The Relationship Between Fast Food Consumption and Daily Lifestyle Changes During School Closures Following the COVID-19 Pandemic: A Cross-Sectional Study Among Adolescents in Korea.

OBJECTIVE: Increased fast food consumption can have adverse effects on health and well-being among adolescents, posing a significant public health concern. The school closures due to the coronavirus disease-2019 (COVID-19) pandemic have led to changes in eating patterns and disrupted a balance diet among adolescents. This study explored the factors associated with fast food consumption among adolescents during school closures due to the COVID-19 pandemic. METHODS: A total of 1,710 middle and high school students in Gwangju, South Korea participated in a cross-sectional survey. The self-administered questionnaire included items assessing dietary intake, physical activity, sleep, media use, and sociodemographic information. The Patient Health Questonnaire-9, Generalized Anxiety Disorder-7, and three item version of the UCLA Loneliness Scale were also administered. Multivariable logistic regression was used to examine the factors associated with increased fast food consumption. RESULTS: Approximately 34.6% of the surveyed adolescents reported increased fast food consumption during school closures, as well as increased sleep duration, increased sedentary behaviors including watching TV and using the internet, and reduced physical activity. Multivariable logistic regression analysis revealed that fast food consumption during school closures was associated with irregular patterns of main meals and sleep, decreased physical activity, increased internet use, and a lack of daytime adult supervision. CONCLUSION: Our results highlight the need for dietary and lifestyle monitoring and guidelines to promote health among adolescents, especially during school closures. In conclusion, nutrition intervention programs aiming to limit fast food consumption and enhance healthy dietary habits among adolescents during long-term school closures are warranted.

The Influence of Psychological Factors on Physical Activity in Individuals With Patellofemoral Pain.

CONTEXT: Patellofemoral pain (PFP) is a prevalent chronic condition characterized by retropatellar or peripatellar pain exacerbated by various knee flexion-based activities. Previous research has highlighted the impact of psychological constructs on pain and function in chronic musculoskeletal pain conditions, yet their influence on physical activity in PFP cohorts remains unexplored. We aimed to evaluate whether pain self-efficacy and pain catastrophizing predict variations in steps per day and moderate to vigorous physical activity (MVPA) among individuals with PFP. DESIGN: Cross-sectional observational study. METHODS: Thirty-nine participants (11 males) with PFP were included. Dependent variables were steps per day and minutes of MVPA. Independent variables were pain self-efficacy and pain catastrophizing, measured by the pain self-efficacy questionnaire and the pain catastrophizing scale. Participants were given an ActiGraph wGT3X-BT for 7 days to assess physical activity. Correlations were assessed between psychological measures and physical activity, and a simple linear regression was performed on psychological variables that correlated with physical activity. Alpha was set a priori at P < .05. RESULTS: Pain self-efficacy scores displayed a moderate association with steps per day (rho = .45, P = .004) and a weak association with MVPA (rho = .38, P = .014). Pain catastrophizing scores exhibited no significant associations with physical activity (P < .05). Regression models affirmed pain self-efficacy scores as significant predictors of both steps per day (F1,37 = 10.30, P = .002) and MVPA (F1,37 = 8.98, P = .004). CONCLUSIONS: Psychological measures continue to demonstrate value to clinicians treating PFP. Pain self-efficacy scores were moderately associated with steps per day and weakly associated with MVPA, explaining nearly a fifth of the variation in physical activity. Clinicians should prioritize the assessment of pain self-efficacy when treating individuals with PFP, potentially employing psychological interventions to improve physical activity in the PFP population.

Clinical, cognitive, and functional characteristics of recent-onset psychosis with autistic features: A 2-year longitudinal study.

