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.

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 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.

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.

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.

Structural insights into CYP107G1 from rapamycin-producing Streptomyces rapamycinicus.

Rapamycin is a clinically important macrolide agent with immunosuppressant and antiproliferative properties, produced by the actinobacterium, Streptomyces rapamycinicus. Two cytochrome P450 enzymes are involved in the biosynthesis of rapamycin. CYP107G1 and CYP122A2 catalyze the oxidation reactions of C27 and C9 of pre-rapamycin, respectively. To understand the structural and biochemical features of P450 enzymes in rapamycin biosynthesis, the CYP107G1 and CYP122A2 genes were cloned, their recombinant proteins were expressed in Escherichia coli, and the purified enzymes were characterized. Both enzymes displayed low spin states in the absolute spectra of ferric forms, and the titrations with rapamycin induced type I spectral changes with Kd values of 4.4 ± 0.4 and 3.0 ± 0.3 µM for CYP107G1 and CYP122A2, respectively. The X-ray crystal structures of CYP107G1 and its co-crystal complex with everolimus, a clinical rapamycin derivative, were determined at resolutions of 2.9 and 3.0 Å, respectively. The overall structure of CYP107G1 adopts the canonical scaffold of cytochrome P450 and possesses large substrate pocket. The distal face of the heme group is exposed to solvents to accommodate macrolide access. When the structure of the everolimus-bound CYP107G1 complex (CYP107G1-Eve) was compared to that of the ligand-free CYP107G1 form, no significant conformational change was observed. Hence, CYP107G1 has a relatively rigid structure with versatile loops to accommodate a bulky substrate. The everolimus molecule is bound to the substrate-binding pocket in the shape of a squeezed donut, and its elongated structure is bound perpendicular to a planar heme plane and I-helix.

A High Neutrophil-to-Lymphocyte Ratio Is Associated with Recovery from Ramsay Hunt Syndrome.

BACKGROUND: Prediction of the severity and outcomes of Ramsay Hunt syndrome (RHS) is difficult. OBJECTIVES: The aim of this study was to evaluate the predictive power of the neutrophil-to-lymphocyte ratio (NLR) for the severity and outcomes of RHS. MATERIAL AND METHODS: From 2007 to 2017, a retrospective study of 102 hospitalized patients with RHS was conducted. The degree of paralysis was assessed by determining the House-Brackmann (H-B) grade. Obesity, hypertension, and diabetes mellitus were assessed, and the blood NLR and platelet-to-lymphocyte ratio were determined. The patients received steroids and antivirals, and were followed in the outpatient department at 1, 4, 12, and 24 weeks. RESULTS: The H-B grade of the high-NLR group was significantly higher than that of the normal-NLR group (p = 0.039), and the probability of complete recovery was significantly lower in the high-NLR group (p = 0.048). CONCLUSIONS: Patients with RHS who have an elevated NLR have poor outcomes in terms of the H-B grade. Therefore, the NLR may be useful for evaluating the prognosis of patients with RHS.

Development of the Suicide Risk Scale for Medical Inpatients.

BACKGROUND: Inpatient suicide is one of the major issues related with hospital patient safety. Although there are many studies addressing suicide in the psychiatric unit, little is known about suicide in a medical setting. This study included the development and validation of a screening tool for the assessment of suicide risk, specialized for medical inpatients. METHODS: The preliminary questionnaire was based on review of previous suicide ideation scales, mood scales, and clinical experiences of psychiatrists and clinical psychologists in Korea. Initially, the questionnaire consisted of 12 questions. One hundred adult medical inpatients were asked to perform the questionnaire. Explorative factor analysis was used to examine construct validity. Concurrent validity was evaluated by comparison with the Korean versions of the Beck Scale for Suicide Ideation (BSI), the Beck Hopelessness Scale (BHS), and the Hospital Anxiety and Depression Scale (HADS). RESULTS: Five questions were removed from the preliminary questionnaire by explorative factor analysis and seven questions remained to comprise the Suicide Risk Scale for Medical inpatients (SRSM). Explorative factor analysis showed that the SRSM is composed of a single factor. SRSM was highly reliable in terms of internal consistency (Cronbach's alpha 0.91). Concurrent validity with the BSI, BHS, and HADS was statistically significant. The proposed cut-off score of the SRSM was five. CONCLUSION: In conclusion, the SRSM is a valid and reliable measure for screening suicide risks in medical inpatients.

The Melatonin Signaling Pathway in a Long-Term Memory In Vitro Study.

The activation of cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) via phosphorylation in the hippocampus is an important signaling mechanism for enhancing memory processing. Although melatonin is known to increase CREB expression in various animal models, the signaling mechanism between melatonin and CREB has been unknown in vitro. Thus, we confirmed the signaling pathway between the melatonin receptor 1 (MT1) and CREB using melatonin in HT-22 cells. Melatonin increased MT1 and gradually induced signals associated with long-term memory processing through phosphorylation of Raf, ERK, p90RSK, CREB, and BDNF expression. We also confirmed that the calcium, JNK, and AKT pathways were not involved in this signaling pathway by melatonin in HT-22 cells. Furthermore, we investigated whether melatonin regulated the expressions of CREB-BDNF associated with long-term memory processing in aged HT-22 cells. In conclusion, melatonin mediated the MT1-ERK-p90RSK-CREB-BDNF signaling pathway in the in vitro long-term memory processing model and increased the levels of p-CREB and BDNF expression in melatonin-treated cells compared to untreated HT-22 cells in the cellular aged state. Therefore, this paper suggests that melatonin induces CREB signaling pathways associated with long-term memory processing in vitro.

