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Objective assessment of atopic dermatitis (AD) is essential for choosing proper management strategies. This study investigated the performance of convolutional neural networks (CNN) models in grading the severity of AD. Five board-certified dermatologists independently evaluated the severity of 9,192 AD images. The severity of AD was evaluated based on an Investigator's Global Assessment (IGA) and six signs of AD. For CNN training, we applied three distinct approaches: (1) ensemble vs. integration (2) hard-label vs. soft-label and (3) train-set pruning. For the IGA prediction, the two best models were chosen based on the macro-averaged AUROC and F-1 score. The ensemble-soft-label-pruning model was chosen based on AUROC 0.943, 0.927 for the internal and external validation set respectively, and integration-soft-label-whole dataset model was chosen based on the F1-score 0.750, 0.721 for the internal and external validation set respectively. CNN models trained by multi-evaluator dataset outperformed the models by an individual evaluator dataset, and they performed better to the dataset in which the assessment of dermatologists was concordant. In conclusion, CNN models for AD could be improved by labeled dataset from multiple evaluators, merging methods with soft-label and train-set pruning.

Knowledge of the topographic thickness of the skin and soft tissues is necessary when performing a high-intensity focused ultrasound (HIFU) procedure. Thermal tissue injury to the superficial musculoaponeurotic system (SMAS) or deeper can injure the facial nerve and its branches.To demonstrate the topographic thickness of the lower facial skin, superficial fat, and SMAS.The ultrasound data of 200 healthy patients who underwent lower facial rejuvenation were retrospectively reviewed.The mean age was 41.1 ± 13.7 years (range, 19-76 years). The jowl had thinner skin, thicker superficial fat, and deeper superficial and deep margins of the SMAS than the preauricle or lower cheek. The thickness of the superficial fat decreased with age, especially on the preauricle, lower cheek, and jowl. Women had thicker superficial fat than men on the preauricle and lower cheek. The superficial and deep margins of the SMAS were located more superficially in old and male patients with a slim facial figure than in young and female patients with a chubby facial figure.The present findings provide anatomical information regarding the superficial fat and SMAS, which is useful in determining the focal penetration depth of HIFU treatment for lower face rejuvenation.

Background Melasma shows characteristic histological features of photoaged skin. Objective We evaluated the effect of dermal rejuvenation using a temperature-controlled continuous non-invasive radiofrequency (RF) device on melasma. Methods Continuous skin heating at the temperature of 43°C for 20 minutes was performed in ten subjects with melasma who underwent 3 tri-weekly RF sessions. Pigmentation was evaluated with Mexameter® and investigator's global assessment (IGA). Immunohistochemical staining and image analysis was performed to evaluate biopsies from melasma skin before and after the treatment. Results The lesional melanin index was decreased by 13.7% at week 9. IGA score was improved from 3.50 at baseline to 2.95 at week 9. No significant adverse event was reported. Histologic analysis revealed reduced melanin and increased collagen density and thickness. The expression of procollagen-1 and type IV collagen was increased after the treatment. The number of p16INK4A-positive senescent fibroblasts was reduced after the treatment, while the expression of heat shock protein 70 and 90 was increased. Stromal derived factor-1, a senescence-associated anti-melanogenic factor secreted from the fibroblasts, was up-regulated after the treatment, while the level of c-kit was not changed. Conclusion Thermal skin stimulation by the temperature-controlled continuous RF device improved melasma through dermal rejuvenation.

Dupilumab was the first biological drug to be approved for adult patients with moderate-to-severe atopic dermatitis (AD), and its use is growing exponentially worldwide. Though its therapeutic efficacy and favorable safety profile have been demonstrated, data on real-world long-term experience with the drug are only beginning to accumulate. Herein, we present a retrospective analysis of Korean patients with moderate-to-severe AD who were treated with dupilumab. We observed excellent overall treatment efficacy with the mean Eczema Area and Severity Index (EASI) score decreased from 28.2 to 3.2 at week 52. Notably, the therapeutic effect was maintained despite the considerable number of patients requiring an increase in treatment intervals due to the financial burden in a real clinical setting. In contrast to the previous reports, paradoxical head and neck erythema/dermatitis was rare in our study group, and pre-existing dermatitis in the very region, as well as in the hands, responded well to dupilumab treatment. Additionally, we were able to discontinue dupilumab treatment for two patients who achieved complete clearance of AD symptoms (EASI and Investigator's Global Assessment [IGA] scores of 0) for more than three months. There have been no flare-up events of AD in these patients; with topical corticosteroids alone, one of them has been completely disease-free for 43 weeks and the other has been maintaining an IGA score of 1 for 66 weeks. Furthermore, conjunctivitis was again confirmed to be the most frequent side effect associated with dupilumab, and it generally responded well to conventional conjunctivitis treatment.

