Photobiomodulation Therapy on Brain: Pioneering an Innovative Approach to Revolutionize Cognitive Dynamics.

Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson's disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM's potential as a therapeutic modality capable of effectively impeding disease progression within the organism-a task often elusive with conventional pharmacological interventions.

Human vascular organoids with a mosaic AKT1 mutation recapitulate Proteus syndrome.

Vascular malformation, a key clinical phenotype of Proteus syndrome, lacks effective models for pathophysiological study and drug development due to limited patient sample access. To bridge this gap, we built a human vascular organoid model replicating Proteus syndrome's vasculature. Using CRISPR/Cas9 genome editing and gene overexpression, we created induced pluripotent stem cells (iPSCs) embodying the Proteus syndrome-specific AKTE17K point mutation for organoid generation. Our findings revealed that AKT overactivation in these organoids resulted in smaller sizes yet increased vascular connectivity, although with less stable connections. This could be due to the significant vasculogenesis induced by AKT overactivation. This phenomenon likely stems from boosted vasculogenesis triggered by AKT overactivation, leading to increased vascular sprouting. Additionally, a notable increase in dysfunctional PDGFRß+ mural cells, impaired in matrix secretion, was observed in these AKT-overactivated organoids. The application of AKT inhibitors (ARQ092, AZD5363, or GDC0068) reversed the vascular malformations; the inhibitors' effectiveness was directly linked to reduced connectivity in the organoids. In summary, our study introduces an innovative in vitro model combining organoid technology and gene editing to explore vascular pathophysiology in Proteus syndrome. This model not only simulates Proteus syndrome vasculature but also holds potential for mimicking vasculatures of other genetically driven diseases. It represents an advance in drug development for rare diseases, historically plagued by slow progress.

Calibration of toroidal visible bremsstrahlung diagnostics and reconstruction of effective charge profiles in Korea Superconducting Tokamak Advanced Research (KSTAR).

The investigation of impurity behavior in fusion plasmas is a critical issue in fusion plasma research. The effective charge (Zeff) profile is a widely used measure of the impurity levels in fusion plasmas. In this study, the visible bremsstrahlung emissivity profile is reconstructed using toroidal visible bremsstrahlung (TVB) arrays at Korea Superconducting Tokamak Advanced Research (KSTAR). KSTAR TVB arrays have recently been developed and calibrated using a halogen light source and an integrating sphere. The reconstruction algorithm has been developed using the Phillips-Tikhonov method, and the reconstruction accuracy is assessed with test profiles. Electron density and temperature profiles from Thomson scattering diagnostics are fitted for Zeff calculations. Subsequently, the Zeff profiles in the edge localized mode suppression experiment are reconstructed. In addition, line-averaged Zeff values in the 2020 KSTAR campaign are presented, which are mostly distributed from two to four.

Functional connectivity and network analysis in adolescents with major depressive disorder showing suicidal behavior.

BACKGROUND: This study aimed to gather a homogeneous sample of adolescent patients to analyze the differences in functional connectivity and brain network parameters between suicidal and non-suicidal major depressive disorder (MDD) patients using a data-driven whole-brain approach. METHODS: Patients recruited at the psychiatry department of Korea University Guro Hospital from November 2014 to March 2020 were diagnosed with MDD, were 13-18 years old, had IQ scores >80, had no family history of psychotic or personality disorders, had no smoking or alcohol consumption history, and were drug-naïve to psychotropic medication. Depressive symptoms were assessed using the Hamilton Depression Rating Scale and the Children's Depression Inventory. Structural and functional MRI scans were conducted and analyzed using the CONN toolbox. RESULTS: Of 74 enrolled patients, 62 were analyzed. Regions of interest (ROIs) showing higher betweenness centrality in non-suicidal patients were the left superior temporal gyrus and left supramarginal gyrus. ROIs showing higher betweenness centrality in suicidal patients were the right hippocampus, left intracalcarine cortex, right inferior temporal gyrus, and the lateral visual network. Suicidal patients also showed different resting state functional connectivity profiles from non-suicidal patients. LIMITATIONS: Small sample size. CONCLUSION: Suicidal patients may overthink and overvalue future risks while having a more negatively biased autobiographical memory. Social cognition and the ability to overcome egocentricity bias seem to weaken. Such features can disrupt cognitive recovery and resilience, leading to more suicidal behaviors. Therefore, increased suicidality is not acquired, but is an innate trait.