Though categorized as separate illnesses, schizophrenia and autism are known to exhibit shared characteristics. This study explored the distinctions in clinical, cognitive, and functional characteristics among individuals with recent-onset psychosis, considering the severity of their autistic symptoms, involving longitudinal examinations. We analyzed 671 patients with recent-onset psychosis from Korean Early Psychosis Cohort Study (KEPS), and used the PANSS Autism Severity Score (PAUSS) to categorize patient into 'autistic', 'moderate', and 'non-autistic' groups. The autistic group had the highest rate of schizophrenia diagnosis, and the lowest incidence of comorbid psychiatric disorders. Schizophrenia diagnosis predicted membership of the autistic group. More severe autistic symptoms correlated with worse overall symptoms and functional outcomes, which significantly predicted membership of the autistic group. Cognitive impairments and emotional recognition difficulties increased with the severity of autistic symptoms. 2-year longitudinal assessments demonstrated that group differences in autistic features and overall symptoms, and functional outcomes remained consistent, and membership of the autistic group significantly predicted symptomatic remission and functional recovery. In conclusion, the presence of autistic symptoms has a significant impact on the overall symptomatology and functional capabilities. They are enduring attributes rather than temporary state variables, and serve as a significant predictor for both symptomatic and functional recovery.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Design and validation of a wearable dynamometry system for knee extension-flexion torque measurement.

Muscle strength assessments are vital in rehabilitation, orthopedics, and sports medicine. However, current methods used in clinical settings, such as manual muscle testing and hand-held dynamometers, often lack reliability, and isokinetic dynamometers (IKD), while reliable, are not easily portable. The aim of this study was to design and validate a wearable dynamometry system with high accessibility, accuracy, and reliability, and to validate the device. Therefore, we designed a wearable dynamometry system (WDS) equipped with knee joint torque sensors. To validate this WDS, we measured knee extension and flexion strength in 39 healthy adults using both the IKD and WDS. Comparing maximal isometric torque measurements, WDS and IKD showed strong correlation and good reliability for extension (Pearson's r: 0.900; intraclass correlation coefficient [ICC]: 0.893; standard error of measurement [SEM]: 9.85%; minimal detectable change [MDC]: 27.31%) and flexion (Pearson's r: 0.870; ICC: 0.857; SEM: 11.93%; MDC: 33.07%). WDS demonstrated excellent inter-rater (Pearson's r: 0.990; ICC: 0.993; SEM: 4.05%) and test-retest (Pearson's r: 0.970; ICC: 0.984; SEM: 6.15%) reliability during extension/flexion. User feedback from 35 participants, including healthcare professionals, underscores WDS's positive user experience and clinical potential. The proposed WDS is a suitable alternative to IKD, providing high accuracy, reliability, and potentially greater accessibility.

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neoplasm versus non-neoplasm categories, as well as T-stage estimation in cancer lesions (T1, T2-T4). The lesion detection rate was 95.22% (219/230 patients) on a per-patient basis: 100% for EGC, 97.22% for AGC, 96.49% for dysplasia, 75.00% for BGU, 97.22% for benign polyps, and 80.49% for benign erosion. The six-class category exhibited an accuracy of 73.43%, sensitivity of 80.90%, specificity of 83.32%, positive predictive value (PPV) of 73.68%, and negative predictive value (NPV) of 88.53%. The sensitivity and NPV were 78.62% and 88.57% for the cancer versus non-cancer category, and 83.26% and 89.80% for the neoplasm versus non-neoplasm category, respectively. The T stage estimation model achieved an accuracy of 85.17%, sensitivity of 88.68%, specificity of 79.81%, PPV of 87.04%, and NPV of 82.18%. The novel CNN-based model remarkably detected and classified malignant, premalignant, and benign gastric lesions and accurately estimated gastric cancer T-stages.

Differential predictors of early- and delayed-onset post-traumatic stress disorder following physical injury: a two-year longitudinal study.