Background Recently, pulsed dye laser (PDL) combined with triamcinolone intralesional injection (TAILI) has been introduced for surgical scar prevention. However, little is known about this procedure's effectiveness in preventing hypertrophic scar following surgical scar removal. Objectives This study aimed to evaluate the outcome of early intervention using PDL combined with TAILI after surgical removal of hypertrophic cesarean section (CS) scars. Methods The medical records of 35 patients who underwent early intervention using PDL and TAILI after removal of hypertrophic CS scars were retrospectively reviewed. The scars' average Vancouver Scar Scale (VSS) scores before scar removal and three months after the final treatment were compared. Results The patients received 4.23 treatments on average and were followed up for a mean period of 7.74 months. The mean final VSS was 3.11 ± 1.52 and was significantly lower than that of the previous VSS (9.29 ± 1.74, P = 0.000). VSS of the previous CS scar, and the presence or absence of keloid formation in other areas, were associated with treatment outcome (P = 0.003 and 0.008, respectively). Conclusions Early intervention using PDL combined with TAILI could prevent the recurrence or progression of hypertrophic CS scarring after surgical scar removal.

The mechanism by which stromal cells fill voids in injured tissue remains a fundamental question in regenerative medicine. While it is well-established that fibroblasts fill voids by depositing extracellular matrix (ECM) proteins as they migrate toward the wound site, little is known about their ability to adopt an epithelial-like purse-string behavior. To investigate fibroblast behavior during gap closure, we created an artificial wound with a large void space. We discovered that fibroblasts could form a free-standing bridge over deep microvoids, closing the void via purse-string contraction, a mechanism previously thought to be unique to epithelial wound closure. The findings also revealed that myosin II mediated contractility and intercellular adherent junctions were required for the closure of the fibroblast gap in our fabricated three-dimensional artificial wound. To fulfill their repair function under the specific microenvironmental conditions of wounds, fibroblasts appeared to acquire the structural features of epithelial cells, namely, contractile actin bundles that span over multiple cells along the boundary. These findings shed light on a novel mechanism by which stromal cells bridge the 3D gap during physiological processes such as morphogenesis and wound healing.

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P0.0001) and 1.1% (P0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P = 0.045) and 10.1% (P = 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

Since the outbreak of COVID-19 in December 2019, more than 4 million people have died worldwide. The global pandemic of COVID-19 has prompted each country to issue an emergency use authorization for COVID-19 vaccines. In South Korea, the Ministry of Food and Drug Safety authorized the administration of four vaccines; Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), Oxford-AstraZeneca (ChAdOx1), and Johnson & Johnson's Janssen (JNJ-78436735). Although these vaccines were presumed to be safe, several nonspecific skin eruptions have been reported after the health system initiated public COVID-19 vaccination1 . Here, we report a patient who developed de novo erythema multiforme (EM) following COVID-19 vaccination with BNT162b2.

Basal cell carcinoma (BCC) affecting different sites has been reported to have different clinicopathological features. In previous studies, the scalp was commonly classified to the head and neck region. However, the scalp has distinct characteristics from those of other parts of the skin. We retrospectively reviewed the medical records of patients who underwent surgical treatment for BCC. A total of 734 lesions were examined, and 13.2% originated from the scalp. The nodular type was the most common histologic subtype; however, the proportion of the superficial type was significantly higher than that of facial BCC (p

Treatment of periorbital wrinkles is a challenging task because of its anatomical significance and delicate nature.The aim of this study was to evaluate the efficacy and safety of a microneedle monopolar radiofrequency (RF) device for treating periorbital wrinkles.This retrospective study involved 21 patients who were treated for periorbital wrinkles using a RF device. The type of microneedle used, clinical photography, VISIA wrinkle score, and adverse events were collected from the medical chart. Two independent dermatologists assessed the photographs.There were 18 female and 3 male patients with a mean age of 50.24 years. The severity of wrinkle was improved according to photographic assessment and VISIA wrinkle score at 6-month post-treatment. When 21 sites treated with a long microneedle (LMN, 1.5 mm) and the other 21 sites treated with a short microneedle (SMN, 0.8 mm) were compared, significant improvement in the VISIA wrinkle score was observed only in those treated with the LMN. No serious adverse events, including scarring and postinflammatory hyperpigmentation, occurred.We demonstrated the efficacy and safety of the microneedle monopolar RF device in treating periorbital wrinkles. The LMN was superior to the SMN in reducing VISIA wrinkle score.