High-Resolution Tactile-Sensation Diagnostic Imaging System for Thyroid Cancer.

In this study, we propose the direct diagnosis of thyroid cancer using a small probe. The probe can easily check the abnormalities of existing thyroid tissue without relying on experts, which reduces the cost of examining thyroid tissue and enables the initial self-examination of thyroid cancer with high accuracy. A multi-layer silicon-structured probe module is used to photograph light scattered by elastic changes in thyroid tissue under pressure to obtain a tactile image of the thyroid gland. In the thyroid tissue under pressure, light scatters to the outside depending on the presence of malignant and positive properties. A simple and easy-to-use tactile-sensation imaging system is developed by documenting the characteristics of the organization of tissues by using non-invasive technology for analyzing tactile images and judging the properties of abnormal tissues.

The Novel Digital Therapeutics Sensor and Algorithm for Pressure Ulcer Care Based on Tissue Impedance.

Visual diagnosis and rejuvenation are methods currently used to diagnose and treat pressure ulcers, respectively. However, the treatment process is difficult. We developed a biophotonic sensor to diagnose pressure ulcers and, subsequently, developed a pressure ulcer care device (PUCD.) We conducted animal and clinical trials to investigate the device's effectiveness. We confirmed the accuracy of the pressure ulcer diagnosis algorithm to be 91% and we observed an 85% reduction in immune cells when using the PUCD to treat pressure ulcer-induced mice. Additionally, we compared the treatment group to the pressure ulcer induction group to assess the PUCD's effectiveness in identifying immune cells through its nuclear shape. These results indicate a positive effect and suggest the use of PUCD as a recovery method for pressure ulcer diagnosis and treatment.

Altered Functional Connectivity of the Nucleus Accumbens and Amygdala in Cyber Addiction: A Resting State Functional Magnetic Resonance Imaging Study.

Objective: Cyber addiction, which is more vulnerable in adolescents, is defined as the excessive use of computers and the Internet that causes serious psychological, social, and physical problems. In this study, we investigated the resting-state functional connectivity (rsFC) in adolescents with cyber addiction. Methods: We collected and analyzed resting-state functional neuroimaging data of 20 patients with cyber addiction, aged 13-18 years, and 27 healthy controls. Based on previous studies, the seed regions included the dorsolateral prefrontal cortex, medial orbitofrontal cortex, lateral orbitofrontal cortex, dorsal anterior cingulate cortex, insula, hippocampus, amygdala, nucleus accumbens, and the ventral tegmental area. Seed-to-voxel analyses were performed to investigate the differences between patients and healthy controls. A correlation analysis between rsFC and cyber addiction severity was also performed. Results: Patients with cyber addiction showed the following characteristics: increased positive rsFC between the left insular-right middle temporal gyrus; increased positive rsFC between the right hippocampus-right precentral gyrus; increased positive rsFC between the right amygdala-right precentral gyrus and right parietal operculum cortex; increased negative rsFC between the left nucleus accumbens-right cerebellum crus II and right cerebellum VI. Conclusion: Adolescents with cyber addiction show altered functional connectivity during the resting state. The findings of this study may help us better understand the neuropathology of cyber addiction in adolescents.

Efficacy of 3D-printed simulation models of unruptured intracranial aneurysms in patient education and surgical simulation.

