberrant Axonal Sprouting in a Mouse Model of Pilocarpine-Induced Epilepsy.

Transient receptor potential vanilloid 6 (TRPV6) is a highly selective calcium-ion channel that belongs to the TRPV family. TRPV6 is widely distributed in the brain, but its role in neurological diseases such as epilepsy remains unknown. Here, we report for the first time that TRPV6 expression is upregulated in the hippocampus of a pilocarpine-induced status epilepticus model, mainly in the suprapyramidal bundle of the mossy fiber (MF) projection of the hippocampal CA3 regions. We found that TRPV6 overexpression via viral vector transduction attenuated abnormal MF sprouting (MFS), whereas TRPV6 knockdown aggravated the development of MFS and the incidence of recurrent seizures during epileptogenic progression. In the in vitro experiments, our results showed that modulation of TRPV6 expression resulted in a change in axonal formation in cultured hippocampal neurons. In addition, we found that TRPV6 was implicated in the regulation of Akt-glycogen synthase kinase-3-ß activity, which is closely related to the cellular mechanism of axonal outgrowth. Therefore, these findings suggest that TRPV6 may regulate the formation of aberrant synaptic circuits during epileptogenesis.

MRI-Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic-Clonic Seizures Alone.

BACKGROUND: The clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate. PURPOSE: To develop and validate an MRI-based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups. STUDY TYPE: Retrospective. POPULATION: 164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets. FIELD STRENGTH/SEQUENCE: 3T; 3D T1-weighted spoiled gradient-echo. ASSESSMENT: A total of 17 region-of-interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty-eight machine-learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set. STATISTICAL TESTS: Model performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature. RESULTS: The AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591-0.943) and 0.717 (0.563-0.871), respectively. SHAP analysis revealed that the first-order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model. CONCLUSION: The proposed MRI-based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Development and validation of an artificial intelligence model for the early classification of the aetiology of meningitis and encephalitis: a retrospective observational study.

Background: Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process. Methods: In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model. Findings: Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264. Interpretation: This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction. Funding: MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea.

Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.

Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.

Multi-functional regulation of cGAS by the nuclear localization signal2 (NLS2) motif: Nuclear localization, enzyme activity and protein degradation.

Cyclic GMP-AMP synthase (cGAS), which recognizes double-stranded DNA (dsDNA) and activates the innate immune system, is mainly localized in the cytosol, but also shows nuclear localization. Here, we sought to determine the role of nuclear cGAS by mutating known nuclear localization signal (NLS) motifs in cGAS and assessing its functionality by monitoring phosphorylation of the downstream target, interferon regulatory factor-3 (IRF3). Interestingly, NLS2-mutated cGAS failed to promote phosphorylation of IRF3, reflecting the loss of its ability to produce cyclic GMP-AMP (cGAMP). We further found that insertion of an NLS from SV40 large T antigen could not restore this loss of activity, indicating that this loss was attributable to the mutation of NLS2 itself, but not dependent on the inability of cGAS to enter the nucleus. NLS2-mutant cGAS protein also showed decreased stability dependent on polyubiquitination, an effect that was independent of both its loss of catalytic function and its inability to enter into the nucleus. Collectively, these findings indicate that the NLS2 motif of cGAS is not only involved in regulating the subcellular localization of cGAS protein but also influences its stability and enzymatic activity through independent mechanisms, highlighting the novel roles of NLS2 in regulating the intracellular functions of cGAS.

The association between social jetlag and depression is independent of sleep debt.

OBJECTIVES: To investigate whether the association between SJLsc (sleep-corrected social jetlag) and depressive mood is significant and independent of sleep debt. METHODS: Participants from the general adult population were interviewed using structured questionnaires on sleep duration, weekday/weekend sleep schedules, and depressive mood (Patient Health Questionnaire-9). Social jetlag (SJL) was measured by SJLsc and standard SJL (SJLs). SJLs was the absolute difference between mid-sleep time on free days (MSF) and workdays (MSW). For SJLsc, both MSF and MSW were adjusted for average sleep duration across the week according to the direction of sleep debt. Sleep debt was defined by sleep extension on free days. The association of SJL with depression was investigated, and covariates included age, sex, sociodemographic factors, insomnia symptoms, sleep duration, and sleep debt. RESULTS: A total of 1982 individuals (1089 men; age 43.1 ± 14.4 years) were analyzed. SJL was present in 24.6% measured by SJLsc and 51.0% by SJLs. SJLsc and SJLs were significantly associated with depressive mood (r = 0.06, P = 0.02; r = 0.06, P = 0.01, respectively), independent of sleep debt. Sleep debt was also associated with depression (r = 0.07, P < 0.01). CONCLUSIONS: By adopting sleep-corrected formula for SJL, this study found that misaligned and insufficient sleep, at levels occurring in routine social life, can negatively affect mood. Minimizing social jetlag and sleep deprivation may promote individual psychological well-being.