Background Elliptical excision (EE) has been the standard surgical technique for the removal of epidermal cysts. However, it produces to create a long, linear wound causing cosmetic concerns. Objective The purpose of the study was to evaluate the cosmetic outcomes and postoperative complications of rectangular lid excision (LE) compared with EE in epidermal cyst removals. Methods Medical records of patients who received surgical excision for epidermal cysts were retrospectively reviewed. Three dermatologists evaluated the postoperative photographs using 4-scale investigator's global assessment. Results Of the 123 lesions, EE and LE were performed in 58 and 65 lesions, respectively. Although the mean diameter of cysts was significantly greater in the LE group than in the EE group, the number of postoperative complications was not different between the 2 groups. A photographic assessment was performed in 11 lesions in the LE group and 11 lesions in the EE group. Although the mean diameter of cysts was greater in the LE group than in the EE group, the cosmetic outcome was significantly better in the LE group. Conclusion The LE technique was shown to have a superior cosmetic outcome than the EE technique. Hence, it is a viable alternative for the removal of epidermal cysts.

Background: Several local flaps have been introduced for the reconstruction of small-to-medium-sized nasal supratip defects.Objective: To evaluate the outcome of reconstructing supratip defects usi...

BACKGROUND AND OBJECTIVES Conventional treatment options for eyelid fat bulging are generally limited to surgical approaches. However, many attempts have been made recently to manage this disfigurement using non-surgical interventions. The purpose of this study was to evaluate the efficacy and safety of a micro-insulated needle radiofrequency system for the treatment of lower eyelid fat bulging. METHODS This is a single center pre-post comparative study. Twenty-two subjects with lower eyelid fat bulging were treated twice using the needle radiofrequency system, at an interval of four weeks. Two types of partially insulated needles with different lengths were used in each session. A three-dimensional photogrammetry system was used to objectively measure changes in the extent of the fat bulge. The investigator's global assessment (IGA) of the severity of fat bulging was also evaluated. RESULTS The average extent of fat bulging was decreased significantly after twelve weeks, and was maintained until 24 weeks. The IGA score was significantly decreased after four weeks and further decreased after twelve weeks, and then maintained until 24 weeks. There were no side effects, except for lower eyelid swelling and bruising that lasted for about a week. CONCLUSION The micro-insulated needle radiofrequency system can be a beneficial and well-tolerated treatment for lower eyelid fat bulging.

The aim of this study was to evaluate changes in the skin surface microbiome in patients with atopic dermatitis during treatment. The effect of narrowband ultraviolet B phototherapy was also studied to determine the influence of exposure to ultraviolet. A total of 18 patients with atopic dermatitis were included in the study. Patients were divided into 2 groups based on treatment: 1 group treated with narrowband ultraviolet B phototherapy and topical corticosteroid, and the other group treated with topical corticosteroid only. Skin swabs and high-throughput sequencing of 16S ribosomal RNA bacterial genes were performed at 3 time-points. The microbial diversity of lesional skin increased greatly after treatment. The proportion of Staphylococcus aureus showed a significant positive correlation with eczema severity. In conclusion, a drastic increase in microbial diversity and decrease in S. aureus proportion were observed with eczema treatment. Narrowband ultraviolet B treatment did not exert additive effects on eczema improvement; however, it appeared to reduce the recurrence of eczema.