Induction of Inflammation In Vivo by Electrocardiogram Sensor Operation Using Wireless Power Transmission.

Prolonged monitoring by cardiac electrocardiogram (ECG) sensors is useful for patients with emergency heart conditions. However, implant monitoring systems are limited by lack of tissue biocompatibility. Here, we developed an implantable ECG sensor for real-time monitoring of ventricular fibrillation and evaluated its biocompatibility using an animal model. The implantable sensor comprised transplant sensors with two electrodes, a wireless power transmission system, and a monitoring system. The sensor was inserted into the subcutaneous tissue of the abdominal area and operated for 1 h/day for 5 days using a wireless power system. Importantly, the sensor was encapsulated by subcutaneous tissue and induced angiogenesis, inflammation, and phagocytosis. In addition, we observed that the levels of inflammation-related markers increased with wireless-powered transmission via the ECG sensor; in particular, levels of the Th-1 cytokine interleukin-12 were significantly increased. The results showed that induced tissue damage was associated with the use of wireless-powered sensors. We also investigated research strategies for the prevention of adverse effects caused by lack of tissue biocompatibility of a wireless-powered ECG monitoring system and provided information on the clinical applications of inflammatory reactions in implant treatment using the wireless-powered transmission system.

Delayed facial nerve decompression for Bell's palsy.

Incomplete recovery of facial motor function continues to be long-term sequelae in some patients with Bell's palsy. The purpose of this study was to investigate the efficacy of transmastoid facial nerve decompression after steroid and antiviral treatment in patients with late stage Bell's palsy. Twelve patients underwent surgical decompression for Bell's palsy 21-70 days after onset, whereas 22 patients were followed up after steroid and antiviral therapy without decompression. Surgical criteria included greater than 90 % degeneration on electroneuronography and no voluntary electromyography potentials. This study was a retrospective study of electrodiagnostic data and medical chart review between 2006 and 2013. Recovery from facial palsy was assessed using the House-Brackmann grading system. Final recovery rate did not differ significantly in the two groups; however, all patients in the decompression group recovered to at least House-Brackmann grade III at final follow-up. Although postoperative hearing threshold was increased in both groups, there was no significant between group difference in hearing threshold. Transmastoid decompression of the facial nerve in patients with severe late stage Bell's palsy at risk for a poor facial nerve outcome reduced severe complications of facial palsy with minimal morbidity.

Bio-optics based sensation imaging for breast tumor detection using tissue characterization.

The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer.

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.

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.

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.

Optical-based artificial palpation sensors for lesion characterization.

Palpation techniques are widely used in medical procedures to detect the presence of lumps or tumors in the soft breast tissues. Since these procedures are very subjective and depend on the skills of the physician, it is imperative to perform detailed a scientific study in order to develop more efficient medical sensors to measure and generate palpation parameters. In this research, we propose an optical-based, artificial palpation sensor for lesion characterization. This has been developed using a multilayer polydimethylsiloxane optical waveguide. Light was generated at the critical angle to reflect totally within the flexible and transparent waveguide. When a waveguide was compressed by an external force, its contact area would deform and cause the light to scatter. The scattered light was captured by a high-resolution camera and saved as an image format. To test the performance of the proposed system, we used a realistic tissue phantom with embedded hard inclusions. The experimental results show that the proposed sensor can detect inclusions and provide the relative value of size, depth, and Young's modulus of an inclusion.

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.

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.

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.

Cortical changes after mental imagery training combined with electromyography-triggered electrical stimulation in patients with chronic stroke.

BACKGROUND AND PURPOSE: Paresis of the upper extremity after stroke is not effectively solved by existing therapies. We investigated whether mental imagery training combined with electromyogram-triggered electric stimulation improved motor function of the paretic upper extremity in patients with chronic stroke and induced cortical changes. METHODS: Fourteen subjects with chronic stroke (≥12 months) were randomly allocated to receive mental imagery training combined with electromyogram-triggered electric stimulation (n=7) or generalized functional electric stimulation (n=7) on the forearm extensor muscles of the paretic extremity in 2 20-minute daily sessions 5 days a week for 4 weeks. The upper extremity component of the Fugl-Meyer Motor Assessment, the Motor Activity Log, the modified Barthel Index, and (18)F-fluorodeoxyglucose brain positron emission tomography were measured before and after the intervention. RESULTS: The group receiving mental imagery training combined with electromyogram-triggered electric stimulation exhibited significant improvements in the upper extremity component of the Fugl-Meyer Motor Assessment after intervention (median, 7; interquartile range, 5-8; P<0.05), but the group receiving functional electric stimulation did not (median, 0; interquartile range, 0-3). Differences in score changes between the 2 groups were significant. The mental imagery training combined with electromyogram-triggered electric stimulation group showed significantly increased metabolism in the contralesional supplementary motor, precentral, and postcentral gyri (P(uncorrected)<0.001) after the intervention, but the functional electric stimulation group showed no significant differences. CONCLUSIONS: Mental imagery training combined with electromyogram-triggered electric stimulation improved motor function of the paretic extremity in patients with chronic stroke. The intervention increased metabolism in the contralesional motor-sensory cortex. Clinical Trial Registration- URL: https://e-irb.khmccri.or.kr/eirb/receipt/index.html?code=02&status=5. Unique identifier: KHUHMDIRB 1008-02.