Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment

OBJECTIVE

Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment.

METHOD

A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted.

RESULTS

Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters.

CONCLUSION

The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.

Depression and posttraumatic stress disorder in adolescents with nonsuicidal self-injury: comparisons of the psychological correlates and suicidal presentations across diagnostic subgroups

BACKGROUND

Nonsuicidal self-injury (NSSI) combined with suicide ideation increases the risk of suicidal behaviors. Depression and posttraumatic stress disorder (PTSD) are comorbidities of NSSI compounding this relationship. The present study compared diagnostic subgroups of NSSI based on current depression and PTSD on psychological correlates (i.e., vulnerabilities and impairment) and suicidal presentations (i.e., suicidal cognitions and behaviors) in a psychiatric sample of adolescents.

METHODS

Eighty-seven adolescents meeting DSM-5 criteria for NSSI and 104 age-range-matched nonclinical controls (NC) participated. Participants completed self-report measures on psychological vulnerabilities and impairment (e.g., emotion regulation difficulties, negative cognitions). Adolescents with NSSI also completed clinical interviews on psychiatric diagnoses and a recent self-injurious behavior (SIB). Scores on the psychological correlates of NSSI were compared between adolescents with NSSI and NC, and across three diagnostic subgroups of NSSI (A: NSSI+/depression-/PTSD-, n = 14; B: NSSI+/depression+/PTSD-, n = 57; C: NSSI+/depression+/PTSD+, n = 14). Differences between NSSI diagnostic subgroups were tested on the motives for SIB and accompanying suicidal presentations (e.g., desire, intent, motive, lethality).

RESULTS

Common comorbidities of NSSI included depression, panic disorder, generalized anxiety disorder, and PTSD. The NSSI subgroup classification was significantly associated with panic disorder, which was controlled for in the subsequent group comparisons. Overall, adolescents who engage in NSSI with vs. without depression reported more psychological vulnerabilities and impairment and a greater degree of suicidal thoughts/desire in SIB (i.e., groups B, C >A), which remained significant after controlling for panic disorder. An increased odds of the suicidal motive for SIB was found in adolescents with all three conditions (i.e., group C: NSSI+/depression+/PTSD+) compared to those with NSSI but neither depression nor PTSD (i.e., group A: NSSI+/depression-/PTSD-); however, this was not significant after controlling for panic disorder.

CONCLUSIONS

Psychological underpinnings of adolescent NSSI in clinical contexts may be largely associated with concurrent depression. Suicidal motives in adolescents who engage in NSSI in the presence of depression and PTSD may be confounded by the co-occurrence of panic disorder. This study warrants the importance of attending to the comorbid depression with NSSI in adolescents as it is related to an increase in suicidal desire accompanying SIB.

Brain structural correlates of subjective sleepiness and insomnia symptoms in shift workers

Background

Studies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers.

Methods

Shift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms).

Results

No differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers.

Conclusion

Left motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study

The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell's C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.

Intrahospital transmission and infection control of Candida auris originating from a severely infected COVID-19 patient transferred abroad

BACKGROUND

Intrahospital spread of Candida auris, which survives tenaciously in many environments, can cause sustained colonization and infection. A large outbreak of C. auris was experienced in the intensive care units (ICUs) at the study hospital during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

The index patient with severe COVID-19, who was transferred from Vietnam in January 2022, developed C. auris candidaemia 10 days after hospitalization. From mid-June 2022 to January 2023, strengthened infection prevention and control (IPC) measures were implemented in three ICUs: (1) contact precautions and isolation (CPI) for C. auris-positive cases; (2) surveillance cultures including point-prevalence (N=718) for patients or close contacts or ICU-resident healthcare workers (HCWs); (3) intensive environmental disinfection with 10-fold diluted bleach; and (4) 2% chlorhexidine bathing for all ICU patients. Environmental cultures (ECx) on surfaces and shared objects (N=276) were conducted until early September 2022, when all ECx were negative.