Background: Pentasodium tetracarboxymethyl palmitoyl dipeptide-12 (PTPD-12), a newly-synthesized peptide, enhances the autophagy activity, ultimately managing inflammation. Objective: To determine the effect of a new moisturizer containing PTPD-12 as the treatment of mild-to-moderate atopic dermatitis (AD). Methods: In this double-blind, randomized, placebo-controlled trial, 43 patients with mild-to-moderate AD were randomly assigned to either the PTPD-12 or control groups. Evaluations were performed at baseline, week 2, and week 4, including SCORing Atopic Dermatitis (SCORAD) index score, corneometry, trans-epidermal water loss (TEWL), visual analog scale (VAS) for pruritus, 7-point investigator's global assessment (IGA), and collection of adverse events. Results: The PTPD-12 group showed significant improvement with respect to SCORAD score, skin hydration, TEWL, and pruritus at weeks 2 and 4 when compared with baseline. Although the control group showed significant improvement regarding the SCORAD score and skin hydration, no significant change in TEWL or pruritus was demonstrated throughout the study. The mean changes in the SCORAD index score, skin hydration, TEWL, pruritus, and number of patients with improvement in IGA were not statistically different between the two groups. Conclusion: The moisturizer with autophagy-stimulating property provides a good therapeutic option to mild-to-moderate atopic dermatitis by contributing to skin barrier restoration and control of inflammation.

Background Lichenoid drug eruption is rare and can mimic idiopathic lichen planus and other dermatoses. Clonazepam, a commonly used drug for the treatment of anxiety-related disorders and seizures, is known to be an unlikely cause of cutaneous adverse effects. Only one case report of LDE due to clonazepam has been reported. Case presentation A 81-year-old male patient with Alzheimer’s disease developed a lichenoid eruption after taking clonazepam. He developed a violaceous scaly patch on his lower extremities, from both buttocks to the feet. The cutaneous eruption resolved 2 months after cessation of clonazepam and with initiation of corticosteroid therapy. Conclusion A skin eruption that develops after clonazepam administration can be a lichenoid drug eruption, which is less likely to resolve spontaneously and requires discontinuation of clonazepam administration.

BACKGROUND Although deep neural networks have shown promising results in diagnosing skin cancer, a prospective evaluation in a real-world setting could confirm these results. OBJECTIVE The aim of this study was to evaluate whether an algorithm (http://b2019.modelderm.com) could improve the accuracy of nondermatologists in diagnosing skin neoplasms. METHODS A total of 285 cases (random series) with skin neoplasms suspected by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with capturing photographs and assisted by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared. RESULTS Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance was higher than that before the assistance (46.5%, P = 0.0081). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < 0.0001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = 0.1867) and the number of differential diagnoses was not also significantly increased (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = 0.5653). CONCLUSIONS In real-world settings, artificial intelligence augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.

Background The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings. Methods and findings To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval [CI] 0.852–0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9–65.1) and 90.0% (95% CI 89.4–90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7–47.3) and 94.8% (CI 94.4–95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7–76.0) and 87.4% (95% CI 82.5–92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7–75.9) and 85.7% (95% CI 82.4–88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population. Conclusions Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information., Seung Seog Han and colleagues compare the performance of a neural network algorithm with that of dermatologists in diagnosis of skin neoplasms., Author summary Why was this study done? The diagnostic performance of artificial intelligence based on deep learning algorithms has been demonstrated to be superior to or at least comparable with that of dermatologists. However, the difference in diagnostic efficiency between algorithms and dermatologists was determined using experimental reader tests with limited clinical information related to the photographed skin abnormalities. Most studies performed internal validation, which indicates that both the training and validation images were selected from the same source. In addition, only a small number of disorders have been validated in the previous studies. Thus, practical limitations and biases have complicated translation to actual practices. What did the researchers do and find? The performance of the neural network algorithm was compared with that of standard dermatologic practice for diagnosing almost all types of skin neoplasms on a large scale. The algorithm could successfully screen malignancy, without lesion preselection by a dermatologist. Under experimental settings, in which only images were provided for diagnosis, the performance of the algorithm was comparable with that of the 44 dermatologists who performed the reader test. However, the performance of the algorithm was inferior to that of the attending physicians who actually consulted with patients. This highlights the value of clinical data, in addition to visual findings, for accurate diagnosis of cutaneous neoplasms. What do these findings mean? Given photographs of abnormal skin findings, the algorithm can work ceaselessly to determine the need for dermatologic consultation at a performance level comparable with that of dermatologists. To further improve the algorithm’s performance, metadata, such as past medical history, should be integrated with the clinical images.

