BACKGROUND: Stabilized hyaluronic acid (S-HA) is used for soft tissue augmentation and can also be used for dermal hydration. According to previous reports, the injection volume of each shot is more than 0.02 cc for dermal hydration. Clinically, this is an excessive amount for this purpose, so I will introduce a modified method using particle type S-HA injection for dermal hydration. METHODS: One hundred fifty patients who had 1,000 injections of 1 cc S-HA by injector were analyzed. The patients were examined after 1, 2, 6, 12, and 24 months. Changes in skin texture were determined with regard to skin roughness and morphology, dermal hydration by electric resistance, dermal thickness by ultrasonic imaging, and biopsy. An intra-individual study was also performed to compare changes in skin texture between dermal and subdermal injections. RESULTS: Skin roughness significantly improved after this procedure. Electric resistance also decreased. The dermis of the face and hand were thickened about 4% after dermal injection. The injection depth was confirmed by biopsy. There were no significant changes in dermal thickness after subdermal injection with S-HA, which merely replaced fluid volume. Injections into the dermis changed both skin texture and thickness. CONCLUSIONS: Skin texture was improved after 1000 dermal injections (0.001 cc each) of particle type S-HA. Deeper injections only replaced the fluid volume and could not improve skin texture. For this purpose, the use of an injector or mesogun is recommended.
Biopsy ; Dermis* ; Electric Impedance ; Hand ; Humans ; Hyaluronic Acid* ; Hypodermoclysis ; Injections, Intradermal ; Microinjections ; Skin* ; Ultrasonography

Objective: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods: Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results: Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). Conclusion This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.

OBJECTIVES: This study involved a survey of the facility investment experiences, which was designed to recognize the importance of health and safety problems, and industrial accident prevention. Ultimately, we hope that small scale industries will create effective industrial accident prevention programs and facility investments. METHODS: An individual survey of businesses' present physical conditions, recognition of the importance of the health and safety problems, and facility investment experiences for preventing industrial accidents was conducted. The survey involved 1,145 business operators or management workers in small business places with fewer than 50 workers in six industrial complexes. RESULTS: Regarding the importance of occupational health and safety problems (OHS), 54.1% said it was "very important". Received technical and financial support, and industrial accidents that occurred during the past three years were recognized as highly important for OHS. In an investigation regarding facility investment experiences for industrial accident prevention, the largest factors were business size, greater numbers of industrial accidents, greater technical and financial support received, and greater recognition of the importance of the OHS. The related variables that decided facility investment for industry accident prevention in a logistic regression analysis were the experiences of business facilities where industrial accidents occurred during the past three years, received technical and financial support, and recognition of the OHS. Those considered very important were shown to be highly significant. CONCLUSIONS: Recognition of health and safety issues was higher when small businesses had experienced industrial accidents or received financial support. The investment in industrial accidents was greater when health and safety issues were recognized as important. Therefore, the goal of small business health and safety projects is to prioritize health and safety issues in terms of business management and recognition of importance. Therefore, currently various support projects are being conducted. However, there are issues regarding the limitations of the target businesses and inadequacies in maintenance and follow-up. Overall, it is necessary to provide various incentives for onsite participation that can lead to increased recognition of health and safety issues and practical investments, while perfecting maintenance and follow up measures by thoroughly revising existing operating systems.
Accident Prevention ; Accidents, Occupational ; Commerce ; Financial Support ; Hope ; Investments* ; Logistic Models ; Motivation ; Occupational Health ; Small Business*