RESULTS

Among 53 C. auris-positive patients between February 2022 and January 2023, invasive infections resulted in seven cases of candidaemia and one case of pneumonia. C. auris was isolated from reusable tympanic thermometers (TTMs) contaminated with earwax. The isolation rate of C. auris in ECx decreased from 6.8% in June 2022 to 2.0% in August 2022, and was no longer detected in TTMs. Colonization in HCWs was remarkably rare (0.5%). The number of C. auris-positive patients peaked in July (N=10) then decreased gradually. By January 2023, no C. auris were isolated in the ICU.

CONCLUSION

Aggressive IPC measures with CPI, ECx and surveillance, decontamination of TTMs, and bathing were effective in successfully controlling this C. auris outbreak.

An extremely energetic cosmic ray observed by a surface detector array

Cosmic rays are energetic charged particles from extraterrestrial sources, with the highest-energy events thought to come from extragalactic sources. Their arrival is infrequent, so detection requires instruments with large collecting areas. In this work, we report the detection of an extremely energetic particle recorded by the surface detector array of the Telescope Array experiment. We calculate the particle's energy as [Formula: see text] (~40 joules). Its arrival direction points back to a void in the large-scale structure of the Universe. Possible explanations include a large deflection by the foreground magnetic field, an unidentified source in the local extragalactic neighborhood, or an incomplete knowledge of particle physics.

Circadian misalignment alters resting-state functional connectivity of the salience network in rotating shift workers

STUDY OBJECTIVES

This study compared resting-state functional connectivity (rsFC) of the salience network (SN) between rotating shift workers (RSWs) and controls. Furthermore, we examined whether rsFC of the SN was correlated with sleep, emotion, cognition, and attention.

METHODS

The 60 RSWs and 57 controls enrolled in this study completed self-report questionnaires and sleep diaries to assess subjective sleep quality, and polysomnography and actigraphy to evaluate objective sleep and 24-hour rest-activity rhythm parameters. The participants also underwent resting-state functional magnetic resonance imaging and structural T1 scans. We performed a seed-based rsFC analysis of the SN using the anterior cingulate cortex (ACC) and anterior insula (AI) as seed regions. Furthermore, AI and ACC rsFC were compared in RSWs and controls, and we analyzed correlations between rsFC and variables of interest showing significant group differences.

RESULTS

Compared with controls, RSWs showed reduced rsFC between the ACC and right insula, and increased rsFC of the ACC with the left occipital lobe and right superior frontal gyrus extending to the supplementary motor area (SFG/SMA). Moreover, RSWs showed reduced rsFC between the right AI and right superior parietal lobule (SPL). Finally, rsFC between the ACC and right AI was correlated with 24-hour rest-activity rhythmicity.

CONCLUSIONS

Although RSWs did not show sleep disturbance, emotional distress, cognitive impairment, or attention deficits, alterations of right insula, left occipital lobe, right SFG/SMA, and right SPL rsFC in the SN indicate that impairments in salience detection and top-down attentional control may emerge in shift workers over time.

Amygdala resting-state functional connectivity alterations in patients with chronic insomnia disorder: correlation with electroencephalography beta power during sleep

STUDY OBJECTIVES

This study investigated alterations in resting-state functional connectivity (RSFC) and hyperarousal biomarkers in patients with chronic insomnia disorder (CID), compared with good sleepers (GS). We also examined the relationships between altered RSFC and hyperarousal biomarkers.

METHODS

Fifty patients with CID and 52 GS completed self-reporting questionnaires, and then underwent polysomnography and resting-state functional magnetic resonance imaging. We analyzed RSFC in the amygdala (AMG) and anterior insula (aINS), which are core regions of the salience network that are likely to be involved in hyperarousal. We also analyzed electroencephalography (EEG) relative beta power and heart rate variability (HRV) parameters (e.g. low and high frequency) during sleep. We then tested between-group differences in the RSFC and hyperarousal biomarkers; we examined correlations of RSFC with EEG beta power and HRV.