OBJECTIVE: The purpose of this study was to determine the efficacy of a 3D-printed aneurysm simulation model (3DPM) in educating patients and improving physicians' comprehension and performance. METHODS: This prospective study involved 40 patients who were diagnosed with unruptured intracranial aneurysms (UIAs) and scheduled for surgical clipping or endovascular coiling and randomly divided into two groups (the 3DPM group and the non-3DPM group). The 3DPM was used in preoperative consultation with patients and intraoperatively referenced by surgeons. The patients, 7 neurosurgical residents, and 10 surgeons completed questionnaires (5-point Likert scale) to determine the usefulness of the 3DPM. RESULTS: Patients in the 3DPM group had significantly higher scores in terms of their understanding of the disease (mean 4.85 vs. 3.95, p<0.001) and the treatment plan (mean 4.85 vs. 4.20, p=0.005) and reported higher satisfaction during consultation (5.0 vs. 4.60, p=0.036) than patients in the non-3DPM group. During patient consultation, 3DPMs were most useful in improving doctor-patient communication (mean 4.57, range 4-5). During clipping surgery, the models were most useful in assessing adjacent arteries (mean 4.9, range 4-5); during endovascular coiling, they were especially helpful in microcatheter shaping (mean 4.7, range 4-5). CONCLUSIONS: In general, 3DPMs are beneficial in educating patients and improving the physician's performance in terms of surgical clipping and endovascular coiling of UIAs.

Smartphone Usage and Sleep Quality in Korean Middle School Students During the COVID-19 Pandemic.

OBJECTIVE: This study evaluated the quality of sleep and smartphone addiction in Korean adolescents during the coronavirus disease 19 pandemic and analyzed their effects on perceived quality of life. METHODS: An online survey system provided by the Gwangmyeong City Health Center located in Gyeonggi-do, Korea, was completed by 1,678 local middle school students from April 1 to June 30, 2021. We measured quality of life using the KIDSCREEN-27 health-related quality of life questionnaire, quality of sleep using the Korean version of the Pittsburgh Sleep Quality Index, and smartphone addiction using the Smartphone Addiction Self-Diagnosis Scale. Bed times, wake times, and total sleep hours were also recorded. RESULTS: Sleep quality and quantity were directly associated with smartphone addiction. The results indicated that lower quality of life was associated with lower sleep quality, delayed onset of sleep, shorter total sleep time, and higher smartphone addiction. Female students reported lower quality of life, lower sleep quality, delayed sleep phases, shorter total sleep hours, and higher smartphone addiction scores than did male students. CONCLUSION: Our results suggest that adolescents' quality of life is threatened by poor sleep, which in turn could be due to unhealthy smartphone usage. Girls were more heavily affected by this trend than were boys. Preparations to intervene for further mental health issues that might follow are necessary.

The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea.

BACKGROUND: Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability. METHODS: The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users). RESULTS: A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions. CONCLUSIONS: This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care.

Reply to Çiftci, S.; Aydin, B.K. Comment on "Lee et al. Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening. Diagnostics 2021, 11, 1174".

We thank Dr. Sadettin Ciftci for his comment on the key point issues in measuring the alpha and beta angle with Graf method. We appreciated his feedback [...].

Machine Learning to Identify Psychomotor Behaviors of Delirium for Patients in Long-Term Care Facility.

This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients. A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 clinical variables and the survey items from the short confusion assessment method (S-CAM), and developed a mobile-based application. Multiple machine learning algorithms, including four rule-mining algorithms (C4.5, CBA, MCAR, and LEM2) and four other statistical learning algorithms (LR, ANNs, SVMs with three kernel functions, and random forest), were validated by paired Wilcoxon signed-rank tests on both macro-averaged F1 and weighted average F1-measures during the 10-times stratified 2-fold cross-validation. The LEM2 algorithm achieved the best prediction performance (macro-averaged F1-measure of 49.35%; weighted average F1-measure of 96.55%), correctly identifying adult patients at delirium risk. In the pairwise comparison between predictive powers observed from independent models, the LEM2 model showed a medium or large effect size between 0.4925 and 0.8766 when compared with LR, ANN, SVM with RBF, and MCAR models. We have confirmed that acute consciousness in S-CAM assessment is closely associated with different predictors for screening three psychomotor behaviors of delirium: 1) education level, dementia type or its level, sleep disorder, dehydration, and infection in mixed-type delirium; 2) gender, education level, dementia type, dehydration, bedsores, and foley catheter in hyperactive delirium; and 3) pain, sleep disorder, and haloperidol use in hypoactive delirium.

