Background: Oxidative stress causes fatal damage to follicular keratinocytes (FKCs) and is a common pathophysiology of many hair disorders. Objective: This study investigated the protective effects of Red ginseng extract (RGE) and its main ginsenosides against oxidative hair damage using an in vitro organ model of human hair follicles. Methods: We examined whether RGE and its constituent ginsenosides could prevent oxidative damage induced by H 2 O 2 in FKCs by suppressing apoptosis and promoting hair growth. Results: RGE and its main ginsenoside, G-Rb1, significantly inhibited reactive oxygen species production and apoptosis in FKCs. Furthermore, they effectively alleviated the inhibition of hair growth induced by oxidative damage and inhibited the transition of hair from the anagen to the telogen stage. The hair cycle and apoptosis were associated with the modulation of p53 and Bax/Bcl2 signaling. Conclusion RGE and G-Rb1 can effectively mitigate the oxidative damage caused by FKCs, thereby affecting hair growth and hair cycles.

Background: Diabetes mellitus (DM) is one of the most common endocrine diseases, and the relationship between diabetes and skin diseases is well-known and its mechanisms have been studied. Objective: This study aimed to examine the association between DM and skin diseases. Methods: We used the medical record database provided by the National Health Insurance Service. We constructed a cohort with 1,197,225 patients diagnosed with type 2 DM from 2011 to 2015. We analyzed 3,992,368 medical records of patients with DM who visited the hospital from January 1, 2009 to December 13, 2018 with skin and subcutaneous tissue diseases (ICD-10 code, L00-L99). After that, we compared the changes in skin and subcutaneous tissue diseases before and after the diagnosis of type 2 DM. Results: The number of patients with skin diseases, after the diagnosis of type 2 DM was 1,629,756 (50.6%). The frequency of skin diseases increased after the diagnosis of type 2 DM compared to that before the diagnosis. Particularly, infectious diseases (＋29.03%,p＜0.001), vesiculobullous diseases (＋33.13%, p＜0.001) and ulcerrelated diseases (pressure sores [＋530.18%], and lower extremity ulcers [＋321.56%], p＜0.001) increased sharply whereas dermatitis and eczematous diseases (−9.96%, p＜0.001) and urticaria (−12.99%, p＜0.001) decreased. Conclusion Skin diseases increased following the diagnosis of diabetes, and there were changes in the pattern of skin diseases before and after the diagnosis of diabetes.

Background: The morphology of hair regrowth in alopecia areata (AA) patches could be classified into four types, namely diffuse, irregular, marginal, and targetoid patterns, according to the DIMT classification. However, factors affecting hair regrowth patterns have not been investigated. Objective: We investigated whether the DIMT-classified hair regrowth patterns of AA patches are associated with treatment modality and patch size. Methods: We conducted a retrospective, cross-sectional study of 152 AA patches with hair regrowth. Results: The associations between the diffuse pattern and patch size >2 cm (p=0.006;odds ratio [OR]: 0.36, 95% confidence interval [CI]: 0.17~0.74), between the irregular pattern and triamcinolone acetonide intralesional injection (p<0.001; OR: 274.87, 95% CI:25.75~2,933.56), between the marginal pattern and systemic and topical corticosteroid (p=0.018; OR: 4.89, 95% CI: 1.31~18.27), and between the targetoid pattern and patch size >2 cm (p=0.028; OR: 2.50, 95% CI: 1.10~5.68) were statistically significant. Conclusion Treatment modalities and patch size are the factors affecting hair regrowth patterns in AA patches.

Purpose: Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. Materials and Methods: The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. Results: A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). Conclusion This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

no abstract available