Eugenol alleviates neuronal damage via inhibiting inflammatory process against pilocarpine-induced status epilepticus.

Neuroinflammation is one of the most common pathological outcomes in various neurological diseases. A growing body of evidence suggests that neuroinflammation plays a pivotal role in the pathogenesis of epileptic seizures. Eugenol is the major phytoconstituent of essential oils extracted from several plants and possesses protective and anticonvulsant properties. However, it remains unclear whether eugenol exerts an anti-inflammatory effect to protect against severe neuronal damage induced by epileptic seizures. In this study, we investigated the anti-inflammatory action of eugenol in an experimental epilepsy model of pilocarpine-induced status epilepticus (SE). To examine the protective effect of eugenol via anti-inflammatory mechanisms, eugenol (200 mg/kg) was administrated daily for three days after pilocarpine-induced SE onset. The anti-inflammatory action of eugenol was evaluated by examining the expression of reactive gliosis, pro-inflammatory cytokines, nuclear factor-κB (NF-κB), and the nucleotide-binding domain leucine-rich repeat with a pyrin-domain containing 3 (NLRP3) inflammasome. Our results showed that eugenol reduced SE-induced apoptotic neuronal cell death, mitigated the activation of astrocytes and microglia, and attenuated the expression of interleukin-1ß and tumor necrosis factor α in the hippocampus after SE onset. Furthermore, eugenol inhibited NF-κB activation and the formation of the NLRP3 inflammasome in the hippocampus after SE. These results suggest that eugenol is a potential phytoconstituent that suppresses the neuroinflammatory processes induced by epileptic seizures. Therefore, these findings provide evidence that eugenol has therapeutic potential for epileptic seizures.

Adjudin prevents neuronal damage and neuroinflammation via inhibiting mTOR activation against pilocarpine-induced status epilepticus.

Inflammatory responses in the brain play an etiological role in the development of epilepsy, suggesting that finding novel molecules for controlling neuroinflammation may have clinical value in developing the disease-modifying strategies for epileptogenesis. Adjudin, a multi-functional small molecule compound, has pleiotropic effects, including anti-inflammatory properties. In the present study, we aimed to investigate the effects of adjudin on pilocarpine-induced status epilepticus (SE) and its role in the regulation of reactive gliosis and neuroinflammation. SE was induced in male C57BL/6 mice that were then treated with adjudin (50 mg/kg) for 3 days after SE onset. Immunofluorescence staining, terminal deoxynucleotidyl transferase dUTP nick end labeling staining, and western blot analysis were used to evaluate the effects of adjudin treatment in the hippocampus after SE. Our results showed that adjudin treatment significantly mitigated apoptotic cell death in the hippocampus after SE onset. Moreover, adjudin treatment suppressed SE-induced glial activation and activation of mammalian target of rapamycin signaling in the hippocampus. Concomitantly, adjudin treatment significantly reduced SE-induced inflammatory processes, as confirmed by changes in the expression of inflammatory mediators such as tumor necrosis factor-α, interleukin-1ß, and arginase-1. In conclusion, these findings suggest that adjudin may serve as a potential neuroprotective agent for preventing pathological mechanisms implicated in epileptogenesis.

Sleep state of the elderly population in Korea: Nationwide cross-sectional population-based study.

Objective: Interest in sleep disorders among the elderly, especially those in Korea, has increased. We aimed to describe the overall sleep status of the elderly population in Korea using survey data and to determine the risk factors concerning different aspects of sleep status. Methods: We conducted a cross-sectional survey on 271 respondents aged 65-86 years old. We performed multistage clustered random sampling according to the population and socioeconomic distribution of all Korean territories. The survey questionnaire was used to perform a structural assessment of sociodemographic characteristics; medical comorbidities; psychiatric comorbidities; and sleep status, including sleep duration, sleep quality, presence of insomnia, excessive daytime sleepiness, sleep apnea, and restless legs syndrome. Results: Approximately 12.5, 22.%, and 51.3% of the elderly population had poor sleep quality, excessive daytime sleepiness, and insomnia, respectively. Hypertension, dyslipidemia, insomnia, anxiety, and depression predicted poor sleep quality. Female sex, insomnia, and sleep apnea predicted excessive daytime sleepiness. Poor sleep quality and depression predicted insomnia. Conclusion: A substantial proportion of the elderly Korean population have sleep problems, including poor sleep quality, excessive daytime sleepiness, and insomnia. Sleep status is influenced by various factors, including age, sex, and metabolic and psychiatric comorbidities.

Immunogenicity after Second ChAdOx1 nCoV-19 (AZD1222) Vaccination According to the Individual Reactogenicity, Health Status and Lifestyle.