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance.

The deficiency and excess of vitamin D cause various diseases, necessitating continuous management; but it is not easy to accurately measure the serum vitamin D level in the body using a non-invasive method. The aim of this study is to investigate the correlation between vitamin D levels, body information obtained by an InBody scan, and blood parameters obtained during health checkups, to determine the optimum frequency of vitamin D quantification in the skin and to propose a vitamin D measurement method based on impedance. We assessed body composition, arm impedance, and blood vitamin D concentrations to determine the correlation between each element using multiple machine learning analyses and an algorithm which predicted the concentration of vitamin D in the body using the impedance value developed. Body fat percentage obtained from the InBody device and blood parameters albumin and lactate dehydrogenase correlated with vitamin D level. An impedance measurement frequency of 21.1 Hz was reflected in the blood vitamin D concentration at optimum levels, and a confidence level of about 75% for vitamin D in the body was confirmed. These data demonstrate that the concentration of vitamin D in the body can be predicted using impedance measurement values. This method can be used for predicting and monitoring vitamin D-related diseases and may be incorporated in wearable health measurement devices.

Impact of Vitamin B12 Insufficiency on Sarcopenia in Community-Dwelling Older Korean Adults.

Vitamin B12 (B12) is involved as a cofactor in the synthesis of myelin. A lack of B12 impairs peripheral nerve production, which can contribute to sarcopenia. In this cross-sectional study, we aimed to investigate the relationship between B12 insufficiency and sarcopenia in community-dwelling older Korean adults. A total of 2325 (1112 men; 1213 women) adults aged 70-84 years were recruited. The tools used for sarcopenia were based on the Asian Working Group for Sarcopenia (AWGS) guidelines. Individuals with low appendicular skeletal muscle mass index (ASMI) (<7.0 kg/m2 for men; <5.4 kg/m2 for women) and low hand grip strength (HGS) (<28 kg for men; <18 kg for women) were defined as the sarcopenia group. Among this group, those who showed low physical performance (≤9 points on the Short Physical Performance Battery (SPPB)) were defined as the severe sarcopenia group. B12 concentrations were classified into insufficient (<350 pg/mL) and sufficient (≥350 pg/mL). Univariate and multivariate logistic regression analyses were used to evaluate the relationship between sarcopenia and B12 levels. Low ASMI showed a high incidence in the B12-insufficient group. However, HGS, SPPB, and the severity of sarcopenia showed no correlation with B12. Further, insufficient B12 may affect muscle quantity rather than muscle strength or physical performance.

Steroids plus antiviral agents are more effective than steroids alone in the treatment of severe Bell's palsy patients over 40 years of age.

OBJECTIVE: The effectiveness of the combination of steroids and antiviral agents in the treatment of Bell's palsy remains unclear. This study evaluated the therapeutic effect of combination therapy in severe Bell's palsy patients and assesses specific conditions under which combination therapy is more effective than steroids alone. METHODS: From January 2005 to December 2019, the records of 1710 Bell's palsy patients who visited Kyung Hee University Hospital were reviewed retrospectively. Of these, 335 (19.6%) patients were diagnosed with severe Bell's palsy, with 162 patients treated with steroids alone and 173 patients treated with combinations of steroids and antiviral agents. The outcomes of treatment were assessed using the House-Brackmann (H-B) grade according to age, sex, hypertension, diabetes, and obesity. RESULTS: The favorable recovery rate was significantly higher in severe Bell's palsy patients who were treated with combinations of steroids and antiviral agents than with steroids alone (78.0% vs. 66.7%, p = 0.020). Subgroup analysis showed that combination therapy resulted in significantly higher recovery rates than steroids alone in patients aged ≥40 years (77.5% vs. 64.1%, p = 0.023) and in those without hypertension (75.8% vs. 63.3%, p = 0.044) and diabetes (79.7% vs. 65.5%, p = 0.007). CONCLUSION: Combination therapy with steroids and antiviral agents resulted in significantly higher favorable recovery rates than steroids alone in severe Bell's palsy patients. Combination therapy was particularly more effective than steroids alone in patients aged ≥40 years and in patients without hypertension and diabetes.