Objectives: This study aimed to investigate the predictors of both early- and delayed-onset PTSD over a 2-year period following physical injuries. Methods: Patients were recruited from a trauma center at a university hospital in South Korea (June 2015 ~ January 2021). At baseline, 1142 patients underwent comprehensive assessments including socio-demographic, pre-trauma, trauma-related, and peri-trauma evaluations. Diagnoses of acute stress disorder (ASD) and subthreshold ASD were also determined using the Clinician-administered PTSD Scale (CAPS). Follow-up assessments at three months included diagnoses of PTSD and subthreshold PTSD using CAPS, and stressful life events (SLEs), with additional evaluations at 6, 12, and 24 months. The analyzed sample comprised 1014 patients followed up at least once after the baseline and 3-month evaluations. PTSD diagnoses were categorized into early-onset (within the first six months after trauma) and delayed-onset (more than six months after trauma). Logistic regression models identified predictors for each group. Results: Early-onset and delayed-onset PTSD were diagnosed in 79 and 35 patients, respectively. Early-onset PTSD was predicted by previous psychiatric disorders, previous traumatic events, ASD and subthreshold ASD diagnoses, and higher anxiety levels. In contrast, delayed-onset PTSD was linked to higher education, higher injury severity, and subthreshold PTSD and SLEs at 3-month follow-up. Conclusion: Distinct predictors were found for early-onset and delayed-onset PTSD. The findings underscore the heterogeneous factors influencing the temporal development of PTSD post-trauma, and may provide valuable guidance for more targeted interventions and improved patient outcomes.

Hip rate of torque development, but not strength, is associated with single-leg squat kinematics in individuals with patellofemoral pain.

INTRODUCTION: Isometric hip strength seems to have limited association with frontal plane kinematics in individuals with patellofemoral pain (PFP), but little is known about the association with hip rate of torque development (RTD). OBJECTIVE: To explore the association of hip strength and RTD with trunk, pelvis, hip, and knee kinematics during a single-leg squat (SLS) in individuals with PFP. METHODS: Twenty individuals with PFP participated in this study. Hip abductor and extensor strength and RTD (early phase and late phase) were assessed using a hand-held dynamometer. Lateral trunk motion, pelvic drop, hip frontal plane projection angle (HFPPA), and knee frontal plane projection angle (KFPPA) were evaluated during a SLS using a two-dimensional motion analysis. RESULTS: Lower early and late phase hip abductor RTD were moderately associated with greater HFPPA (early phase: r = -0.501, p = .025; late phase: r = -0.580, p = .007) and KFPPA (early phase: r = -0.536, p = .015; late phase: r = -0.554, p = .011). Lower early phase hip extensor RTD was moderately associated with greater pelvic drop (r = 0.571, p = .009), HFPPA (r = -0.548, p = .012), and KFPPA (r = -0.530, p = .016). Hip abductor and extensor strength were not associated with any kinematic variables (p > .05). CONCLUSION: Lower hip RTD, but not strength, was associated with greater frontal plane kinematics during a SLS in individuals with PFP, indicating that the ability to produce torque rapidly may be important for kinematic control during functional tasks.

Moderating role of functioning level on the association between suicidal ideation and delayed-onset post-traumatic stress disorder.

OBJECTIVES: This study aimed to investigate the relationship between suicidal ideation at baseline and the development of post-traumatic stress disorder (PTSD) in individuals who have experienced physical injuries, with a specific focus on how this relationship is moderated by the patient's functioning level. METHODS: Participants were consecutively recruited from a trauma center and prospectively followed for two years. At baseline, suicidal ideation was assessed using the Brief Psychiatric Rating Scale, and functioning level was evaluated using the Social and Occupational Functioning Assessment Scale. During the follow-up, PTSD diagnosis was established using the Clinician-Administered PTSD Scale for DSM-5. Binary and multinomial logistic regression analyses were employed to examine the associations between suicidal ideation, functioning level, and PTSD. RESULTS: Of the 1014 participants analyzed, 114 (11.2%) developed PTSD, with early-onset observed in 79 (7.8%) and delayed-onset in 35 (3.5%) cases. Suicidal ideation at baseline was significantly associated with both early- and delayed-onset PTSD. Notably, higher functioning individuals with baseline suicidal ideation had an increased likelihood of developing delayed-onset PTSD, while this association was not significant in lower functioning individuals, with significant interaction terms. Additionally, suicidal ideation was a consistent predictor of early-onset PTSD across all functioning levels. CONCLUSION: The impact of baseline suicidal ideation on PTSD varies depending on the individual's functioning level, with higher functioning individuals being more vulnerable to delayed-onset PTSD. These findings underscore the importance of considering functional status in the assessment and intervention of PTSD following physical trauma.