RESULTS

Compared with GS, patients with CID showed more negative RSFC between the right amygdala (R.AMG) and left supramarginal gyrus (L.SMG), but less positive RSFC between the left aINS and bilateral lateral prefrontal cortex. The R.AMG-L.SMG RSFC was negatively correlated with EEG beta power in central regions (C3: r = -0.336, p = 0.012; C4: r = -0.314, p = 0.024).

CONCLUSIONS

Decreased RSFC between the R.AMG and L.SMG in patients with insomnia may reflect the difficulty in cortical top-down regulation of the AMG, indicating daytime hyperarousal. Individuals who experience hyperarousal during the daytime may also exhibit cortical hyperarousal during sleep, as indicated by increased EEG beta power.

Subgenual Anterior Cingulate Cortex Reactivity to Rejection Vs. Acceptance Predicts Depressive Symptoms among Adolescents with an Anxiety History

OBJECTIVE

The goal of this study was to examine whether neural sensitivity to negative peer evaluation conveys risk for depression among youth with a history of anxiety. We hypothesized that brain activation in regions that process affective salience in response to rejection, relative to acceptance, from virtual peers would predict depressive symptoms 1 year later and would be associated with ecological momentary assessment (EMA) reports of peer connectedness.

METHOD

Participants were 38 adolescents ages 11-16 (50% female) with a history of anxiety, recruited from a previous clinical trial. The study was a prospective naturalistic follow-up of depressive symptoms assessed 2 years (Wave 2) and 3 years (Wave 3) following treatment. At Wave 2, participants completed the Chatroom Interact Task during neuroimaging and 16 days of EMA.

RESULTS

Controlling for depressive and anxiety symptoms at Wave 2, subgenual anterior cingulate (sgACC; β = .39, p = .010) activation to peer rejection (vs. acceptance) predicted depressive symptoms at Wave 3. SgACC activation to rejection (vs. acceptance) was highly negatively correlated with EMA reports of connectedness with peers in daily life (r = - .71, p < .001).

CONCLUSION

Findings suggest that elevated sgACC activation to negative, relative to positive, peer evaluation may serve as a risk factor for depressive symptoms among youth with a history of anxiety, perhaps by promoting vigilance or reactivity to social evaluative threats. SgACC activation to simulated peer evaluation appears to have implications for understanding how adolescents experience their daily social environments in ways that could contribute to depressive symptoms.

Measures of Connectivity and Dorsolateral Prefrontal Cortex Volumes and Depressive Symptoms Following Treatment With Selective Serotonin Reuptake Inhibitors in Adolescents

Importance

Selective serotonin reuptake inhibitors (SSRIs) are considered a first-line pharmacological treatment for adolescent depression with moderate or higher levels of symptom severity. Thus, it is important to understand neurobiological changes related to SSRIs during the course of treatment for adolescents with depression.

Objective

To examine neurobiological changes associated with SSRI treatment in adolescents with major depressive disorder (MDD) by measuring longitudinal changes in volume and resting-state functional connectivity (rsFC) in the dorsolateral prefrontal cortex (DLPFC), a core region of cognitive control.

Design, Setting, and Participants

This cohort study was conducted with an open-label design. Adolescents with MDD and healthy controls were recruited at the Seoul National University Hospital (Seoul, South Korea). Adolescents with MDD were treated with escitalopram for 8 weeks. Data analysis was conducted between April 2021 and February 2022.

Main Outcomes and Measures

Depressive symptoms were assessed using the Children's Depression Rating Scale-Revised. The outcome measure was defined as the change in Children's Depression Rating Scale-Revised scores from week 0 (before treatment) to week 8 (after treatment) or upon termination. Participants completed structural and resting-state functional magnetic resonance imaging (rsfMRI) assessments before (week 0) and after (week 8) SSRI treatment. Repeated measures analysis of variance and liner mixed model analyses were used to examine the longitudinal associations of SSRI treatment with DLPFC volume and rsFC between responders who showed at least a 40% decrease in depressive symptoms and nonresponders who did not.