How stromal cells fill voids in wounded tissue remains one of the most fundamental questions in regenerative medicine. Fibroblasts are known to fill voids by depositing extracellular matrix (ECM) proteins while migrating towards the wound site; however, their ability to adopt an epithelial-like purse-string behaviour remains unexplored. Here, we fabricated an artificial wound with a deep void space to investigate fibroblasts’ behaviour during gap closure. We found that fibroblasts can form a free-standing bridge on deep microvoids and consequently close the void through the purse-string contraction, which was previously believed to be exclusively an epithelial wound closure mechanism. The results also revealed that the fibroblast gap closure in our fabricated 3D artificial wound depends on myosin II-mediated contractility and intercellular adherent junctions. Our study reveals that stromal cells can gain the structural features of epithelial cells, namely, intercellular contractile rings, to fulfil their functions under the specific microenvironmental conditions of tissue repair. Furthermore, fibroblasts can close artificial wounds with gap widths up to 300 μm, approximately twice as large as the critical epithelial gap closure size on non-adherent substrates. Fibroblasts exhibited a zip-up gap closure mechanism with a geometrical size effect. These findings reveal a new mechanism for gap closure by stromal cells during wound healing and pave a way to groundbreaking therapeutic strategies for tissue repair.

Abnormal histone modification by histone deacetylases (HDACs), including HDAC1 and sirtuin 1 (SIRT1), has been reported to play an important role in the pathogenesis of psoriasis by altering cell proliferation, differentiation, and inflammation. However, findings on the expression level of HDACs in psoriatic skin lack consistency. We assessed the expression of HDAC1, SIRT1, p63, and proliferating cell nuclear antigen (PCNA) in skin tissues from 23 patients with psoriasis (15 with plaque psoriasis and eight with guttate psoriasis) and five healthy individuals using immunohistochemistry, and analyzed their associations with clinical phenotypes of the disease. The expression of HDAC1 and keratinocyte proliferative markers, such as p63 and PCNA significantly increased, whereas that of SIRT1 decreased in the basal layer (P &lt, 0.05) of the patients with psoriasis compared to those in healthy controls. Among the patients with psoriasis, expression of HDAC1, p63, and PCNA was significantly higher in plaque psoriasis than in guttate psoriasis. There was no significant differences in the level of SIRT1 between the two clinical phenotypes. The findings of this study suggest that histone modifications are involved in the pathogenesis of psoriasis and may contribute to the formation of clinical phenotypes.

Particulate matter (PM), a major air pollutant, is a complex mixture of solid and liquid particles of various sizes. PM has been demonstrated to cause intracellular inflammation in human keratinocytes, and is associated with various skin disorders, including atopic dermatitis, eczema, and skin aging. Resveratrol is a natural polyphenol with strong antioxidant properties, and its beneficial effects against skin changes due to PM remain elusive. Therefore, in the present study, we investigated the effect of resveratrol on PM-induced skin inflammation and attempted to deduce the molecular mechanisms underlying resveratrol&rsquo, s effects. We found that resveratrol inhibited PM-induced aryl hydrocarbon receptor activation and reactive oxygen species formation in keratinocytes. It also suppressed the subsequent cellular inflammatory response by inhibiting mitogen-activated protein kinase activation. Consequentially, resveratrol reduced PM-induced cyclooxygenase-2/prostaglandin E2 and proinflammatory cytokine expression, including that of matrix metalloproteinase (MMP)-1, MMP-9, and interleukin-8, all of which are known to be central mediators of various inflammatory conditions and aging. In conclusion, resveratrol inhibits the PM-induced inflammatory response in human keratinocytes, and we suggest that resveratrol may have potential for preventing air pollution-related skin problems.