The immune-acquired responses after vaccination vary depending on the type of vaccine and the individual. The purpose of this study was to investigate the relationship between the acquisition of immunity and the side effects, health status, and lifestyle after completion of the second dose of AZD1222. Blood samples were collected after a second dose of AZD1222. The Euroimmun Anti-SARS-CoV-2 ELISA (IgG) for anti-S1 antibody, the cPASS SARS-CoV-2 neutralizing antibody detection kit for the surrogate virus neutralization test, and the T-spot Discovery SARS-CoV-2 kit were used to identify cellular immunogenicity. Patient experience of adverse effects was investigated using questionnaires. Information on health status and lifestyle were collected from the most recent health checkup data. Generally, females experience more reactogenicity in both intensity and duration. The rash of the first shot and chills of the second shot were associated with humoral immunity. However, comprehensive adverse effects had no correlation with humoral and cellular immunity. The T-spot-positive group had a higher creatinine level, which reflects muscle mass, than the T-spot-negative group. Males presented a higher level of T-spot assays. Body mass index and age were negatively correlated with the T-spot assay and anti-S1 antibody, respectively. Immune acquisition after the second AZD1222 shot was not associated with reactogenicity. However, individuals' sex, age, and BMI were found to be associated with immunogenicity after vaccination.

Weekend catch-up sleep and depression: results from a nationally representative sample in Korea.

BACKGROUND: There is limited information on the association between weekend catch-up sleep (CUS), which has beneficial effects on health, and depression. This study aimed to investigate the association between CUS and depression in adults. METHODS: We used the data of the Seventh Korea National Health and Nutrition Examination Survey, 2016. Depression was defined as Patient Health Questionnaire-9 score ≥10. We categorized CUS duration as ≤0, 0 < to 1, 1 < to 2, and >2 h. RESULTS: Of 5550 eligible participants, 3286 (54.9%), 1033 (19.5%), 723 (14.7%) and 508 (10.9%) had CUS duration ≤0, 0 < to 1, 1 < to 2, and >2 h, respectively; of these, the prevalence of depression was 7.0%, 4.2%, 2.9%, and 6.0%, respectively. Multivariable regression analyses including covariates revealed that individuals with CUS duration 1 < to 2 h had a significantly decreased risk of depression compared to individuals with CUS duration ≤0 h (odds ratio [OR] = 0.517, 95% CI = 0.309-0.865). Individuals with CUS duration 0 < to 1 h (OR = 0.731, 95% CI = 0.505-1.060) and >2 h (OR = 1.164, 95% CI = 0.718-1.886) showed no significantly different risk of depression. CONCLUSIONS: The risk of depression in individuals with CUS duration 1 < to 2 h was lower than for those with CUS duration ≤0 h. This finding provides a better understanding on the association between CUS and depression; and can be a basis for better management of depression.

Impact of Insomnia Symptoms on the Clinical Presentation of Depressive Symptoms: A Cross-Sectional Population Study.

Objective: Insomnia and depression are prevalent disorders that often co-occur. This study aimed to investigate the impact of clinically significant insomnia symptoms on the prevalence and clinical presentation of clinically significant depressive symptoms and vice versa. Methods: This study used data from the Korean Headache-Sleep Study (KHSS), a nationwide cross-sectional population-based survey regarding headache and sleep. Clinically significant insomnia symptoms were defined as Insomnia Severity Index (ISI) scores ≥ 10 and clinically significant depressive symptoms were defined as Patient Health Questionnaire-9 (PHQ-9) scores ≥ 10, respectively. We referred clinically significant insomnia symptoms and clinically significant depressive symptoms as insomnia symptoms and depressive symptoms, respectively. Results: Of 2,695 participants, 290 (10.8%) and 116 (4.3%) were classified as having insomnia and depressive symptoms, respectively. The prevalence of depressive symptoms was higher among participants with insomnia symptoms than in those without insomnia symptoms (25.9 vs. 1.7%, respectively, P < 0.001). Among participants with depressive symptoms, the PHQ-9 scores were not significantly different between participants with and without insomnia symptoms (P = 0.124). The prevalence of insomnia symptoms was significantly higher among participants with depressive symptoms than in those without depressive symptoms (64.7 vs. 8.3%, respectively, P < 0.001). The ISI scores were significantly higher among participants with insomnia and depressive symptoms than in participants with insomnia symptoms alone (P < 0.001). Conclusions: Participants with depressive symptoms had a higher risk of insomnia symptoms than did those without depressive symptoms. The severity of depressive symptoms did not significantly differ based on insomnia symptoms among participants with depressive symptoms; however, the severity of insomnia symptoms was significantly higher in participants with depressive symptoms than in those without depressive symptoms.