Results

Ninety-five adolescents with MDD and 57 healthy controls were initially recruited. The final analyses of volume included 36 responders (mean [SD] age, 15.0 [1.6] years; 25 girls [69.4%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). Analyses of rsFC included 33 responders (mean [SD] age, 15.2 [1.5] years; 21 girls [63.6%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). The longitudinal associations of SSRI treatment were more evident in responders than in nonresponders. Responders showed significantly increased right DLPFC volume, decreased bilateral DLPFC rsFC with the superior frontal gyri, and decreased left DLPFC rsFC with the ventromedial PFC after treatment compared with before treatment. Furthermore, increased right DLPFC volume was correlated with decreased rsFC between the right DLPFC and superior frontal gyri after SSRI treatment.

Conclusions and Relevance

The preliminary results of this cohort study suggest that the DLPFC volumetric and rsFC changes may serve as potential neurobiological treatment markers that are associated with symptom improvement in adolescents with MDD.

Photobiomodulation therapy restores olfactory function impaired by photothrombosis in mouse olfactory bulb

An ischemic stroke typically accompanies numerous disorders ranging from somatosensory dysfunction to cognitive impairments, inflicting patients with various neurologic symptoms. Among pathologic outcomes, post-stroke olfactory dysfunctions are frequently observed. Despite the well-known prevalence, therapy options for such compromised olfaction are limited, likely due to the complexity of olfactory bulb architecture, which encompasses both the peripheral and central nervous systems. As photobiomodulation (PBM) emerged for treating ischemia-associated symptoms, the effectiveness of PBM on stroke-induced impairment of olfactory function was explored. Novel mouse models with olfactory dysfunctions were prepared using photothrombosis (PT) in the olfactory bulb on day 0. The post-PT PBM was performed daily from day 2 to day 7 by irradiating the olfactory bulb via an 808 nm laser with a fluence of 40 J/cm2 (325 mW/cm2 for 2 min per day). The buried food test (BFT) was used to score behavioral acuity in food-deprived mice to assess the olfactory function before PT, after PT, and after PBM. Histopathological examinations and cytokine assays were performed on the mouse brains harvested on day 8. The results from BFT were specific to an individual, with positive correlations between the baseline latency time measured before PT and its alteration at the ensuing stages for both the PT and PT + PBM groups. Also, the correlation analysis in both groups showed highly similar, significant positive relationships between the early and late latency time change independent of PBM, implicating a common recovery mechanism. Particularly, PBM treatment accelerated the recovery of impaired olfaction following PT by suppressing inflammatory cytokines and enhancing both glial and vascular factors (e.g., GFAP, IBA-1, and CD31). PBM therapy during the acute phase of ischemia improves the compromised olfactory function by modulating microenvironments and inflammation status of the affected tissue.

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

BACKGROUND

Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem.

OBJECTIVE

We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app.

METHODS

We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week's baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children's Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection.

RESULTS

We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD.

CONCLUSIONS

Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.

The Influence of Life Stress and Sleep Disturbance on White Matter Integrity

OBJECTIVE

This study investigated whether sleep and stress mutually interact to induce changes in white matter integrity.

METHODS

Diffusion tensor imaging (DTI) was conducted on 36 participants (male=22, female=14; mean age=38.33±12.78 years). Participants were divided into three groups depending on their sleep quality and stress levels: poor sleepers with stress, poor sleepers without stress, and good sleepers. Sleep quality and stress level were evaluated using the Pittsburgh Sleep Quality Index and the Life Experiences Survey, respectively. Fractional anisotropy (FA) values were calculated employing DTI tractography.

RESULTS

After controlling for age and sex, poor sleepers with stress exhibited a lower FA of the left inferior cerebellar peduncle (ICP) than did poor sleepers without stress (t=2.81, p=0.02). Poor sleepers without stress showed a higher FA of the right middle longitudinal fasciculus (MdLF) than did good sleepers (t=3.35, p=0.006).

CONCLUSION

The current study reports the effects of sleep, stress, and their interaction on the white matter integrities of the ICP and MdLF. ICP change seems to be associated with sleep disturbances related to stress, while MdLF change would be associated with sleep disturbances unrelated to stress.