Understanding the Fluctuations in Korea's Suicide Rates: A Change-Point Analysis and Interrupted Time Series Analysis.

BACKGROUND: Korea has witnessed significant fluctuations in its suicide rates in recent decades, which may be related to modifications in its death registration system. This study aimed to explore the structural shifts in suicide trends, as well as accidental and ill-defined deaths in Korea, and to analyze the patterns of these changes. METHODS: We analyzed age-adjusted death rates for suicides, deaths due to transport accidents, falls, drowning, fire-related incidents, poisonings, other external causes, and ill-defined deaths in Korea from 1997 to 2021. We identified change-points using the 'breakpoints' function from the 'strucchange' package and conducted interrupted time series analyses to assess trends before and after these change-points. RESULTS: Korea's suicide rates had three change-points in February 2003, September 2008, and June 2012, characterized by stair-step changes, with level jumps at the 2003 and 2008 change-points and a sharp decline at the 2012 change-point. Notably, the 2003 and 2008 spikes roughly coincided with modifications to the death ascertainment process. The trend in suicide rates showed a downward slope within the 2003-2008 and 2008-2012 periods. Furthermore, ill-defined deaths and most accidental deaths decreased rapidly through several change-points in the early and mid-2000s. CONCLUSION: The marked fluctuations in Korea's suicide rate during the 2000s may be largely attributed to improvements in suicide classification, with potential implications beyond socio-economic factors. These findings suggest that the actual prevalence of suicides in Korea in the 2000s might have been considerably higher than officially reported.

Brain Derived Neurotrophic Factor Methylation and Long-term Outcomes after Stroke Interacting with Suicidal Ideation.

Objective: This study aimed to evaluate the unexplored relationship between BDNF methylation, long-term outcomes, and its interaction with suicidal ideation (SI), which is closely associated with both BDNF expression and stroke outcomes. Methods: A total of 278 stroke patients were assessed for BDNF methylation status and SI using suicide-related item in the Montgomery-Åsberg Depression Rating Scale at 2 weeks post-stroke. We investigated the incidence of composite cerebro-cardiovascular events (CCVEs) during an 8-14-year period after the initial stroke as long-term stroke outcome. We conducted Cox regression models adjusted for covariates to evaluate the association between BDNF methylation status and CCVEs, as well as its interaction with post-stroke SI at 2 weeks. Results: Higher methylation status of CpG 1, 3, and 5, but not the average value, predicted a greater number of composite CCVEs during 8-14 years following the stroke. The associations between a higher methylation status of CpGs 1, 3, 5, and 8, as well as the average BDNF methylation value, and a greater number of composite CCVEs, were prominent in patients who had post-stroke SI at 2 weeks. Notably, a significant interaction between methylation status and SI on composite CCVEs was observed only for CpG 8. Conclusion: The significant association between BDNF methylation and poor long-term stroke outcomes, particularly amplified in individuals who had post-stroke SI at 2 weeks, suggested that evaluating the biological marker status of BDNF methylation along with assessing SI during the acute phase of stroke can help predict long-term outcomes.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling.

BACKGROUND: Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process. METHOD: Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters. RESULTS: Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance. CONCLUSION: Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Identification of depression in patients with acute coronary syndrome using multiple serum biomarkers.

