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.

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.

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.

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.

Tailoring tokamak error fields to control plasma instabilities and transport.

A tokamak relies on the axisymmetric magnetic fields to confine fusion plasmas and aims to deliver sustainable and clean energy. However, misalignments arise inevitably in the tokamak construction, leading to small asymmetries in the magnetic field known as error fields (EFs). The EFs have been a major concern in the tokamak approaches because small EFs, even less than 0.1%, can drive a plasma disruption. Meanwhile, the EFs in the tokamak can be favorably used for controlling plasma instabilities, such as edge-localized modes (ELMs). Here we show an optimization that tailors the EFs to maintain an edge 3D response for ELM control with a minimized core 3D response to avoid plasma disruption and unnecessary confinement degradation. We design and demonstrate such an edge-localized 3D response in the KSTAR facility, benefiting from its unique flexibility to change many degrees of freedom in the 3D coil space for the various fusion plasma regimes. This favorable control of the tokamak EF represents a notable advance for designing intrinsically 3D tokamaks to optimize stability and confinement for next-step fusion reactors.

3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers.

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.