Gender differences in osteoporotic hip fractures in Sarawak General Hospital

INTRODUCTION

Osteoporosis and osteoporotic fracture pose a major public health problem in our ageing population, and particularly concerning is the increased morbidity and mortality associated with osteoporotic hip fractures. While overall diagnosis and treatment for osteoporosis have improved, osteoporosis in men remains underdiagnosed and undertreated. We aim to describe the difference in clinical characteristics between elderly men and women with osteoporotic hip fractures in Sarawak General Hospital.

MATERIALS AND METHODS

All patients diagnosed with osteoporotic hip fracture admitted to Sarawak General Hospital from June 2019 to March 2021 were recruited, and demographic data and clinical features were obtained.

RESULTS

There were 140 patients with osteoporotic hip fracture, and 40 were men (28.6%). The mean age for males was 74.1 ± 9.5 years, while the mean age for females was 77.4 ± 9.1 years (p=0.06). The types of fracture consisted of neck of femur=78, intertrochanteric=61 and subtrochanteric=1. More men were active smokers (15% vs 1%, p<0.001). There were 20 men with secondary osteoporosis (50%), while 13 women (13%) had secondary osteoporosis (p<0.001). The causes of secondary osteoporosis among the men were hypogonadism, COPD, glucocorticoid-induced osteoporosis, renal disease, androgen deprivation therapy, thyroid disorder, prostate cancer and previous gastrectomy. There were two deaths among the men and four deaths among the women during the inpatient and 3 months follow-up period. There was no statistical significance between the mortality rates between male patients (5%) and female patients (4%) (p=0.55).

CONCLUSION

There were more females with osteoporotic hip fractures, and there were significantly more males with secondary osteoporotic hip fractures.

Unsupervised anomaly detection with generative adversarial networks in mammography

Breast cancer is a common cancer among women, and screening mammography is the primary tool for diagnosing this condition. Recent advancements in deep-learning technologies have triggered the implementation of research studies via mammography. Semi-supervised or unsupervised methods are often used to overcome the limitations of supervised learning, such as manpower and time, for labeling in clinical situations where abnormal data are significantly lacking. Accordingly, we proposed a generative model that uses a state-of-the-art generative network (StyleGAN2) to create high-quality synthetic mammographic images and an anomaly detection method to detect breast cancer on mammograms in unsupervised methods. The generation model was trained via only normal mammograms and breast cancer classification was performed via anomaly detection using 50 breast cancer and 50 normal mammograms that did not overlap with the dataset for generative model learning. Our generative model has shown comparable fidelity to real images, and the anomaly detection method via this generative model showed high sensitivity, demonstrating its potential for breast cancer screening. This method could differentiate between normal and cancer-positive mammogram and help overcome the weakness of current supervised methods.

Obesity mechanism after hypothalamic damage: Cohort analysis of neuroimaging, psychological, cognitive, and clinical phenotyping data

OBJECTIVE

The hypothalamus regulates energy homeostasis, and its damage results in severe obesity. We aimed to investigate the multifaceted characteristics of hypothalamic obesity.

METHODS

We performed multidimensional analyses of brain structure/function and psychological and behavioral phenotypes in 29 patients with hypothalamic damage (HD) (craniopharyngioma) and 31 controls (non-functional pituitary adenoma). Patients underwent structural and functional magnetic resonance imaging and completed self-reports and cognitive tasks.

RESULTS

Patients with HD showed significantly higher postoperative weight gain than controls. The HD group also showed significant hypothalamic damage and lower neural activation in the left caudate nucleus in response to food images. The HD group had significantly higher food inattention, lower satiety, and higher restrained eating behavior. Within the HD group, higher restrained eating behavior was significantly associated with lower activation in the bilateral fusiform gyrus.

CONCLUSION

These results suggest that hypothalamic damage contributes to weight gain by altering the brain response, attention, satiety, and eating behaviors. The present study proposes novel neuro-psycho-behavioral mechanisms targeted for patients with hypothalamic obesity.