BACKGROUND: Biomarkers for depression in patients with acute coronary syndrome (ACS) have not been identified. METHODS: This study evaluated multiple serum biomarkers for depressive disorders after ACS. Thirteen serum biomarkers associated with seven functional systems, along with sociodemographic/clinical characteristics, were evaluated in 969 patients within 2 weeks after ACS onset (acute phase). In total, 711 patients were evaluated for depressive disorder using DSM-IV criteria 1 year later (chronic phase). Logistic regression was used for the analysis. RESULTS: Depressive disorders were observed in 378 patients (39.0%) in the acute phase of ACS and 183 patients (25.7%) in the chronic phase. The weighted scores of five serum biomarkers (high-sensitivity C-reactive protein, interleukin-6, homocysteine, troponin I, and creatine kinase-MB) were significantly associated with depressive disorder diagnosis in the acute phase, and the weighted scores of three other biomarkers (tumor necrosis factor-alpha, interleukin-1 beta, and homocysteine) were significantly associated with depressive disorders in the chronic phase, in a dose-dependent manner after adjusting for relevant covariates (all P-values <0.001). CONCLUSIONS: The combination of several serum biomarkers exhibited robust associations with depressive disorders in both the acute and chronic phases of ACS.

Impact of suicidality on delayed-onset post-traumatic stress disorder is modified by age: A two-year longitudinal study.

OBJECTIVES: This study aimed to investigate the relationship between early suicidality and post-traumatic stress disorder (PTSD) onset in patients with physical injuries, focusing on age as a modifying factor. METHODS: At baseline, 1014 patients were evaluated for suicidality, age divided into younger (<60 years) vs. older (≥60 years) groups, and potential covariates. PTSD was diagnosed at follow-up at 3, 6, 12, and 24 months, and then were categorized into early-onset (within the first six months after trauma) and delayed-onset (more than six months after trauma). Logistic regression models were used after adjusting for covariates. RESULTS: Presence of suicidality at baseline was significantly associated with delayed-onset PTSD in older but not younger patients with significant interaction terms, whereas it was significantly associated with early-onset PTSD across all age groups. CONCLUSION: Age-specific associations were found between suicidality and PTSD onset. The findings highlight the importance of early suicidality assessment, especially in older patients for ongoing monitoring and support, and underscore the critical need for early PTSD identification and intervention for all ages.

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

A multimodal virtual vision platform as a next-generation vision system for a surgical robot.

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot.

Predicting suicidal ideation using multiple serum biomarkers in patients with acute coronary syndrome.

BACKGROUND: Biomarkers for suicidal behavior in patients with acute coronary syndrome (ACS) have yet to be elucidated. This study aimed to identify a panel of serum biomarkers associated with suicidal ideation (SI) in patients with ACS. METHODS: The study evaluated 969 patients within 2 weeks of ACS (acute phase) and 711 patients 12 months later (chronic phase). The evaluation included 14 serum biomarkers covering 7 functional systems, socio-demographic/clinical characteristics, and SI assessed by the "suicidal thoughts" item of the Montgomery-Åsberg Depression Rating Scale. Logistic regression models were used to analyze the data. The results showed that 195 patients (20.1 %) had SI in the acute phase, and 87 patients (12.2 %) had SI in the chronic phase. RESULTS: A combination of five serum biomarkers (tumor necrosis factor-α, interleukin-1ß, folate, troponin I, and creatine kinase-MB) was significantly associated with SI in the acute phase, and a combination of three serum biomarkers (tumor necrosis factor-α, interleukin-1ß, and folate) was significantly associated with SI in the chronic phase in a clear dose-dependent manner (all P-values < 0.001) after adjustment for relevant covariates. DISCUSSION: These findings suggest that the application of a combination of multiple serum biomarkers could improve the predictability of SI in patients with ACS at both acute and chronic phases.

Use of Serum Biomarkers to Aid Antidepressant Selection in Depressive Patients.

Objective: : This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in out-patients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. Methods: : This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3-12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. Results: : Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1ß and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. Conclusion: : Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.