Background Kaempferol (3,4',5,7-tetrahydroxyflavone) is a flavonoid known to have a wide range of pharmacological activities. The 3-OH group in flavonoids has been reported to determine antioxidant activities. Objective We tested whether kaempferol can affect the expression of integrins and the stem cell fate of interfollicular epidermal stem cells. Methods Skin equivalent (SE) models were constructed, and the expression levels of stem cell markers and basement membrane-related antigens were tested. The immunohistochemical staining patterns of integrins, p63, and proliferating cell nuclear antigen (PCNA) were compared between kaempferol- and apigenin-treated SE models. Reverse transcription-polymerase chain reaction (RT-PCR) was used to evaluate the mRNA expression of integrins. Results Kaempferol increased the thickness of the epidermis when added to prepare SEs. In addition, the basal cells of kaempferol- treated SEs appeared more columnar. In the immunohistological study, the expression of integrins α6 and β1 and the numbers of p63- and PCNA-positive cells were markedly higher in the kaempferol-treated model. However, apigenin showed no effects on the formation of three-dimensional skin models. RT-PCR analysis also confirmed that kaempferol increased the expression of integrin α6 and integrin β1. Conclusion Our findings indicated that kaempferol can increase the proliferative potential of basal epidermal cells by modulating the basement membrane. In other words, kaempferol can affect the fate of interfollicular epidermal stem cells by increasing the expression of both integrins α6 and β1. These effects, in particular, might be ascribed to the 3-OH group of kaempferol.

Background: Asthma and allergic diseases account for school absenteeism and may limit academic and social opportunities for a substantial number of students, because they are common chronic disorde...

BACKGROUNDThe aim of this study was to validate the performance of algorithm (http://rcnn.modelderm.com) for the diagnosis of benign and malignant skin tumors.METHODSWith external validation dataset (43 disorders; 40,331 clinical images from 10,426 patients; January 1, 2008 – March 31, 2019), we compared the prediction of algorithm with the clinical diagnosis of 65 attending physicians at the time of biopsy request.RESULTSFor binary-task classification of determining malignancy, the AUC of the algorithm was 0.863(95% CI 0.852-0.875) with unprocessed clinical photographs. The sensitivity/specificity of the algorithm at the predefined high-sensitivity and high-specificity threshold were 79.1%(76.9-81.4)/76.9%(76.1-77.8) and 62.7%(59.9- 65.5)/90.0%(89.4-90.6), respectively. The sensitivity/specificity calculated by the clinical diagnosis of attending physicians were 88.1%/83.8%(Top-3) and 70.2%/95.6%(Top-1), which were superior to those of algorithm.For multi-task classification, the mean Top-1,2,3 accuracies of the algorithm were 42.6±20.7%, 56.1±22.8%, 61.9±22.9%, and those of clinical diagnosis were 65.4±17.7%, 73.9±16.6%, 74.7±16.6%, respectively.In the reader test with images from 30-patients batches, the sensitivity / specificity of the algorithm at the predefined threshold were 66.9%±30.2% / 87.4%±16.5%. The sensitivity / specificity derived from the first diagnosis of 44 the participants were 65.8%±33.3% / 85.7%±11.0%, which were comparable with those of the algorithm (Wilcoxon signed-rank test; P=0.61 / 0.097).CONCLUSIONSOur algorithm could diagnose skin tumors at dermatologist-level when diagnosis was made solely with photographs, demonstrating its potential as a mass screening tool in telemedicine setting. However, due to limited data relevancy, the performance was inferior to that of actual medical examination. Clinical information should be integrated with imaging information to achieve more accurate predictions.

Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant.Region-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1 106 886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean [SD] age, 58.2 [19.9] years; 308 men [45.8%]; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018.The area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants.The algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 [0.126], P .001) and Youden index score (0.675 vs 0.417 [0.124], P .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 [0.040]; Youden index score, 0.675 vs 0.671 [0.100]).The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.

Several terms have been used to characterize skin types. However, these are not based on evident dermatologic definitions, which usually include subjective and psychological properties.The objective of this study is to establish a new practical questionnaire to classify skin subtypes in Korean women to establish treatment and skin care guidance.Eight experts developed consensus statements about skin types and created a questionnaire. The content was categorized into four major subtypes: dry or non-dry; oily or nonoily; sensitive or nonsensitive; and pigmented or nonpigmented. A total of 512 patients completed the questionnaire. Correlations with age, skin Fitzpatrick's phototypes, and dermatologic comorbidities were analyzed.Korean women commonly have oily skin but complain of dryness after cleansing. They are especially sensitive to skin care products and prone to developing pigmentary lesions. There was a trend in the proportion of dry skin subtype that increased as pigmented skin increased and oily skin decreased with advanced age. The proportion of sensitive skin was higher in patients with dermatologic comorbidities. The proportion of the pigmented skin was higher in darker Fitzpatrick skin phototypes.This is the first questionnaire established for Korean women to classify practical skin subtypes and may provide a basis for treating various skin conditions.