Comparing the efficacy of different methods of faecal microbiota transplantation via oral capsule, oesophagogastroduodenoscopy, colonoscopy, or gastric tube

BACKGROUND

The increasing prevalence of multidrug-resistant organism (MDRO) carriage poses major challenges to medicine as healthcare costs increase. Recently, faecal microbiota transplantation (FMT) has been discussed as a novel and effective method for decolonizing MDRO.

AIM

To compare the efficacy of different FMT methods to optimize the success rate of decolonization in patients with MDRO carriage.

METHODS

This prospective cohort study enrolled patients with MDRO carriages from 2018 to 2021. Patients underwent FMT via one of the following methods: oral capsule, oesophagogastroduodenoscopy (EGD), colonoscopy, or gastric tube.

FINDINGS

A total of 57 patients underwent FMT for MDRO decolonization. The colonoscopy group required the shortest time for decolonization, whereas the EGD group required the longest (24.9 vs 190.4 days, P = 0.022). The decolonization rate in the oral capsule group was comparable to that in the EGD group (84.6% vs 85.7%, P = 0.730). An important clinical factor associated with decolonization failure was antibiotic use after FMT (odds ratio = 6.810, P = 0.008). All four groups showed reduced proportions of MDRO species in microbiome analysis after FMT.

CONCLUSION

Compared to other conventional methods, the oral capsule is an effective FMT method for patients who can tolerate an oral diet. The discontinuation of antibiotics after FMT is a key factor in the success of decolonization.

Clinical Outcomes of Stereotactic Ablative Radiotherapy for All Stages of Non-Small Cell Lung Cancer; Definitive versus Consolidative

Background and Objectives: Stereotactic ablative radiotherapy (SABR) is not confined to early stage non-small cell lung cancer (NSCLC) and has a potential role in stage IV disease. We aimed to evaluate the effect of SABR on local control rates and survival outcomes in patients with all stages of NSCLC according to the treatment aim. Materials and Methods: We retrospectively reviewed the medical records of 88 patients with NSCLC who received SABR at the Korea University Guro Hospital between January 2015 and March 2021. Among these, 64 patients with stage I–II NSCLC ineligible for surgery were treated with a definitive aim. Twenty-four patients with stage IV limited metastatic NSCLC showing a favorable response to prior systemic therapy were treated with a consolidative aim. Results: The median follow-up time was 34 (range: 5–88) months. Thirty-one patients developed recurrence (35.2%), with distant metastasis being the most common (25/31, 80.6%). In-field local recurrence occurred in four patients (4/88 patients, 4.5%). For patients treated with definitive SABR, the 3-year overall survival (OS) and disease-free survival (DFS) rates were 91.8% and 58.6%, respectively. In patients treated with consolidative SABR, the 3-year OS and DFS rates were 86.7% and 53.8%, respectively. With respect to treatment-related pulmonary toxicity, grade 3 radiation pneumonitis incidence requiring hospitalization was 2.3% (2/88). Conclusions: Definitive SABR is appropriate for medically inoperable or high surgical risk patients with early stage NSCLC with acceptable treatment-related toxicities. Consolidative SABR improves local control rates and helps achieve long-term survival in patients with limited metastatic NSCLC.

Neural activation underlying emotional interference of cognitive control in rotating shift workers: moderating effects of the prefrontal cortex response on the association between sleep disturbance and depressive symptoms

Study Objectives

This study investigated the altered neural function involved in emotional interference and its role in linking sleep disturbance and depressive/anxiety symptoms in rotating shift workers.

Methods

Sixty rotating shift workers and 61 controls performed the emotional Stroop task in three blocks (emotional-related, sleep-related, and neutral words) during functional magnetic resonance imaging (fMRI) assessments. Sleep disturbance and depressive/anxiety symptoms were assessed using self-report measures and sleep diaries. Actigraphy was used to assess the sleep and circadian variables. fMRI scans were performed to compare brain activation during the emotional Stroop task. The proposed moderating models were tested using the PROCESS macro in SPSS software.