Prospective study on resting state functional connectivity in adolescents with major depressive disorder after antidepressant treatment.

Recent advances in functional magnetic resonance imaging (fMRI) have resulted in many studies on resting-state functional connectivity (rsFC) in depressed patients. Previous studies have shown alterations between multiple brain areas, such as the prefrontal cortex, anterior cingulate cortex, and basal ganglia, but there are very few prospective studies with a longitudinal design on adolescent depression patients. We therefore investigated the change in positive rsFC in a homogeneous drug-naïve adolescent group after 12 weeks of antidepressant treatment. Functional neuroimaging data were collected and analyzed from 32 patients and 27 healthy controls. Based on previous literature, the amygdala, anterior cingulate cortex (ACC), insula, hippocampus, and dorsolateral prefrontal cortex (DLPFC) were selected as seed regions. Seed-to-voxel analyses were performed between pre- and post-treatment states as well as between the patients and controls at baseline. The positive rsFC between the right DLPFC and the left putamen/right frontal operculum were shown to be higher in patients than in the controls. The positive rsFC between the left DLPFC and left putamen/left lingual gyrus was also higher in the patients than in the controls. The positive rsFC between the right dorsal ACC and the left precentral gyrus had reduced after the 12-week antidepressant treatment. Regions involved in the frontolimbic circuit showed changes in the positive rsFC in the depressed adolescents as compared to in the healthy controls. There were also significant changes in the positive rsFC after 12-weeks of antidepressant treatment. The involved regions were associated with emotional regulation, cognitive functioning, impulse control, and visual processing.

Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks.

Malignant melanoma accounts for about 1-3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.

Radial profile measurement with an improved 1 kHz Thomson scattering system on Versatile Experiment Spherical Torus.

A Thomson scattering (TS) system has been utilized to measure the electron temperature and density of the core region of Versatile Experiment Spherical Torus (VEST). Recently, the laser injection system is successfully upgraded adopting the burst laser with the repetition rate of 1 kHz and the energy of 2 J. Furthermore, improved collection optics with additional polychromators and a 32-channel fast digitizer are prepared to observe the fast time evolution of radial profiles. This improvement is essential to study fast phenomena such as internal reconnection event (IRE). We increase the TS signal and reduce the stray light by introducing new filters having better optical properties such as high optical density at 1064 nm, transmission, and reflectance. Moreover, the optimum reverse bias voltages are newly set to make the system operational independent of the background radiation. As a result, 1 kHz radial profiles of the core electron temperature and density are measured for the first time, showing characteristics of IREs in VEST.

Prospective study on microstructure in medication-naïve adolescents with first-episode major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is one of the most prevalent mental disorders during adolescence, and early diagnosis and treatment are important. We aimed to characterize the microstructure of the brain in medication-naïve adolescents with first-episode MDD. METHODS: Patients with MDD (N = 31) and healthy controls (N = 27) participated in this study and severity of depressive symptoms, duration of untreated depressive episode, demographic data, and diffuse tensor imaging data were collected. A comparative analysis of patients and healthy controls was performed, and the effect of medication on the brain`s integrity was investigated through comparison before and after 3 months of treatment in the patient group. Tract-based spatial statistics was used for diffusion tensor image analysis. RESULTS: In the patient group, functional anisotropy (FA) values were significantly higher at the genu of the corpus callosum, body of the corpus callosum, and right anterior corona radiata than in healthy controls. After 3 months of treatment, FA values were significantly decreased in the left anterior limb of the internal capsule, left posterior limb of the internal capsule, and left superior longitudinal fasciculus. LIMITATIONS: The sample size is relatively small and 3-month treatment period was relatively short. CONCLUSION: FA values of patients with MDD were increased compared to healthy controls and decreased after treatment. These results suggested that pharmacological treatments in the early stages of MDD might restore the brain`s integrity.

Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening.

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system's cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen's κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.