Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach.

BACKGROUND: Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility. METHODS: We introduce a hybrid technique that integrates optical methods with deep learning to produce skin tone and pigmented region-modified images with numerical control. The pigment discrimination model produces melanin, hemoglobin, and shading maps from skin images. The outputs are reconstructed into skin images using a forward problem-solving approach, with model training aimed at minimizing the discrepancy between the reconstructed and input images. By adjusting the melanin and hemoglobin maps, we create pigment-modified images, allowing precise control over changes in melanin and hemoglobin levels. Changes in pigmentation are quantified using the individual typology angle (ITA) for skin tone and melanin and erythema indices for pigmented regions, validating the intended modifications. RESULTS: The pigment discrimination model achieved correlation coefficients with clinical equipment of 0.915 for melanin and 0.931 for hemoglobin. The alterations in the melanin and hemoglobin maps exhibit a proportional correlation with the ITA and pigment indices in both quantitative and qualitative assessments. Additionally, regions overlaying melanin and hemoglobin are demonstrated to verify independent adjustments. CONCLUSION: The proposed method offers an approach to generate modified images of skin tone and pigmented regions. Potential applications include visualizing alterations for clinical assessments, simulating the effects of skincare products, and generating datasets for deep learning.

Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering.

Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.

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.

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.

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.

Molecular Biomarkers for Pediatric Depressive Disorders: A Narrative Review.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments were conducted according to clinical symptoms. Objective and biological validation is lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers including brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interferon-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

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.

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.

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.

Wholly π-conjugated low-molecular-weight organogelator that displays triple-channel responses to fluoride ions.

A novel salicylidene aniline-based wholly π-conjugated molecule that could be self-assembled into an organogel was synthesized. The rigid organogel collapsed into fluid solutions with significant changes in UV-vis absorption and fluorescence colors in response to fluoride ions. It was found that the hydroxyl group in the salicylidene aniline moiety played a key role not only in the gelation but also in fluoride ion responses.

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste.

Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne training data is not easy. Therefore, this study proposes a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 in Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrated an improvement in performance, offering a novel direction for future acne analysis.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. METHODS: This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. RESULTS: Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. CONCLUSION: The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

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.

Identifying Subgroups of Suicidality Among Adolescents and Influencing Factors Using Latent Class Analysis.

OBJECTIVE: We aimed to classify subgroups of suicidality among adolescents and identify the influencing factors of the classification of these latent classes. METHODS: Suicidal thought, plans, and attempts as well as the feelings of sadness/hopelessness and loneliness were utilized as indicators to derive the suicidality classes. Additionally, health behaviors, such as dietary habits, physical activity, experiences of violence victimization, sexual activity, and deviant behavior, along with demographic factors, such as sex, school year, grades, and household income, were considered as influencing factors. The analysis utilized data from the 18th Youth Health Behavior Survey (2022) conducted by the Korea Disease Control and Prevention Agency, involving 51,850 middle and high school students. RESULTS: The findings revealed three latent classes of suicidality among adolescents: "active suicidality," "passive suicidality," and "non-suicidality." The influencing factor analysis indicated that all factors, with the exception of high-intensity physical activities, significantly influenced the classification of latent classes of suicidality. Notably, walking exercise and the frequency of exercise during physical education class were found to be factors that differentiated between active and passive suicidality within the suicidality classes. CONCLUSION: This study employed nationwide data to identify the exhibited suicidality classes among adolescents and tested the influencing factors necessary for predicting such classes. The study's findings offer valuable insights for policy development in suicide prevention and suggest the need for developing customized interventions tailored to each identified class.

Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions.

BACKGROUND: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. AIMS: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. METHODS: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. RESULTS: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. CONCLUSION: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations.

Study on Awareness of Suicide and Suicide Prevention Among Community Youth.

Objectives: South Korea has the highest suicide rate among Organisation for Economic Co-operation and Development countries; there is an increasing trend in suicide attempts among middle and high school students. Various factors contribute to the risk of suicide among adolescents, and the perception of suicide prevention has emerged as a significant factor. This study aimed to investigate the association between emotional and behavioral difficulties among middle and high school students and their perceptions of suicide prevention and to explore differences in suicide perception according to age. Methods: A survey was conducted among community middle and high school students, including 530 participants, between 2020 and 2021. Emotional and behavioral difficulties were assessed using the Strengths and Difficulties Questionnaire-Korean version, and participants were asked to complete a questionnaire on the importance and possibility of suicide prevention. A correlation test and analysis of variance were used to examine the relationships between the variables, and suicide awareness was compared according to age. Results: The participants who displayed higher strength or lower difficulty were more likely to respond positively to suicide prevention measures. They also exhibited high strength and low difficulty levels, thus agreeing with the importance of suicide prevention. Regarding age-related perceptions of suicide, adults aged 20-29 years reported the lowest probability of suicide prevention. Conclusion: Suicide perceptions influence the incidence of suicide. Therefore, active societal engagement through suicide prevention campaigns and related education is essential to improve such perceptions. Continuous attention and support are required to address this issue.

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.