Results

A significant condition effect on reaction time was detected. Regardless of the group, reaction times were longer in the negative emotional word and sleep-related conditions than in the neutral word condition. Whole-brain analysis revealed that rotating shift workers show greater neural activation in the left dorsolateral prefrontal cortex (DLPFC) compared with controls while performing the emotional Stroop task with negative emotional words. Sleep disturbance was more strongly associated with depressive symptoms when activation of the left DLPFC was higher during the emotional Stroop task with negative words.

Conclusions

The left DLPFC may play important roles in increased sensitivity to emotional information, possibly due to circadian misalignment, and has moderating effects on the association between sleep disturbance and depressive symptoms in rotating shift workers. These findings will help to identify possible brain regions where interventions can be performed to correct sleep and mood problems in rotating shift workers.

Trends in body mass index changes among Korean adolescents between 2005-2020, including the COVID-19 pandemic period: a national representative survey of one million adolescents

OBJECTIVE

The impact of the coronavirus disease 2019 (COVID-19) pandemic on weight gain in children and adolescents remains unknown. We aimed to identify an estimated 15-year trend in mean body mass index (BMI) changes and prevalence of obesity and overweight among Korean adolescents from 2005 to 2020, including the period of the COVID-19 pandemic.

PATIENTS AND METHODS

We analyzed data taken from a nationwide survey (Korea Youth Risk Behavior Survey), between 2005 and 2020. Representative samples of one million Korean adolescents aged 13-18 years (n=1,057,885) were examined. The 15-year trends in mean BMI and proportion of obesity or overweight, and the changes due to the COVID-19 pandemic were analyzed.

RESULTS

The data of 1,057,885 Korean adolescents were analyzed (mean age: 14.98 years; females, 48.4%). The estimated weighted mean BMI was 20.5 kg/m2 [95% confidence interval (CI), 20.4-20.5] from 2005 to 2008 and 21.5 kg/m2 (95% CI, 21.4-21.6) in 2020 (during the COVID-19 pandemic). Although the 15-year trend of mean BMI gradually increased, the change in mean BMI before and during the pandemic significantly lessened (βdiff, -0.027; 95% CI, -0.028 to -0.026). The 15-year (2005-2020) trend changes in the prevalence of obesity and overweight were similar (obesity prevalence from 2005-2008, 3.2%; 95% CI, 3.1-3.3 vs. obesity prevalence in 2020, 8.6%; 95% CI, 8.2-9.0; βdiff, -0.309; 95% CI, -0.330 to -0.288).

CONCLUSIONS

The 15-year trend of overall mean BMI and obesity and overweight prevalence demonstrated a significant increase; however, its slope decreased during the pandemic. These landmark results suggest the need for the development of precise strategies to prevent pediatric obesity and overweight during the COVID-19 pandemic.

Enhancing deep learning based classifiers with inpainting anatomical side markers (L/R markers) for multi-center trials

BACKGROUND AND OBJECTIVE

The protocol for placing anatomical side markers (L/R markers) in chest radiographs varies from one hospital or department to another. However, the markers have strong signals that can be useful for deep learning-based classifier to predict diseases. We aimed to enhance the performance of a deep learning-based classifiers in multi-center datasets by inpainting the L/R markers.

METHODS

The L/R marker was detected with using the EfficientDet detection network; only the detected regions were inpainted using a generative adversarial network (GAN). To analyze the effect of the inpainting in detail, deep learning-based classifiers were trained using original images, marker-inpainted images, and original images clipped using the min-max value of the marker-inpainted images. Binary classification, multi-class classification, and multi-task learning with segmentation and classification were developed and evaluated. Furthermore, the performances of the network on internal and external validation datasets were compared using DeLong's test for two correlated receiver operating characteristic (ROC) curves in binary classification and Stuart-Maxwell test for marginal homogeneity in multi-class classification and multi-task learning. In addition, the qualitative results of activation maps were evaluated using the gradient-class activation map (Grad-CAM).

RESULTS

Marker-inpainting preprocessing improved the classification performances. In the binary classification based on the internal validation, the area under the curves (AUCs) and accuracies were 0.950 and 0.900 for the model trained on the min-max clipped images and 0.911 and 0.850 for the model trained on the original images, respectively (P-value=0.006). In the external validation, the AUCs and accuracies were 0.858 and 0.677 for the model trained using the inpainted images and 0.723 and 0.568 for the model trained using the original images (P-value<0.001), respectively. In addition, the models trained using the marker inpainted images showed the best performance in multi-class classification and multi-task learning. Furthermore, the activation maps obtained using the Grad-CAM improved with the proposed method. The 5-fold validation results also showed improvement trend according to the preprocessing strategies.

CONCLUSIONS

Inpainting an L/R marker significantly enhanced the classifier's performance and robustness, especially in internal and external studies, which could be useful in developing a more robust and accurate deep learning-based classifier for multi-center trials. The code for detection is available at: https://github.com/mi2rl/MI2RLNet. And the code for inpainting is available at: https://github.com/mi2rl/L-R-marker-inpainting.

Life stress, sleep disturbance and depressive symptoms: The moderating role of prefrontal activation during emotion regulation

OBJECTIVES

Evidence suggests that emotion regulation difficulty may play an important role in the association between life stress, sleep disturbance and depressive symptoms. We proposed two models depicting the possible moderating roles of prefrontal cortex activation during emotion regulation in the associations among these variables and tested them. We hypothesized that (1) the association between stress and sleep disturbance would differ across prefrontal cortex activation during emotion regulation (moderation model) and (2) the indirect effects of stress on depressive symptoms through sleep disturbance would depend on prefrontal cortex activation during emotion regulation (moderated mediation model).

METHODS

Forty-eight healthy adults without sleep disorders based on nocturnal polysomnography participated in this study. They received functional magnetic resonance imaging scans while performing an emotion regulation task. They also completed questionnaires assessing life stress, sleep disturbance and depressive symptoms. The proposed models were tested using the PROCESS macro for SPSS.

RESULTS

As hypothesized, there was a significant moderating effect of prefrontal cortex activation during emotion regulation on the association between life stress and sleep disturbance. Furthermore, right lateral prefrontal cortex activation had a moderating role in the indirect effect of life stress on depressive symptoms through sleep disturbance.

CONCLUSION

These findings highlight the important role of prefrontal cortex function during emotion regulation in the associations between stress, sleep disturbance and depressive symptoms. Increasing lateral prefrontal cortex recruitment when regulating the emotional response to negative life events may be critical for the prevention and intervention of depression as well as sleep problems.

COVID-19 susceptibility and clinical outcomes in autoimmune inflammatory rheumatic diseases (AIRDs): a systematic review and meta-analysis

OBJECTIVE

This meta-analysis aims to assess the susceptibility to and clinical outcomes of COVID-19 in autoimmune inflammatory rheumatic disease (AIRD) and following AIRD drug use.

MATERIALS AND METHODS

We included observational and case-controlled studies assessing susceptibility and clinical outcomes of COVID-19 in patients with AIRD as well as the clinical outcomes of COVID-19 with or without use of steroids and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs).

RESULTS

Meta-analysis including three studies showed that patients with AIRD are not more susceptible to COVID-19 compared to patients without AIRD or the general population (OR: 1.11, 95% CI: 0.58 to 2.14). Incidence of severe outcomes of COVID-19 (OR: 1.34, 95% CI: 0.76 to 2.35) and COVID-19 related death (OR: 1.21, 95% CI: 0.68 to 2.16) also did not show significant difference. The clinical outcomes of COVID-19 among AIRD patients with and without csDMARD or steroid showed that both use of steroid (OR: 1.69, 95% CI: 0.96 to 2.98) or csDMARD (OR: 1.35, 95% CI: 0.63 to 3.08) had no effect on clinical outcomes of COVID-19.

CONCLUSIONS

AIRD does not increase susceptibility to COVID-19, not affecting the clinical outcome of COVID-19. Similarly, the use of steroids or csDMARDs for AIRD does not worsen the clinical outcome.